Methods for Integrated analysis of Multiple Omics datasets

Interpreting data to promote health

The MIMOmics series of ebooks

Vol. 1 – the geneXplain platform

MIMOmics is a FP7-HEALTH-2012 Innovation project funded by the European Commission.

Contents

1.      Introduction

2.      Organization of the geneXplain platform

2.1.      The user interface

2.1.1.      Work Space

2.1.2.      Tree Area

2.1.2.1.      Databases

2.1.2.2.      Data

2.1.2.3.      Analyses

2.1.3.      The Info Box

2.1.3.1.      Info tab

2.1.3.2.      Search

2.1.4.      The Operations Field

2.2.      How to organize the user work space

2.2.1.      Changing user password and personal data

2.2.2.      User Project, Data folder, creating new folders

2.2.3.      How to check information about the available work space

2.2.4.      User toolbar

2.2.5.      Project properties, or how to fix the releases of the databases to be applied in user’s projects

2.3.      How to handle tables, tracks, diagrams: basics

2.3.1.      File handling

2.3.1.1.      File selection

2.3.1.2.      File handling in the Tree Area

2.3.2.      Tables

2.3.3.      Basic operations with tracks

2.3.4.      Diagram handling

3.      Load data

3.1.1.      Import a file from a local computer

3.1.2.      Uploading data from URL

3.1.3.      Paste plain DNA sequence

4.      RNA-seq

4.1.      RNA-seq preprocessing

4.1.1.      SRA to FASTQ

4.1.2.      Convert genome coordinates with Lift-Over

4.1.3.      Alignment of FASTQ with Bowtie

4.1.4.      Quantification of RNA-seq with Cufflinks for multiple BAM files

4.1.5.      Find gene fusions from RNA-seq

4.1.6.      Find genome variations and indels from RNA-seq

4.1.7.      Quantification of RNA-seq with Cufflinks (no de-novo assembly) for FASTQ files

4.1.8.      Quantification of RNA-seq with Cufflinks (with de-novo assembly) for FASTQ files

4.2.      Detect differentially expressed gene (DEG)

4.2.1.      Estimate differential expression using Linear Models for MicroArrays (LIMMA)

4.2.2.      Estimate differential expression by the gene expression mixture model of EBarrays

4.3.      Further workflows in this area

5.      Proteomics

5.1.      Analyze networks

5.1.1.      Find master regulators

5.1.1.1.      Find master regulators with TRANSPATH®

5.1.1.2.      Find master regulators with GeneWays

5.1.2.      Find common effectors

5.1.2.1.      Find common effectors with TRANSPATH®

5.1.2.2.      Find common effectors with GeneWays

5.1.3.      Identify functional protein cluster by shortest path analysis

5.2.      Further workflows in this area

6.      Epigenomics

6.1.      Analyze genomic intervals

6.1.1.      Identify and classify target genes near the intervals

6.1.2.      Site search with TRANSFAC

6.1.2.1.      Site search in a single interval list

6.1.2.2.      Site search in multiple interval sets

6.1.3.      Search for composite modules

6.1.3.1.      Search for composite modules with TRANSFAC®

6.1.4.      Search for discriminative sites with TRANSFAC® (MEALR)

6.2.      Further workflows in this area

7.      ChIP-seq

7.1.      Peak calling

7.1.1.      MACS

7.1.2.      SIZER

7.2.      Analyze ChIP-seq peaks

7.2.1.      Identify and classify target genes near the peaks

7.2.1.1.      Classification by GO categories and metabolic pathways

7.2.1.2.      Classification by GO categories and signaling pathways

7.2.1.3.      Classification by GO categories, signaling pathway, and diseases

7.2.2.      Site search with TRANSFAC®

7.2.2.1.      Version 2.0 (Adjusted p-values, site search on track)

7.2.2.2.      Version 1.2 (Classical)

7.2.3.      Search for composite modules with TRANSFAC®

7.2.4.      Search for discriminative sites with TRANSFAC® (MEALR)

7.2.5.      Search for enriched TF sites from tissue specific promoter tracks

7.2.6.      Discover de-novo motifs using ChIPHorde and DiChIPHorde

7.3.      Further workflows in this area

8.      Sequence analysis

8.1.      Analyze any DNA sequence

8.1.1.      Search for TF binding sites

8.1.1.1.      Search for TF binding sites with TRANSFAC®

8.1.1.2.      Search for TF binding sites with GTRD

8.1.2.      Analyze any DNA sequence for site enrichment

8.1.2.1.      Analyze any DNA sequence for site enrichment with TRANSFAC®

8.1.2.2.      Analyze any DNA sequence for site enrichment (GTRD)

8.2.      Further workflows in this area

9.      miRNA

9.1.      Prediction of miRNA binding sites

10.      Microarrays

10.1.      Normalize data

10.1.1.      Experiment vs. control or multiple conditions

10.1.2.      Heatmap

10.1.3.      Normalization quality plots

10.1.4.      Principal Component Analysis (PCA)

10.2.      Detect differentially expressed genes

10.2.1.      Detect differentially expressed genes with T-test

10.2.2.      Detect differentially expressed genes by hypergeometric analysis

10.2.3.      Detect differentially expressed genes with Limma

10.2.4.      Detect differentially expressed genes with EBarrays

10.3.      Discover functional enrichment

10.3.1.      Gene Set Enrichment Analysis (GSEA)

10.3.1.1.      GSEA by GO categories and metabolic pathways

10.3.1.2.      GSEA by GO categories, signaling pathways and diseases

10.3.1.3.      GSEA with a selected ontology

10.3.2.      Functional classification

10.3.2.1.      Mapping to GO categories and metabolic pathways

10.3.2.2.      Mapping to GO categories and signaling pathways

10.3.2.3.      Mapping to GO categories, signaling pathways and diseases

10.3.2.4.      Mapping with selected classification

10.3.2.5.      Cross-species mapping to ontologies, using ortholog information (PROTEOME™)

10.4.      Analyze regulatory regions

10.4.1.      Motif quality analysis

10.4.2.      Create matrix logo

10.4.3.      Identify enriched TF sites in promoters

10.4.3.1.      Version 2.0 (Adjusted p-values)

10.4.3.2.      Version 1.2 (Classical)

10.4.4.      Identify composite modules in promoters

10.4.4.1.      Version 2.0 (Adjusted p-values) with TRANSFAC®

10.4.4.2.      Version 1.2 (Classical) with TRANSFAC®

10.4.5.      Cross-species identification of enriched motifs in promoters using ortholog information (TRANSFAC®)

10.4.6.      Visualization of site search results

10.5.      Further workflows in this area

11.      Drug targets

11.1.      Find drug targets with TRANSFAC® and GeneWays

11.2.      Find drug targets with TRANSFAC® and TRANSPATH®

11.2.1.      Complete upstream analysis (TRANSFAC® and TRANSPATH®)

11.2.2.      Enriched upstream analysis

11.2.3.      Focused upstream analysis

11.2.4.      Upstream analysis with feedback loop

11.3.      Further workflows in this area

12.      Pathways

13.      NGS

14.      Genomic variants

14.1.      Find genome variants and indels from full-genome NGS

14.2.      Visualize variants in genome browser

14.2.1.      Human

14.2.2.      Mouse

14.2.3.      Rat

14.3.      Identify and classify genes with genomic variants

14.4.      Identify TFBS affected by genomic variations

14.4.1.      Enriched TF sites around regulatory SNPs and SIFT analysis

14.4.2.      Find enriched TF binding sites in variation sites

14.4.3.      Mutation effect on sites analysis

14.4.4.      SIFT (Sorting Tolerant From Intolerant) analysis

14.5.      Further workflows in this area

15.      Metabolism

15.1.      Analyze metabolic networks

15.1.1.      Find longest metabolic chain

15.1.2.      Find metabolic clusters by shortest path

15.1.3.      Find metabolic clusters by all path

15.1.4.      Flux Balance Analysis

15.2.      Further workflows in this area

16.      Popular functions

16.1.      Operations with tables

16.1.1.      Annotate table

16.1.2.      Convert identifiers for a gene table

16.1.3.      Join tables

16.1.4.      Intersect tables

16.1.5.      Venn diagram

16.2.      Operations with tracks

16.2.1.      Annotate track with genes

16.2.2.      Intersect tracks

16.2.3.      Gene set to track

16.2.4.      Track to gene set

16.2.5.      Process track with sites

16.2.6.      Create random track

16.2.7.      Create transcript region track

16.2.8.      Create tissue-specific promoter track

16.2.9.      Track statistics

16.2.10.      Mutation effect analysis

16.2.11.      Remove overlapping sites

16.3.      Statistical methods

16.3.1.      Compare analysis results

17.      Gene or protein lists

18.      Complete list of workflows

19.      Working with the different databases

19.1.      Biomodels

19.2.      Biopath

19.3.      Ensembl

19.4.      Gene Ontology (GO)

19.5.      GeneWays

19.6.      Reactome

19.7.      TRANSPATH®

19.8.      GTRD

19.9.      TRANSFAC®

19.10.      Fantom5 cell-type and tissue-specific transcription start sites

19.11.      Other data sets

19.11.1.      DrugExpress – genome-wide transcriptional signatures of drug response

20.      Description of analysis methods

20.1.      Sequence analysis methods

20.1.1.      SNP matching

20.1.2.      Site search on gene set

20.1.3.      Creation of customized profiles

20.1.3.1.      Create profile from gene table

20.1.3.2.      Create profile from site model table

20.1.3.3.      Create profile from matrix library

20.1.4.      Search for enriched TFBSs

20.1.4.1.      Search for enriched TFBSs (genes)

20.1.4.2.      Search for enriched TFBSs (tracks)

20.1.5.      Construct composite modules

20.1.5.1.      Construct composite modules

20.1.5.2.      Construct composite modules on tracks

20.1.5.3.      Hierarchical structure of the composite modules

20.1.5.4.      Visualization and interpretation of the results

20.1.5.5.      Score calculation of the composite models

20.2.      About the GSEA analysis and the interpretation of the results

21.      Operations field

21.1.      Changing the table structure in the Operations Field

21.2.      Changing the layout

21.3.      Expression Mapping

21.4.      Graph Search

22.      Editing and creating workflows

22.1.      Edit a pre-existing workflow

22.2.      Create a new workflow

22.2.1.      Six steps to create a simple workflow

22.2.2.      Complex workflows

22.2.3.      Cycles and scripts

23.      Editing and creating diagrams

23.1.      Diagram types

23.2.      Creation of a new diagram

23.3.      Editing a pre-composed diagram

24.      Editing and creating JS scripts

24.1.      Creating new JS scripts

24.2.      Executing JS scripts

24.3.      Editing JS scripts in workflows

24.4.      Inserting JS scripts in workflows

25.      List of icons and their meaning

25.1.      General (Control Panel)

25.2.      Tree Area Panel

25.2.1.      General

25.2.2.      Database types

25.2.3.      Data types

25.2.4.      Types of analyses

25.3.      Info Box

25.4.      Operations Field

25.5.      Work Space

25.5.1.      Diagram editing

Introduction

The geneXplain platform is an online workbench to assist in operating the daily computer applications in life sciences. It comprises a number of bioinformatics and systems biological modules, or BRICKS, which are unified under a standardized interface, with a consistent look-and-feel. These Bricks can be put together to comprehensive workflows using a workflow management system, which is intuitively handled through a simple drag-and-drop system. With this systems, the user can edit the predefined as well as compose own workflows. Own Bricks can easily be added as JavaScript or R scripts and incorporated into workflows as well.

The whole system aims at covering, with time, all areas of computational applications. The community is invited to contribute Bricks, either as public-domain or as a commercial part of the platform. We are confident that this way, an extremely powerful system will grow in which a user-driven selection will ensure the best tools being the most successful ones.

Besides providing a wide range of sophisticated Bricks (more than 60), the geneXplain platform also facilitates standard analyses through a number (61) of pre-composed workflows presently subsumed under 8 categories.

The technology behind the geneXplain platform is BioUML, which has been developed for many years at the Institute of Systems Biology in Novosibirsk. The architecture is open, so that own scripts can be easily loaded into the system, and new Bricks can be programmed and added by each skilled person.

In the following, we will guide you first through the pre-defined workflows, before we are going to introduce you to the different parts of the system in a more systematic way.

Have fun with the system, and don’t hesitate to come back to us with any suggestion for improvements (info@genexplain.com), we know that there is still ample space to make the system better.

Organization of the geneXplain platform

2.1. The user interface

When you open the geneXplain platform for the first time, a window opens that contains the following areas:

A -      The Work Space, which is the main part of the window.

B -      The Tree Area (to the left of the Work Space), where you find the collection of Databases, the uploaded Data files and the available Analyses methods under the corresponding tabs, organized in a hierarchical tree structure.

C -      The Info Box (in the left lower part), where you can select the data resource to Search in, or where you will get Information about the data file or analysis method that you select with a single click in the Tree Area.

D -      The Operations Field (right lower part), providing a number of options under the different tabs in a context-dependent manner.

E -      The general Control Panel, on top of the different areas, showing a context-dependent set of icons for the available operations.

2.2.1. Work Space

When you open the platform, the Work Space will show you all those research areas that are supported by a number of bioinformatic workflows each. Clicking on one of these tiled buttons will open a detailed listing of functions and pre-composed workflows, which you can launch by directly activating the given hyperlinks.

The listings of each Area introduce each Chapter of this User Guide that explains the functionality provided:

Area

Chapter

Area

Chapter

RNA-seq

4

Pathways

12

Proteomics

5

NGS

13

Epigenomics

6

Genomic variants

14

ChIP-seq

7

Metabolism

15

Sequence analysis

8

Popular functions

16

miRNA

9

Gene or protein list

17

Microarrays

10

Complete list of workflows

18

Drug targets

11

Working with different databases

19

The first workflow in nearly any Area is Load data, which is separately described in Chapter 3, before the individual Areas are being depicted.

Also in the Work Space, all major input requirements will have to be done, the progress of a workflow and the results output will appear here as well.

2.1.2. Tree Area

In the Tree Area (B in the figure of Section 2.1), you find three tabs: Databases, Data, and Analyses. Under each of these tabs, you find a tree-structured listing of contents: the available databases, the data files to work with, and the analysis methods you can apply. You can open any subdirectory by a single mouse click on the respective item and using the button , or by a double-click on the name of the folder to be opened.

2.1.2.1. Databases

Under this tab, you find all databases that are at your disposal: a number of public databases (yellow symbols) as well as commercial ones that you have a valid license for (green symbols). Write access may be additionally indicated by a black “W” within the symbol, which may be red in case that write access, although principally possible for this database, has not been activated in your case.

Select one of the databases in the Tree Area by a single click with the left mouse button; double click will open the subdirectory. For instance, when you wish to go to GeneWays, you have to click once on the little white triangle next to the term GeneWays, or double-click on the term itself to open the directory underneath; either operation, or already a single click onto the name of the database, suffice to indicate this data resource in the Info Box, along with a short description.

Presently, by default, you should see the following listing of available databases:

How you can work with the different databases will be explained in Chapter 19.

2.1.2.2. Data

On the tab Data, you will find your own datasets as well as predefined ones in the directories Examples, Projects, and Public.

In the directory Examples, we provide you with a number of sample data you may use for getting familiar with the system. Presently, these are some optimization examples as well as one application example on expression data of Psoriasis patient samples.

The optimization example refers to the CD95L pathway (Reactome entry REACT_900), shown as diagram CD95 signaling pathway under Diagrams. Under Plots, you find an example for a successful parameter optimization (C3C8_plot), with the dots giving the experimental values for four different molecules, and the straight lines showing the simulation results.

Under the heading Projects, you should initially find only one directory with your username, with the two subfolders Data and Journal. Data will be the place where all your own data will be deposited. Here, you can define own subdirectories by opening a context menu with the right mouse button and selecting the option New folder:

Each option in this menu can also be selected by the corresponding icon on top of the tabs.

The sub-directory Journal stores a history of your activities. Opening it in the Work Space shows you a list of all methods that you have previously launched, including time stamp.

The root directory Public is presently not used yet; it is planned to provide a platform for sharing own results with a wider community at a later stage.

2.1.2.3. Analyses

On this tab, you find all methods you may apply using the geneXplain platform, including the pre-defined workflows:

In the directory JavaScript, a collection of scripts have been gathered that all can be called by your own scripts. The geneXplain platform provides you with the possibility to create your own scripts on the Script tab in the Operations Field. For instance, enter there the command “help (boxPlot);” (boxPlot is the first JavaScript in the subdirectory Functions) and press Execute (or just Enter): Some help text about boxPlot will be printed to the Work Space:

Under Functions, a number of standard JavaScript functions are listed, whereas in the subdirectory Host object, a collection of scripts have been put together that were specifically developed for the gXp platform.

The directory Methods contains all individual tools, or Bricks, that perform specific tasks in one of the listed areas. A full listing of all analysis methods that are presently available is given in the Help texts. Each method can be launched either by double-clicking on the respective item, which will open the corresponding input mask in the Work Space, or through the context menu that you can open with a right-button mouse click on the method of interest.

In the directory Workflows, you find basically the same functional groups of pre-defined workflows that are also listed on the Start page of the platform (Work Space). Please note that the first two items on the Start page are important preparatory tasks, but not workflows according to the definition of the geneXplain platform. Please, refer to the following Chapters for operating the pre-defined workflows and to Chapter 22 for creating your own workflows.

2.1.3. The Info Box

The Info Box shows two tabs, Info and Search.

2.1.3.1. Info tab

Under the Info tab, you will find short descriptions about the database, data file or analysis method that you have clicked in the Tree Area. In many cases, you will just see the name of the directory or file as ID, or the complete path displayed in the field Complete name, and the number of subdirectories and files right under the activated node as Size.

The information shown on the Info tab can be viewed in a separate window by clicking the button .

In case the activated file is a table (icon or derived images; see 25.2.3), the number of rows in the table will be indicated as Size. Optionally, a Description is shown as well, depending on whether it has been entered when the file was generated. By clicking on the icon , you may edit some of the information in a newly opened window. Depending on the file type, the edit window may look like this:

Gray fields cannot be edited, contents of fields with white background can be changed manually, additional information can be entered in the pink fields which will then appear on the Info tab, if the changes made are saved afterwards with the [Save] button of the Edit window.

Depending on the type of directory or file that you have activated in the Tree Area, different types of information can be selected for the display on the Info tab. This can be done by the selection box right to the tabs in the Info Box, initially always showing “Default”:

Please, feel encouraged to find out the effects of the different views offered here.

When you have a network diagram opened in the Work Space (A in the figure of chapter 2) and you select a molecule or reaction with one click, you find information about the selected element in the Info box.

2.1.3.2. Search

As long as you are under the tabs Data or Analyses in the Tree Area, you see here only the message “Select database to search in …, maybe along with fields and data from an earlier search.

This field gets activated as soon as you go to the tab Databases in the Tree Area, and then click on one of the databases listed there. For instance, when you wish to go to GeneWays, you have to click once on the little white triangle next to the term GeneWays, or double-click on the term itself to open the directory underneath; either operation, or already a single click onto the name of the database, suffices to indicate this data resource in the Info Box:

You can insert your search term (e.g., a gene symbol) into the field underneath. Clicking on the icon launches the search. The search routine scans for exact matches, but use of wildcards is possible. Thus, searching for elk* returns results for elk1, elk2p1, elk3, and elk4.

The results will be shown in the Operations Field, under the tab Search result. For instance, when searching in GeneWays for JAG1, the following result table will be displayed:

The search term is highlighted in bold.

In some cases, the search results can be narrowed down by further specifying the search space in the Info Box.

More about how to operate the individual databases will be explained in Chapter 19.

2.1.4. The Operations Field

In the Operations Field (D in the figure of the Section introduction 2.1) a number of essential functions to operate the geneXplain platform are provided on a number of tabs. How many and which tabs are shown depends very much on the context.

Please note that not all tabs are always visible due to space constraints. In these cases, double arrowheads left and right of the tabs indicate that there are additional ones, reachable by clicking on these double arrowheads.

The function of the individual tabs will be explained in more detail in those sections where their effect is part of a certain operation. In general, the icon initiates the corresponding activity within the Operations Field, whereas applies to the results generated in the Operations Field of the Work Space.

The full range of functions that you can make use of in the Operations Field is explained in greater detail in Chapter 21.

2.2. How to organize the user work space

2.2.1. Changing user password and personal data

Your first password for the geneXplain platform is automatically generated and sent to you by e-mail. Once you enter the platform you can change your password and also have an option to edit your personal information.

To change password and edit the personal data, select the Account info button () on the top menu control panel:

Your account information will be displayed in the work space as shown below:

The option to change the password is highlighted by the red oval. Once you press [Change], you get an option to enter the new password:

To change the account details, press the button [Edit account info] in the form above, highlighted by the green oval. The edit form looks like this:

Fill-in your details and press [Save]. Before saving, the system verifies your password to enable the changes made in the form. After entering the password the changes are saved.

2.2.2. User Project, Data folder, creating new folders

When you enter your account for the first time, you can see the following three folders in the Tree Area under the Data tab (red oval on the picture below): Examples, Projects and Public. Each of these folders can be expanded by clicking on the small triangle on the left side of the folder name (green oval on the picture below).

The folder Projects is your folder in the tree where you are going to make all the analyses.

If you expand the Project folder, you can see the project that was created upon registration of your account (), and if you expand it further, you can see the folder Data. This location, data/Projects/User project/Data/ is exactly the location where you can upload your data, and save all the analysis results.

Upon one mouse click on the folder Data, as in the picture above, you can apply the button from the top control menu to create a new folder within the selected one.

In this way, you can define the hierarchical organization of your folders and sub-folders within your project, for example as shown below.

Every time when you run a workflow, you need to specify a location of the results folder, and you can specify any particular location within your project area.

There are two other folders available for you initially, Examples and Public. The folder Examples contains pre-analyzed publicly available data sets, which you are welcome to have a look through our examples. You can copy tables or tracks from these two folders into your project area and use them, for example, for test runs.

The Public folder contains publicly available data sets which might be useful to apply for various analysis purposes.

All users have read access to the folders Examples and Public, but no write access. You cannot save any files directly in the Examples or Public folder. However, you can copy tables and tracks from these two folders into your project area and then modify and work with them as you like.

2.2.3. How to check information about the available work space

To check your totally available work space, go with mouse over to the project name, so that it is highlighted in blue as shown below. In the Info Box you can see information about this project including disk quota. This is the space available for you. If you plan to upload large files, please make sure you have enough work space available.

To check how much space out of your quota is already occupied, go with mouse over to the Data folder within your project, so that it is highlighted in blue as shown below. In the Info Box you can see information about this folder including its size on the disk.

In the same way, you can check the size for every individual folder in your project.

Note. If additional work space or storage space is required, especially if you plan to upload and analyze large data files, please feel free to ask for details (info@genexplain.com).

2.2.4. User toolbar

In the Control Panel (see E in the figure of Section 2.1), the set of icons on the left side is fixed by default, whereas the right side is customizable. Here, you can create a user-specific toolbar with your most frequently used analysis methods, workflows and datasets. To create your own toolbar you can drag and drop your favorite workflows and files for which you often need a quick access into the Control Panel. The User tool bar will then be located at the top right top corner, highlighted by a red oval in the screenshot below.

To add any analysis method/workflow/gene set, open the respective method in the Tree Area or in the Work Space and drag & drop it onto the user toolbar as shown below.

You can quickly open your favorite items from this icon menu. To open any method, place the cursor on the symbol and you get the complete name of the method which can be clicked to open in the workspace as shown below:

In the above screenshot, the method ‘Site search on gene set’ has been opened through the user toolbar. If you want to remove any method from the user toolbar, right click the symbol and you will get an option ‘REMOVE’.

2.2.5. Project properties, or how to fix the releases of the databases to be applied in user’s projects

The geneXplain platform provides access to several versions of the databases installed.

You can select your desired version of the database and fix it for your project using the Project properties feature. By default the latest version of all databases are applied. Project Properties form can be opened via the button in the control panel:

The form opens as shown below:

For each database shown in the form, the available versions can be selected using the drop down menu. As shown below, TRANSPATH® 2013.3 database version is selected. After selection press [Save] and the selected version of the database will be used in this project for all the analyses.

Important to note, this change is applied for one selected project, the project name is shown on the top of the form, highlighted by the red oval on the picture above. If you have several projects, you can fix database versions for each project individually.

2.3. How to handle tables, tracks, diagrams: basics

2.3.1. File handling

2.3.1.1. File selection

All analysis tools, and likewise all prepared workflows, require input data from a file in the Tree Area. The respective file can be loaded into an analysis tool by

simple dragging-and-dropping of the file from the Tree Area into the corresponding field in the Work Space, or

by clicking into this field and making the selection from the directory which opens.

Multiple selections can be done only in the second way.

2.3.1.2. File handling in the Tree Area

On mouse-over, the file name will gain a faint-bluish background. Upon a single click on a file name, its background will turn into a persistent light blue, and information about this file will be displayed in the Info Box. Double-clicking on a file will open it under a new tab in the Work Space, and the file name will be additionally emphasized by bold lettering as long as the corresponding tab is in the Work Space’s foreground. Only files with an icon attached can be opened this way.

Files can also be opened by right-clicking on them and selecting the “Open table” option. You can also delete files (“Remove”; default value is “No”) this way. Both functions are also available through the corresponding icons ( and , resp.) in the Control Panel on top of the different frames.

While opening and deleting files works for nearly any file, the third option is different among distinct file types:

- Tables ( and derivatives) can be exported. When activating this function, a selection of different formats is provided for the file to be generated. It is also available through the icon on top of the Tree Area.

- Diagrams () can be expanded/collapsed to show/hide their components (nodes and edges); this function can also be accessed by clicking on the white arrowhead next to the diagram icon.

Plots () can be edited. Only under this option, their complete deletion is possible as well.

2.3.2. Tables

Any table may be opened by double-clicking the corresponding name in the Tree Area. It will open under a new tab in the Work Space.

The contents of the table are sorted according to the values in one of its columns. Being opened for the first time, a default column is defined for sorting, usually the ID column. This default column is indicated by a blue arrowhead. If this arrowhead points upwards, the table rows are sorted in ascending order of this column’s values. Clicking on this arrowhead will change it into a downwards pointing one, while the values are sorted in descending order. Correspondingly, you may sort the table according to the values of any other column in ascending or descending order by clicking on the up- or downwards pointing gray arrowhead on top of this column, respectively.

On top of the table, you can navigate between the individual pages of the table; it is also shown on which page out of how many pages in total you are, and in the right top corner, the page size in terms of number of entries (rows) is shown and can be adjusted.

You can edit the contents of a table by pressing the [Edit] button in the right upper corner. Now, you can manually edit the contents of each cell in the table. With the [Apply] option, you will save this change, while [Cancel] quits it.

Even without activating the Edit function, you can select

- individual rows with a left-mouse click,

- several ones by keeping the Ctrl key pressed,

- a range of rows with the Shift key pressed when clicking on the last row of the range to be selected, or

- [Select all] by clicking on the corresponding button.

The selected rows can be saved as a separate file, which by default is given the name <original file name> subset, but you can change this name.

2.3.3. Basic operations with tracks

View track in genome browser

Upon double-clicking on a track name in the tree area the track will be opened in the work space in the genome browser.

In the pop-up window Add tracks to genome browser you can select which tracks, among those available in Ensembl, should be opened together with your track. Here, three tracks are selected, GC-content, Genes, and Variations. When the selection is ready, push the [Ok] to get the following view with your track on top:

On the tab name you can see genome, species and build information for this track, highlighted by the red oval.

The small triangles on the right side of the track name can be used to jump to the next or previous site of this track:

Use the buttons in the top control menu to zoom in and out.

The buttons help to shift the visible part to the left or to the right.

The same effect can be also achieved by dragging the picture with the mouse.

You can also jump between different chromosomes by selecting the chromosome number in the field Sequence (chromosome).

As the next step to enrich the visualization, and to gather more information about your track of interest, you can add the additional pre-existing tracks from the folder Public. There, you can find a sub-folder traXplain with several tracks. This sub-folder is highlighted in blue in the screenshot below. As an example, two tracks have been added, the track with DNAse hypersensitive sites from the ENCODE project (DNAse HS sites clustered ENCODE UCSC hg19), and the track with the experimentally proven transcription factor binding sites from the TRANSFAC® database (TRANSFAC 2013.4 human sites hg19).

Open track as a table

To open a track as a table, use a right mouse click on the track name in the tree area or [Ctrl + mouse click] for Mac users.

Using the same menu, you can apply other functions to the selected track, e.g. export it in available formats or delete it.

A tabular view for the same track is shown below. Each row corresponds to one fragment. For each fragment, you can see the column ID with the fragment number, Chromosome, positions in the respective columns From and To, and several additional columns, three in this case.

Filter track by condition.

When any track is opened as a table, it can be filtered by any condition in the specified columns. Filter options are available under the tab Filters in the operations field.

Here, the filter is applied to select the fragments located on chromosome 1.

2.3.4. Diagram handling

Diagrams are provided by a number of databases and tools in the platform. The general schema of their use is that an overview of the corresponding graph is shown in the Operation Field, under the tab Overview, while a full-sized picture is shown in the Work Space, where usually only a part of the whole diagram fits on the screen. The diagrams exhibit components as ovals, reactions as squares, and links as lines or arrows.

The section displayed can be shifted either in the Work Space by moving the mouse pointer (hand symbol) keeping the left mouse button pressed, or by shifting the blue-dotted rectangle in the Operation Field (mouse over: pointer symbol turns into a four-arrow plus sign, shifting can be performed while keeping the left mouse button pressed).

To facilitate orientation in large diagrams, individual edges are highlighted (turning from a thin, usually black arrow into a thick light-blue one).

Double-click on an individual node will show information about this component under the Info tab of the Info Box (this works for BioModels).

Diagrams can be zoomed in and zoomed out, by click on the buttons or , correspondingly.

They can be exported in several formats by clicking the button .

Diagrams can be zoomed in and zoomed out, by clicking on the buttons or , respectively.

Diagram nodes can be multi-selected via the [Ctrl] button (picture below). Selected nodes can be used for Alignments or Distribution editing within the diagram or can be saved as a subset.

Five alignment methods are available in the tool bar (see picture below) after opening a diagram in the workspace.

After pushing one of the buttons in the toolbar, the selected nodes in the diagram are aligned accordingly. The picture below shows an Up alignment of all nodes.

Two distribution methods horizontally and vertically are available in the tool bar after opening a diagram in the workspace. After pushing one of the buttons in the toolbar, the selected nodes in the diagram are positioned accordingly. The picture below shows a Vertically distribution of one node.

For edge editing you can use either the dialog box or a clickable connecting line to add new edges to a diagram.

Load data

The first step is to load your data into the system. This is facilitated by the first topic listed on the Start page. When you click on the term “Load data”, another window with the title “Import file” is opened asking you for the file to upload. By clicking into the topmost field (Target folder), you can select the place where you want to save the file that you are going to upload. This file you can select by several options: upload from your computer, import directly from an FTP address, choose an item from the tree in the geneXplain platform, or paste data, e.g. a DNA sequence. Use one of the buttons [Computer], [Web/FTP], [Repository] or [Raw] to start the import.

3.1.1. Import a file from a local computer

Let us assume you wish to detect differentially expressed genes in your microarray experiments, which is one of the following workflow groups on the Start page. In this case, you might be interested to upload an archive (ZIP, TAR, GZ,…) of several CEL files (when you worked with an Affymetrix platform). In case of importing raw microarray results, we recommend to create a single ZIP archive of all files that you plan to normalize together. Please note that uploading is a lengthy process, and it may take some time. However, if in the uploading bar you see no progress and just 0% for about 10-15 minutes, please cancel and start again, it might be that the server was overloaded at that moment. Wait till the progress bar is completed. The picture below shows an ongoing uploading.

The uploading step is similar for all file formats. After the uploading step has been completed, the next step is specific for the file format, and different forms are displayed for different formats. Here, let’s consider two examples, loading a ZIP archive with Affymetrix CEL files and loading an Excel table.

Loading a ZIP archive with Affymetrix CEL files

When the uploading step is complete, additional options will be displayed, as shown below. The format of the imported file is detected automatically and will be indicated in field “Format”, in the example below it is ZIP-archive. Verify the automatic detection and if necessary refine it manually using the drop-down menu. Next, verify four fields as follows.

In case that here exists already a folder with the same name as the imported file, it will be replaced by imported data if you activate the check box “Cleanup existing folder”. Checking the box “Preserve extension” means that individual CEL files will keep the extension ”.CEL” after importing.

Checking the box “Preserve archive structure” allows you to keep the archive structure (folders and subfolders) after importing. Next, choose the type of the files within the imported archive (if an archive is imported), here “Affymetrix CEL files” row is selected, indicated by the dark blue background in the picture below. Finally, press the [Import] button.

Each imported archive or individual file will appear in the Data tree of the Tree Area when import is complete.

Loading an Excel table

After the uploading step of an Excel table is completed, the following additional options will be displayed as shown below:

The format of the imported file is detected automatically and is indicated in the field “Format”. In the figure above it is the Tabular format, which includes the XLS format among others. Verify the automatically detected format and, if necessary, refine it manually using the drop-down menu. Next, verify the following fields.

“Name for table” – the name of the uploaded Excel table is shown automatically; you can modify it, and under this name the table will be shown in the tree after import is completed. “Sheet name” – with the help of the drop-down menu specify which page of the Excel file should be imported (for Excel files with multiple pages).

“Header row index” – the number of header rows in the Excel table is detected automatically, in this example there is one header row. Verify automatic detection and if necessary correct the number. “First data row index” – the number of the row where the data start is detected automatically, in this example the data start in the second row and in the field figure “2” is shown. Verify automatic detection and, if necessary, correct the number.

“Column for ID” – use the drop-down menu to indicate which column in the imported table contains unique identifiers. The column “ID” is suggested automatically. Verify automatic suggestion and refine it as necessary.

“Type of the table” – this field aims to specify what type of IDs are used as identifiers in the imported Excel table. Use the drop-down menu to specify. In this example IDs in the Excel table correspond to Entrez gene IDs, and correspondingly in the figure below “Genes: Entrez” is specified.

“Species” – use the drop down menu to specify the biological species that corresponds to the IDs.

After that, press the [Import] button, and you can find the imported table in the tree, under the location specified in the “Import file” form in the field “Target folder”.

A number of file formats can be defined for import. You find them listed when opening the “Format” selection list. If you can’t find the required format explicitly listed, select “Generic file”.

3.1.2. Uploading data from URL

This option allows you to upload data directly from the web, e.g. by indicating an FTP address, which is significantly faster in comparison with downloading to a local computer and then uploading into the geneXplain platform. If your data are located on a web server, this would be a preferable way of importing.

First, press [Web/FTP] button on the “Import file” form, and indicate the ftp address in the newly opened form as shown below:

3.1.3. Paste plain DNA sequence

If you would like to import a plain DNA sequence, not saved in any file before, first, choose the button [Raw] on the form “Import file”, and a new form will be opened as shown below:

Paste the DNA sequence in the window “Type file content here”, and in the field “Name” specify a name as it should appear in the tree after importing.

RNA-seq

When you click on the tiled button “RNA-seq” in the Work Space of the on the start page, the following listing will appear:

4.1. RNA-seq preprocessing

4.1.1. SRA to FASTQ

This workflow can be used to convert SRA data files (e. g. from NGS/RNA-seq experiments) into FASTQ files. The FASTQ format is widely used by a number of tools and the geneXplain platform is among them; on the other hand, NGS data are often collected in SRA format, thus the conversion of SRA format into FASTQ format is an important function. An example of public data stored in SRA format can be found here (http://www.ncbi.nlm.nih.gov/sra?term=SRP051443) and can be uploaded directly via FTP import into the geneXplain platform. The workflow “SRA to FASTQ” can be found on the Start page, under the NGS/RNA-seq button.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the folder with the SRA files in the field Input folder. You can drag it from your project within the tree area and drop it in the box beside the folder pictogram. Alternatively, you may click on the field (select element) and a new window will be opened, where you can select the input folder.

Example of an input folder:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC)%2C GSE32424%2C FASTQ files/Data/SRA%2files/

It contains 12 files in SRA format as shown below. Please note, this folder occupies 1.5 GB work space.

The output folder name and folder path is automatically created, but can be changed in a user-specific way.

Press [Run workflow] and wait till the workflow is completed.

Example of an output folder:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC)%2C GSE32424%2C FASTQ files/Data/Fastq%2files/

The output folder contains 12 files with the same names and now with the extension fastq. Please note, the size of the output folder is 16.6 GB.

Note. Working with NGS data in SRA and FASTQ formats requires substantial work space available in your user account. Feel free to contact us (info@genexplain.com) to upgrade your account with additional disk space.

4.1.2. Convert genome coordinates with Lift-Over

While working with NGS data, quite often it might quite often be required to convert positions from one genome assembly to another one, and The Lift-Over program is widely can be applied for this task. Within the geneXplain platform, you can find it on the Start page under the NGS button, as highlighted below.

This tool is based on the LiftOver utility and Chain track from the UC Santa Cruz Genome Browser.

It converts coordinates and annotations between assemblies and genomes. The input is a track with genomic positions according to a particular genome assembly, and the output is a track with positions according to another genome assembly.

To launch the workflow, follow these steps:

Step1. Open the input form from Start page. It looks as shown below:

Step 2. Specify the input track in the field Convert coordinates of. You can drag it from your project within the tree area and drop it in the box of the field.

The further steps of the workflow are demonstrated by means of the tables in one of the pre-prepared examples. You can find these tables in the Examples folder, under

http://platform.genexplain.com/bioumlweb/#de=data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC)%2C GSE32424%2C FASTQ files/Data/Lift_over/

Step 3. Specify the Mapping of the input track by selecting the desired genome conversion from the drop-down menu.

In the majority of cases not everything can be re-mapped to another assembly. The minimum ratio of bases that must be re-mapped is by default 0.95, you can change this by typing in this field.

Step 4. Allow multiple output regions: choose Yes or No.

Step 5. Define where the output tracks should be located in the tree. The method produces two output tracks, one containing all the mapped coordinates and the other containing the unmapped coordinates (if existing).

After filling out all input fields press [Run] and wait till the method is completed.

The output is a folder with two tracks as shown below:

4.1.3. Alignment of FASTQ with Bowtie

Bowtie is a short-read aligner designed to be ultrafast and memory-efficient. It was developed by Ben Langmead and Cole Trapnell (Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10:R25). The Bowtie method can be found in the Galaxy section of the platform (analyses/Galaxy/solexa_tools/bowtie_wrapper).

Input format: Bowtie accepts files in Sanger FASTQ format. Often, the sequence files are represented in SRA format (Sequence Read Archives). This format is used to deposit sequences at the Sequence Read Archives at NCBI, EBI, and DDBJ. Please consider, converting the SRA files into FASTQ files before starting Bowtie. You can use the conversion tool Convert Files in SRA format to FASTQ or Convert Files in SRA format to paired FASTQ in the Galaxy section of the platform (analyses/Galaxy/sra_toolkit/fastq-dump).

To launch the Bowtie tool, follow these steps:

Step1. Open the Bowtie input form from the Start page by clicking on Alignment of FASTQ with Bowtie option in the RNA-seq preprocessing subsection. It will open in the main Work Space and looks as shown below:

Step 2. Specify the reference genome.

The field Will you select a reference genome from your history or use a built-in index? defines the reference genome. If you keep the default “Use a built-in index” the program makes an alignment to the reference genome which is provided as part of the platform (the genome builds for human, mouse and rat are provided). Depending on the species used, please specify hg19 for human, mm9 for mouse and rn4 for rat in the next field Select a reference genome.

If you select the “Use one from the history” option in the 1st field, two new fields will appears in the form: Select the reference genome and Choose whether to use Default options for building indices or to Set your own, as shown in the screenshot below, highlighted by the red ovals. In this case you should provide a preloaded reference genome (preloaded in Fasta, EMBL or Genebank formats) and choose the way how to build sequence indices which will be used by the alignment algorithm of Bowtie.

Step 3. The field Is this library mate-paired? defines the type of the sequence library which was used in NGS sequencing. The default is Single-end, the alternative is Paired-end. You should know this details about your library of short reads.

Step 4. Specify the input file in the field FASTQ file. You can either drag-and-drop or select the file name from the Tree area. Here, as an example, we use data from a published RNA-seq experiment analyzing the human esophageal squamous cell carcinoma (ESCC), GSE32424. FASTQ files can be found in the following Examples folder:

data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC), GSE32424, FASTQ files

This example contains results of an RNA-seq Illumina NGS sequencing of twelve clinical samples from human esophageal squamous cell carcinoma (ESCC) (seven tumors and five non-tumors). The authors provided sequences as so called non-aligned BAM files. We loaded these BAM files directly from GEO as one archive using the ftp uploading function of the geneXplain platform. After that, we converted the non-aligned BAM files into FASTQ files using the tool: SAM to FASTQ from the NGS: Picard (beta) subsection of the Galaxy section of the platform (analyses/Galaxy/picard_beta/picard_SamToFastq).

Step 5. The field Bowtie settings to use is set by default to Commonly used. If you change it to the Full parameter list from the drop-down menu, a full list of parameters of the alignment algorithm is enabled for editing. The full description of all these parameters is given in the original paper of Langmead et al. mentioned above which describes the algorithm of Bowtie.

Step 6. Please keep the box Suppress the header in the output SAM file unchecked (as it is by default) to generate SAM/BAM output files suitable for further use by Cufflinks tool.

Step 7. Set the output file name (for the output BAM file) in the field Map with Bowtie for Illumina… and press the button [Run].

Tip It is recommended to save the output file into a separate folder containing all BAM output files from one particular experiment. This will allow you to run the next workflow for the quantification of all SAM/BAM files from this defined folder.

Results

The result of this method is one BAM file which is generated by the Bowtie program as the result of the alignment of the sequence reads from the input FASTQ file to the reference genome.

At the end of the Bowtie run, the platform requests to specify the genome build again in order to link the output BAM file to the respective genome files in the Ensembl database installed in the platform. When you get such a pop-up form, choose the sequence score from the drop-down menu and specify the genome ID.

This enables a visualization of the BAM file information in the genome browser. Generated BAM file is a track and has the () icon in the tree. As usual for all tracks, it with a double click opens in the Work Space. You can see the positions of each aligned read in the genome and upon zooming-in you get all detailed information about each read complete with sequence, length, and quality.

In the info box you can see information about the output BAM file. The number of aligned and not aligned reads and overall file size is shown.

Note. The input FASTQ and output BAM files of the Bowtie tool require a considerable amount of working space. One FASTQ file can occupy several GB of space. If you need more space for storage and work with your FASTQ and BAM files, please feel free to ask for details (info@genexplain.com).

4.1.4. Quantification of RNA-seq with Cufflinks for multiple BAM files

This workflow is designed to estimate abundances of transcripts in several RNA-Seq samples using the Cufflinks method (published in Trapnell C, Williams BA, Pertea G, Mortazavi AM, Kwan G, van Baren MJ, Salzberg SL, Wold B, Pachter L. Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms. Nature Biotechnology doi:10.1038/nbt.1621).

The Cufflinks method accepts aligned RNA-Seq reads (in "aligned" BAM files) and assembles the alignments into a set of transcripts using a reference annotation of transcripts and genes. Cufflinks then estimates the relative abundances of these transcripts and genes based on how many reads support each one.

In the first part of the workflow, the Cufflinks program is called from the Galaxy section of the geneXplain platform (analyses/Galaxy/ngs-rna-tools/cufflinks.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Input a folder containing BAM file(s) from the tree. You can either drag-and-drop or select the folder name from the Tree area. Here, as an example, we use data from a published RNA-seq experiment on Colon cancer retrieved from GEO, accession number GSE29155. The following folder from the Examples folder is used:

data/Examples/RNA-Seq analysis of human prostate cancer cell line, GSE29155, BAM files/Data/

The input folder in this example contains 11 BAM files of RNA-Seq reads obtained from 7 colon cancer samples and 4 normal samples using the Illumina NGS sequencer. The reads were aligned by the authors of the data to the human genome build hg18. We loaded these BAM files directly from GEO as once archive using the ftp uploading function of geneXplain platform.

Step 3. Choose an appropriate sequence source from the drop-down menu:

The drop-down menu presents in each line: Species, Ensembl build number, Genome build code (e.g. GRCh37_hg19) and the corresponding version of the Genecode derived gene annotation. Select an option which corresponds to the RNA-seq data analyzed and stored in the input BAM files.

The Genome build code refers to the genome build which was used for the alignment of RNA-Seq data when producing the BAM files. You can check the genome build information by clicking on the individual BAM file in the tree area and getting information in the Info box. For example:

This BAM file was produced by alignment of RNA-Seq data to the NCBI36_hg18 genome build (in the "Sequence collection" field you can see the "chromosomes NCBI36")

The choice of the Genecode version depends on the particular needs of the user. The higher version of Genecode corresponds to the most up-to-date gene annotation, whereas the earlier Genecode version may correspond to the gene annotation used in other types of data in the same study, and may therefore be chosen for consistency with other data. (e.g. genecode.v10 corresponds to the most recent annotation done in the ENCODE project)

Step 4. Specify the output folder names. The Result folder and CountsFolder are created temporarily for storing Cufflinks outputs and intermediate quantification outputs. The FPKMfolder defines the folder for the final output of the workflow. The abbreviation FPKM stays for Fragments Per Kilobase of transcript per Million mapped reads, and is a commonly accepted standard measure for this kind of data.

Step 5. Press [Run workflow] and wait till the workflow is completed.

Results.

The results folder consists of several tables of Ensembl type containing the results of quantification of every BAM file from the input folder:

By double-clicking on each table you can see the result of the quantification.

For each Ensembl gene out of 51,520 Genecode annotated genes the FPKM value was computed. The FPKM value corresponds to the expression value of this gene. For RNA-Seq data, the relative expression of a transcript is proportional to the number of cDNA fragments that originated from it.

Note. This workflow may take several hours to complete. You can start this workflow and even switch off your computer, e.g. overnight, while the computation will be running on the server. After several hours you can check the results. In case of any questions, please feel free to ask for details (info@genexplain.com).

4.1.5. Find gene fusions from RNA-seq

Recently, next-generation sequencing techniques at the transcriptome level (RNA-Seq) have been used to verify known and discover novel transcribed gene fusions. This workflow offers the ability to discover gene fusions from RNA-seq data (single-end (SE) or paired-end (PE) RNA-Seq read data) based on the fast FusionFinder program published in 2012 (Francis et al., PLoS ONE 7:e39987, 2012) . It accepts raw RNA-seq reads (fastq format) and produces a table with found gene fusions. The workflow can be found under the section “RNA-seq preprocessing”.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify the input file in FASTQ format in the field Input fastq.
It contains data from your RNA-seq study. To specify the input fastq file, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you select the input file. After having selected the file, press the [Ok] button.

Step 3. Specify the Ensembl version from the drop-down menu. By default, the most recent, human_65, is selected.

Step 4. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Step 5. Press the [Run workflow] button.

Wait until the workflow is completed.

The Output folder contains the two tables, Fusion summary and Fusion isoforms; for this example, let’s consider the results folder located under Examples (data/Examples/Detection of novel fusion transcripts from RNA-seq data, 76mer fastq reads/Data/BI.081030_SL-XBF_0001_FC30CB2AAXX.7.fq (Gene fusions from RNA-seq)/). It is highlighted in blue in the figure below:

The FusionFinder program analyses FASTQ read data (reads must be of at least 50 nucleotides long; see input example) to identify gene fusion candidates. This is achieved by performing an integrated analysis, which is illustrated in the original paper of Francis et al.

The first step is to align the full length reads against a normal coding reference transcriptome. After creation of pseudo paired-end reads (PE), these PE reads are aligned against the coding reference transcriptome. A further step is to analyze the results and filter false-positives. The last step consists of a block filtering and identification of fused exons and isoforms from candidate fusion transcripts.

The output table Fusion summary is a ranked list of fusion candidates based on their evidence strength (total number of sequence reads = total reads). The file provides the Ensembl and HUGO (Human Genome Organization) Gene Nomenclature Committee (HGNC) common name identifiers for G1 and G2 (G1_Ensembl_HGNC_ID and G2_Ensembl_HGNC_ID), the number of blocks on each gene (G1_blocks and G2_blocks), an indication of how many isoforms exist for each G1:G2 pair and the category of fusion indicated by the pair.

The output table Fusion isoforms gives the full details for each isoform of G1 and G2 and includes the genomic coordinates of the alignment blocks on G1 and G2, and their respective corresponding Ensembl exon IDs.

4.1.6. Find genome variations and indels from RNA-seq

The challenge of obtaining accurate variant calls from RNA-seq data is substantial. The workflow is based on a framework to discover genotype variations published by De Pristo et al., Nature Genetics 43:491-498, 2011. The process applied includes initial read mapping, local realignment around indels, base quality score recalibration, SNP discovery and genotyping to find all potential variants.

The workflow can be found in the section “RNA-seq Preprocessing”.

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify the input file in FASTQ format in the field Input fastq file.
To specify the fastq file, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you select the input track. After having selected the track, press the [Ok] button.

Step 3. Specify the Minimum read segment length. By default a minimum length as 25 is given.

Step 4. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field OutputFolder, and a new window will be opened, where you can select the location of the results folder and define its name.

Start the workflow by pressing the [Run workflow] button.

In the following example we took as input the fastq file SRR349741.fastq (data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC), GSE32424, FASTQ files/Data/Fastq files/SRR349741.fastq). Below you can see the result folder (data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC), GSE32424, FASTQ files/Data/SRR349741.fastq (Genome variants and indels from RNA-seq)) for the example. The output folder contains several files and sub-folders with all results of the analysis.

The first step of the workflow is an alignment of all reads of fastq file using the TopHat2 tool. In the result folder one can see a sub-folder “tmp” which contains all found Deletions, Insertions, Splice junctions and Alignments. They are stored as tracks and can be opened in the genome browser by double-click on each of the tracks. Each short line (arrow in the higher zoom) represents an aligned “read” from the fastq file.

After zooming into each individual aligned read the insertions and deletions in the respective tracks of the browser become visible.

The Tophat summary file shows the total numbers of input reads, mapped reads, reads with multiple alignments and the overall read alignment rate.

The initial alignments are sorted and reordered to prepare the next quality checking steps. The results of these two steps are stored in the folder tmp as the two files reorder.bam and sorted.bam.

The next step removes duplicates. The purpose is to mitigate the effects of PCR amplification bias introduced during library construction. Two read pairs are considered duplicate if they align to the same genomic position. The resulting MarkDuplikates1.log file is stored in the log folder and the MarkDuplikates1.stat file is stored in the stat folder.

The next step is a local realignment. Read mapping algorithms operate on each read independently, locally realign reads such that the number of mismatching bases is minimized across all the reads. Output files are Realigner.log and TargetCreator.log in the log folder, ddup1.bam, Realigned.bam and realigner.intervals in the tmp folder.

The realigned BAM file is used again to remove duplicates (output MarkDuplicates2.log and MarkDuplicates2.stat), because realignment may change genomic positions of read pairs. After this step additional duplicates can be identified. The next step is a recalibration of base quality values. For each base in each read various covariates (such as reported quality score, position in read, dinucleotide, read GC-content) are calculated. Using these values the algorithm builds the model that predicts sequencing errors. Then it applies this model to calculate an empirical base quality score and overwrites the phred quality score currently in the read. Output is a new BAM file (Good.bam).

This file is used for the unified GATK (Genome Analysis Toolkit) genotyper method to detect the SNP-indels (table in VCF format) which the user can visualize by double click.

After Zooming in information of variation on nucleotide basis is shown.

In the next step each identified variation (SNP_indels) is analysed with the help of the “variant_effect_predictor” algorithm (http://platform.genexplain.com/bioumlweb/#de=analyses/Galaxy/ensembl/variant_effect_predictor). As a result it creates a final variant effects table that gives detailed information about each variation.

4.1.7. Quantification of RNA-seq with Cufflinks (no de-novo assembly) for FASTQ files

This workflow offers a possibility to discover new genes and transcripts (splice variants) and measure transcript expression in a single assay from RNA-seq data. This workflow is described in “Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks”, Nat. Protoc. 7:562-578, 2012.

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify the Experiment fastq files and the Control fastq files.
To specify the input files in Sanger FASTQ format, you can drag & drop it from your project within the tree area.

Step 3. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Start the workflow by pressing the [Run workflow] button.

All results are saved in the result folder:

data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC), GSE32424, FASTQ files/Data/Fastq files (Quantification of RNA-seq (no de-novo assembly))

Read alignment with TopHat

The first step of the workflow is alignment of sequence reads with TopHat (http://tophat.cbcb.umd.edu/). TopHat aligns reads to the genome and discovers transcript splice sites. TopHat uses Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) as an alignment ‘engine’ and breaks up reads that Bowtie cannot align on its own into smaller pieces called segments.

Output files are tables and tracks with insertions, deletions, splice junctions and the alignments.

Example output of splice junctions opened as track:

Mismatches, insertions and deletions in the alignments can identify polymorphisms between the sequenced sample and the reference genome, or even pinpoint gene fusion events in tumor samples. Reads that align outside annotated genes are often strong evidence of new protein-coding genes and noncoding RNAs. RNA-seq read alignments can reveal new alternative splicing events and isoforms. Alignments can also be used to accurately quantify gene and transcript expression, because the number of reads produced by a transcript is proportional to its abundance.

Differential analysis with Cuffdiff

The second step of the workflow is performed by Cuffdiff, part of the Cufflinks package, which calculates expression in two or more samples and tests the statistical significance of each observed change in expression between them. Cuffdiff allows for supplying multiple technical or biological replicate sequencing libraries per condition. With multiple replicates, Cuffdiff learns how read counts vary for each gene across the replicates and uses these variance estimates to calculate the significance of observed changes in expression.

Cuffdiff reports 15 output files:

The last step of the workflow comprises several conversions and filtering steps of some Cuffdiff output files. The final table and tracks are in the result folder:

The table Differentially expressed Ensembl genes contains all identified differentially expressed genes (Ensembl IDs) (also converted into a table of transcripts and a table of TRANSPATH® proteins).

The Regulated promoters are extracted from the table Differentially expressed Ensembl transcripts. Transcripts with the same transcription start site (TSS) are merged into the single ‘TSS group’ and Differentially expressed TSS groups are also identified. Regulated promoters from TSS groups are identified using those TSS groups. Similarly, Differentially expressed CDS groups are identified (CDS group is the group of transcripts with the same coding sequence; they produce exactly the same protein). And CDS group table is used to find Differentially expressed TRANSPATH proteins from CDS groups.

4.1.8. Quantification of RNA-seq with Cufflinks (with de-novo assembly) for FASTQ files

This workflow offers the ability to discover new genes and transcripts (splice variants) and measure transcript expression in a single assay from RNA-seq data. This workflow is described in “Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks”, Nat. Protoc. 7:562-578, 2012.

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify the Experiment fastq files and the Control fastq files.
To specify the input files in Sanger FASTQ format, you can drag & drop it from your project within the tree area.

Step 3. Specify the Reference annotation.

Step 4. Specify the Reference sequence.

Step 5. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Start the workflow by pressing the [Run workflow] button.

All results are saved in the result folder:

data/Examples/RNA-Seq analysis of human esophageal squamous cell carcinoma (ESCC), GSE32424, FASTQ files/Data/Fastq files (Quantification of RNA-seq (with de novo assembly))

Read alignment with TopHat

The first step of the workflow is a read alignment with TopHat (http://tophat.cbcb.umd.edu/). TopHat aligns reads to the genome and discovers transcript splice sites. TopHat uses Bowtie (http://bowtie-bio.sourceforge.net/index.shtml) as an alignment ‘engine’ and breaks up reads that Bowtie cannot align on its own into smaller pieces called segments.

Output files are tables and tracks with insertions, deletions, splice junctions and the alignments.

Example output of splice junctions opened as track:

Mismatches, insertions and deletions in the alignments can identify polymorphisms between the sequenced sample and the reference genome, or even pinpoint gene fusion events in tumor samples. Reads that align outside annotated genes are often strong evidence of new protein-coding genes and noncoding RNAs. RNA-seq read alignments can reveal new alternative splicing events and isoforms. Alignments can also be used to accurately quantify gene and transcript expression, because the number of reads produced by a transcript is proportional to its abundance.

Transcript assembly with Cufflinks

Cufflinks uses the alignments to map reads against the genome and to assemble the reads into transcripts. Cufflinks assembles individual transcripts from RNA-seq reads that have been aligned to the genome. Because a sample may contain reads from multiple splice variants for a given gene, Cufflinks must be able to infer the splicing structure of each gene. Thus, Cufflinks reports a parsimonious transcriptome assembly of the data. The algorithm reports as few full-length transcript fragments or ‘transfrags’ as are needed to ‘explain’ all the splicing event outcomes in the input data. Output tracks of Cufflinks is the Assembled transcripts track, output tables of Cufflinks are Gene expression and Transcript expression tables.

This step distinguishes this workflow from the workflow called “Quantification of RNA-seq with Cufflinks (no de-novo assembly) for FASTQ files”. In the current workflow the transcripts are assembled “de-novo”, whereas in that other workflow the transcripts are taken from the reference Ensembl transcript annotation. Since here it is a “de-novo” reconstruction of exon-intron structure, no known gene or transcript names are given. All transcripts are defined by the tracking_id, like Cuff.1.1 and so on. This allows us to find new transcripts that were not yet discovered and annotated in the reference genome.

Assembled transcripts merging with Cuffmerge

When you are working with several RNA-seq samples, it becomes necessary to pool the data and assemble them into a comprehensive set of transcripts before proceeding to differential analysis. Cuffmerge, part of the Cufflinks package (http://cufflinks.cbcb.umd.edu/) is essentially a ‘meta-assembler’ — it treats the assembled transfrags the way Cufflinks treats reads, merging them together parsimoniously. Output is a Merged assembly track.

Differential analysis with Cuffdiff

The next step of the workflow is performed by Cuffdiff, part of the Cufflinks package (http://cufflinks.cbcb.umd.edu/), which calculates expression in two or more samples and tests the statistical significance of each observed change in expression between them. Cuffdiff allows supplying multiple technical or biological replicate sequencing libraries per condition. With multiple replicates, Cuffdiff learns how read counts vary for each gene across the replicates and uses these variance estimates to calculate the significance of observed changes in expression.

Cuffdiff reports the following 11 output files:

4.2. Detect differentially expressed gene (DEG)

In order to perform further analyses of the results of the workflow Quantification of RNA-seq with Cufflinks for multiple BAM files it is recommended to join all resulting gene tables into one table using the function “Join several tables” of the platform. The joint table can be used for detection of differentially expressed genes using the Limma or EBarrays functions.

It should be noted here that to perform a Limma or EBarray analysis of the RNA-Seq data you should select the option Unnormalized counts in the input field Input log-base for either of these two methods, as shown below for Limma.

4.2.1 Estimate differential expression using Linear Models for MicroArrays (LIMMA)

Limma estimates differential expression between specified conditions / groups.

This tool provides an interface for the popular and comprehensive Limma package. The platform tool computes differential expression between up to five conditions / groups. The groups consist of columns of a data table that contains normalized measurement values, e.g. from a normalized microarray experiment. Furthermore, one can estimate differential expression for normalized or un-normalized count data as derived from RNA-seq experiments.

All possible contrasts between groups are considered and their output is stored in a common folder. Conditions are compared in the specified order from first to fifth. E.g. given conditions named A, B and C, the output will contain the contrasts AvsB, AvsC and BvsC.

It is necessary to provide a unique name for each group. Also, at least two data columns are required per group.

The input parameters for Limma are described in the following.

Input table: This table contains the columns to analyze.

Input log-base: Here you can specify the scale of the input data. If the log-base is log₂, the tool will use the data values as is. If your data are from RNA-seq, you can select Normalized counts or Unnormalized counts.

1-5. Condition / group name: One can specify up to five groups of columns. Please note that unnamed groups are not considered; a name is not assigned automatically.

1-5. Columns: These fields contain the selected columns. Please note that column selections are not considered without a corresponding name. Columns can only be specified once and there need to be two columns per group.

Output folder: The output folder will contain one output files for each pair of conditions.

An example output table is shown at the end of this section. Its columns are explained in the following. Those highlighted in bold are shown in the default view. The other columns can be included on demand via the Columns tab of the lower right panel (available with opened output table).

logFC: Fold change (log)

CI.025: Fold change (Lower confidence interval)

CI.975: Fold change (Upper confidence interval)

AveExpr: Average log2-expression for the probe over all arrays

t: Moderated T-statistic

P.Value: P-value Differential expression

adj.P.Val: Adjusted P-value (Benjamini-Hochberg)

B: Log-odds that the gene / probe presents differential expression

Reference:
Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, 2005.

4.2.2. Estimate differential expression by the gene expression mixture model of EBarrays

EBarrays estimates differential expression between specified conditions / groups.

This tool provides for differential expression analysis using the EBarrays package. The platform tool can compare up to five conditions / groups. The groups consist of columns of a data table that contains normalized measurement values, e.g. from a normalized microarry experiment.

EBarrays sets up a mixture model matching the specified groups. Differential expression is identified when components for a pattern describe the distribution of measurement values well. Then probe / gene values in the corresponding group were significantly different from their values in the other groups. This is reflected by high posterior probabilities in the column named after that group.

The package estimates a critical posterior probability cutoff for the given FDR level on the basis of the fitted mixture model. Probes / genes exceeding this cutoff in some condition / group are indicated by a value of 1 (instead of -1) in the output column named "condition name Sig". Hence, to isolate the targets differentially expressed in a condition of interest, e.g. condition named "treatment", filter the table for all rows with a value of 1 in the column "treatment Sig". The direction of differential expression can be derived from the fold change column "condition name FC", which contains the log2-fold changes.

The input parameters for EBarrays are described in the following.

Input table: This table contains the columns to analyze.

Input log-base: Here you can specify the scale of the input data. If the log-base is none, the tool will use the data values as is. If your data are from RNA-seq, you can select Normalized counts or Unnormalized counts.

1-5. Condition / group name: One can specify up to five groups of columns. Please note that unnamed groups are not considered; a name is not assigned automatically. Fields for the first two groups need to be set.

1-5. Columns: These fields contain the selected columns. Please note that column selections are not considered without a corresponding name. Columns can only be specified once and there need to be two columns per group. Fields for the first two groups need to be set.

1-5. Is control: Use this field to mark the control column group. One such group is required.

Output folder: The output folder will contain one output files for each pair of conditions.

It is necessary to provide a unique name for each group. Also, at least two data columns are required per group and one group needs to be marked as control group.

Besides the main output table containing differential expression estimates for each probe / gene, EBarrays provides two diagnostic plots named EBarrays CCV and EBarrays Marginal fit. These plots enable a judgment about whether assumptions of the approach hold and how well the fitted model represents the data (please refer to the documentation of the EBarrays Bioconductor package for further details). Examples of an output table, a CCV plot and a Marginal fit plot are shown at the end of this section.

Reference:
Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:3899-3914.

4.3. Further workflows in this area

For the other workflows that you can find in the area RNA-seq, please refer to the following Sections:

Load data

See Chapter 3

Discover functional enrichment of DEG

See Section 10.3

Analyze networks of DEG

See Section 5.1

Analyze regulatory regions of DEG

See Section 10.4

Find drug targets

See Chapter 11

Proteomics

5.1. Analyze networks

5.1.1. Find master regulators

Potential master regulators of the processes analyzed in a typical proteomics experiment can be identified with the aid of pathway databases. The geneXplain platform support working with the TRANSPATH database (5.1.1.1; license required) or with the public GeneWays database (5.1.1.2). More details about both these database can be found in the corresponding Sections 19.7 and 19.5.

5.1.1.1. Find master regulators with TRANSPATH®

As elsewhere, these workflows can be used to analyze data of a single protein table or to mine multiple protein sets. These two options will be explained in the following, complemented by a more detailed explanation how the interpretation of the results should be done.

Analyze a single gene table

This workflow is designed to find important master regulators in signal transduction pathways. The search is done based on the network of the TRANSPATH® database with a maximum radius of 10 steps upstream of an input gene set, a default cutoff for Score at 0.2, for FDR at 0.05, and for Z-score at 1.0.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step2. Specify input gene set. The input gene set might be a list of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input gene set. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated by means of the genes shown to be up-regulated in one of the pre-prepared examples. The pertinent example file can be found in the geneXplain platform online under the path:
http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E1)

When you have selected the gene set, press [Ok].

Step 3. Specify the biological species of the input set in the field Species by selecting the required biological species from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field “select element” in the field “Results folder”, and a new window will be opened, where you can select the location of the results folder and define its name as shown below.

After you have given the name, press [Ok].

Step5. Press the [Run workflow] button. Wait until the workflow is completed, which is shown below:

The results folder contains several files.

The primary result table Regulators upstream 10 () is a list of master regulatory molecules that were identified at the distance up to 10 steps upstream of the input molecules. Each master regulatory molecule is characterized by a Score, Z-score, FDR, and Ranks Sum. Further details about these parameters can be found below, under “Interpretation of the results”.

The column “Reached from set” shows the number of the molecules from the input set that is reached from the respective master regulator, and these molecules are explicitly listed in the column Hit names. The column Reachable total presents the total number of molecules that can be reached from the master regulator in the network, independent of the input list. Details about Score, Z-score, FDR and Ranks sum columns are given below, under “Interpretation of the results”..

Having this table opened in the Work Space you can find additional options available, specific for this kind of table. Select one or several rows in the table “Regulators upstream 10” by mouse click, and you can visualize the network of the selected master regulators (), save the network as a list of genes in the Tree Area (), or save the hits of this network, listed in the column Hit names as a list of genes in the Tree Area
().

The table Regulator proteins () corresponds to the table Regulators upstream 10 converted into the UniProtKB/Swiss-Prot IDs.

The table Regulator genes () corresponds to the table Regulators upstream 10 converted into the Ensembl IDs and in the table Regulator genes annot () the same genes are additionally annotated with gene symbols and gene descriptions.

The three diagrams Top 3 regulators () visualize the networks individually for each of three top master regulators. By default, the top regulators are identified upon sorting the table Regulators upstream 10 () by the column Ranks sum with the lowest rank on top.

The default color code for the molecules is the following:
blue: molecules from the input list
red: master regulatory molecules;
green: connecting molecules considered by the graph-analyzing algorithm to find the path from input list to the master molecule.

If you are interested in visualizing the network for any other master regulator, you can do this in the following way. Open the table Regulators upstream 10 and select the master regulator by a single mouse click, then click the button to visualize the selected row and save the new diagram.

Tip for the workflow editing

You can easily create a similar workflow with parameter values adjusted to your needs. For example, you might be interested to change the number of steps used for the regulator search. By default, 10 steps are applied.

To make a change, you need first to open the workflow under the “Edit workflow” mode, and save its copy in your project area. The [Edit workflow] button is located near the button [Run workflow] (see above, Step 1). Upon clicking on "Edit workflow", the workflow diagram will be opened in the Work Space, and you can select the analysis box you would like to modify. On the screenshot below "Regulator search" analysis was selected, and in the Operations Field, on the tab "Workflow", all the parameters are visible. Under this mode, you can modify default parameters and then save the workflow.

In this way you will get a customized workflow, with the parameters specified according to your needs.

Note. This workflow is available together with a valid TRANSPATH® license.
Please, feel free to ask for details (info@genexplain.com).

Analyze multiple gene sets

The input is a folder with several gene/protein tables. The steps of this workflow for each individual gene/protein table are the same as described in the section above. The same steps are performed iteratively for each of the gene/protein tables in the input folder.

The output is a folder which contains subfolders with the results for each individual gene/protein table.

Interpretation of the results

Score

The score value of each master regulatory molecule reflects how well this molecule is connected with other molecules in the database, and how many molecules from the input list are present in the network of this master molecule. The higher the Score value, the better is this molecule connected in the database, and the more “Hits” from the input list are present in the network of this molecule. By default, only the molecules with Score > 0.2 are shown in the output.

Because molecules with high Scores are well connected in the database, they are being suggested quite often by the tool as potential master regulators even with different input lists, and sometimes such molecules are also expected to be found a priori. It is possible to say that the molecules with the highest Score values are a kind of “trivial” and expected solutions. At the same time, and also because of their good connectivity, they are well studied and published. Therefore the molecules with high Score values might be biologically interesting as known “hubs” in a network.

Let’s have a look at the table Regulators Upstream 10, available in the geneXplain platform online under the path:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E1)%20(Master%20regulators%20Transpath)/Regulators%20upstream%2010

In the screenshot below, the table has been sorted by Score, and we can see PKCalpha, PDK1, Cdk5 as the three top molecules with highest Score values. These are well-studied molecules, and in many cases there is no surprise for the researcher to find such molecules as master regulators; it is a kind of expected result. However, master regulators with high Scores might be of interest if you are looking for well-studied reliable molecules, and would like to see many of the input molecules connected by such master regulators.

Z-score

The Z-score value reflects how specific each master molecule is for the input list. The higher the Z-score value for a molecule, the more specific this molecule is for the input list, and the lesser is the probability to find such a molecule as master regulator in another analysis. Z-score and FDR are calculated based on 1000 random results, for which 1000 random input sets of the same size were generated by the algorithm.

Importantly, Score and Z-score reflect different characteristics of the suggested master regulators in the networks. Molecules with high Score values are well connected in the database, and therefore not very specific for the input list, and correspondingly they have quite moderate Z-score values.

Molecules with highest Z-scores are very specific for the input list, probably because of a few connections that are specific for the input list, but generally they are not so well connected within the database and therefore have quite low Score values.

Sorting by Z-score and considering top molecules might be helpful if you are interested in finding novel master regulators which are specific for your input list and generally are not well studied yet. By default, only the molecules with Z-score > 1.0 are shown in the output.

On the screenshot below the same table as above is sorted here by Z-score, and we can see different molecules on top. Even by the names of these molecules the expert can see that they are not coming up so often in the literature, and might represent interesting novel candidates.

Ranks sum

This column helps to suggest molecules for which both values, Score and Z-score, are quite good. The column Ranks sum reflects a combination of sorting by Score and by Z-score in the following way.

Upon sorting by Score from biggest values to the lowest, a rank is assigned to the molecules; the molecule with the highest Score has rank 1, etc.

Upon independent sorting by Z-Score from biggest values to lowest, a rank is assigned to the molecules; the molecule with the highest Z-score has rank 1, etc.

Next, for each molecule, the ranks upon sorting by Score and upon sorting by Z-Score are summed up in the column Ranks Sum. The lower the Ranks sum, the more interesting the candidate molecule is, with good Score and good Z-score values.

On the screenshot below the table from above is sorted by Ranks sum, and we can see different molecules on top. Upon such sorting, on top there are molecules with a good connection in the database, and simultaneously to a quite good extent specific for the input list. In this example, Score values for the top molecules are between 0.34 and 0.45 (moderate), Z-score values vary between 4.7 and 8.4 (very good Z-score values, but not the best in this table).

By default, the table Regulators Upstream 10 are sorted by the Ranks sum column, to suggest molecules with a balance between their well-studied status and high connectivity (reflected by Score), and novelty and specificity for the input list (reflected by the Z-score).

Suggestion for sorting master regulatory molecules

It might be very helpful to find out which of the suggested master regulators are expressed in your experiment, and especially which are up-regulated. Such molecules might be promising candidates for further experimental examinations.
To do this, you can take the table in the result folder “Regulator genes annot” and take it as input for the analysis “Annotate table”.

As annotation source, you can select the table of genes expressed in the same experiment, e.g. the table of all expressed genes that resulted from the workflow “Detect differentially expressed genes”; in this example the path is:

data/Examples/Breast Cancer GSE9187, Agilent 014850 microarray/Data/metadherin gene knockdown cells and control cells/Deferentially expressed genes/Genes, Fold Genes, fold change and p-value, non-filtered

As “Annotation column” you can select LogFoldChange, and as a result the suggested master regulators are annotated by their expression.

If you are interested in finding reliable well-studied master regulators, e.g. to confirm already known ones, and would like a master regulator network to contain as many molecules from the input list as possible, you might be interested to sort by Score, and consider master molecules with the highest Score values.

If you are looking for novel master regulators that are very specific for your input list, even when they are not well studied yet, you might be interested to sort by Z-score, and consider master molecules with highest Z-score values.

If you are looking for a good balance between well-connected molecules and novel ones specific for your input list, you might be interested to stay with the default sorting by Ranks sum, and consider master molecules with the lowest Ranks sum values.

5.1.1.2. Find master regulators with GeneWays

This workflow is designed to find important master regulators in the signal transduction pathways. Here, a search for master regulators is done based on the network of the GeneWays database with a maximum radius of 4 steps upstream of an input gene set, a default cutoff for Score at 0.2, for FDR at 0.05, and for Z-score at 1.0.
The input form and the resulting tables are very similar to the workflow described above, “Find master regulators in networks (TRANSPATH®)”, please refer to Section 5.1.1.1.

The major difference between these two workflows is the underlying database applied for the network analysis, either TRANSPATH® or GeneWays.

More details about the GeneWays and TRANSPATH® databases can be found in Sections 19.5 and 19.7, respectively.

5.1.2. Find common effectors

5.1.2.1. Find common effectors with TRANSPATH®

This workflow is designed to find important effectors in signal transduction pathways. With this workflow, the effector search is done based on the network of the TRANSPATH® database with a maximum radius of 10 steps, FDR cutoff at 0.05, Score cutoff at 0.2 and a Z-score cutoff at 1.0. You have an option to edit the default parameters using the button [Edit workflow].

To launch the workflow, follow these steps:

Step 1. Open the workflow input form via the Start page. It looks as shown below:

Step2. Specify the input gene set. The input gene set might be a list of differentially regulated genes or any gene or protein list of interest. You can drag & drop it from your project within the tree area and drop it in the pink box of the field Input gene set. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of this workflow are demonstrated with genes shown to be up-regulated in one of the examples. The example file can be accessed using the URL:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E2)

After you have selected the gene set, press [Ok].

Step3. Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field “select element” in the field Results folder, and a new window will be opened where you can select the location of the results folder and define its name as shown below.

After you have specified the name, press [Ok].

Step 5. Press the button [Run workflow]. Wait until the workflow is completed, which is shown below:

The results folder contains several files; in the example given the path is:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Common Effectors_TP

The primary result table Effectors downstream 10 () is the list of effector molecules that were identified at the distance up to 10 steps downstream of the input molecules. Each effector molecule is characterized by Score, Z-score, FDR, and Ranks Sum. Further details about these parameters can be found in the Section 5.1.1.1, under “Interpretation of the results”.

The column Reached from set shows the number of molecules from the input list from which the respective effector molecule can be reached.

The column Reachable total gives the total number of molecules from which the respective effector molecule can be reached, independent of the input list.

Having this table opened in the Work Space you can find additional options available, specific for this kind of table. Select one or several rows in the table Effectors downstream 10 by mouse click, and you can visualize the network of the selected Effectors (), save the network as a list of genes in the Tree Area (), or save hits of this network from the column Hits as a list of genes in the Tree Area ().

The table Effector genes annot ( ) corresponds to the table Effectors downstream 10 converted into Ensembl IDs and additionally annotated with gene symbols and gene descriptions.

The table Effector proteins () corresponds to the table Effectors downstream 10 converted into the UniProt IDs.

The table Transpath peptides () corresponds to the table Effectors downstream 10 converted into TRANSPATH® molecule IDs, and in the table Transpath peptides annot they are further annotated with gene symbols and gene descriptions.

The three diagrams Top 3 effectors () visualize networks individually for each of the three top effector molecules. By default, the top effectors are identified upon sorting the table Effectors downstream 10 () by the column Ranks sum with the lowest rank on top.

The default color code for the molecules is the following:
blue: molecules from the input list
red: master regulatory molecules;
green: connecting molecules considered by the graph-analyzing algorithm to find the path from input list to the master molecule.

If you are interested in visualizing the network for any other effector molecule, you may do so in the following way. Open the table Effectors downstream 10 and select a row with a single mouse click as shown below. Click on menu button to visualize the selected row and save the new diagram into the tree.

Tip for the workflow editing

You can easily create a similar workflow with parameter values adjusted to your needs. For example, you might be interested in changing the number of steps used for the effector search. By default, 10 steps are applied.

To make a change, you need first to open the workflow in the “Edit workflow” mode, and save its copy in your project area. The [Edit workflow] button is located near the button [Run workflow] (see above, Step 1). Upon clicking on [Edit workflow], the workflow diagram will be opened in the work area, and you can select one of the analyses you would like to modify. For the screenshot below "Effector Search" analysis was selected, and in the Operations Field, on the tab "Workflow", all parameters are visible. Under this mode, you can modify default parameters and then save the workflow.

In this way you will get a customized workflow, with the parameters adapted to your needs.

Note. This workflow is available together with a valid TRANSPATH® license.
Please, feel free to ask for details (info@genexplain.com).

5.1.2.2. Find common effectors with GeneWays

This workflow is designed to find important effector molecules in signal transduction pathways. Here, a search for effector molecules is done based on the network of the GeneWays database with a maximum radius of 4 steps upstream of an input gene set, default cutoffs for Score at 0.2, for FDR at 0.05, and for Z-score at 1.0.
The Input form and the resulting tables are very similar to the workflow described above, “Find common effectors in networks (TRANSPATH®)”, please refer to Section 5.1.2.1.

The major difference between these two workflows is the underlying database applied for the network analysis, either TRANSPATH® or GeneWays.

More details about the GeneWays and TRANSPATH® databases can be found in the Sections 19.5 and 19.7, respectively.

5.1.3. Identify functional protein cluster by shortest path analysis

This analysis finds functional clusters in any input table of genes or proteins. It can be found under the tab Analyses, in the folder Methods/Data manipulation/ Molecular networks/Cluster by shortest path (). Here the default input form is shown:

When the expert options are opened, an additional field Input size appears, and the form looks like:

In the following, we will consider the input fields one by one.

Search Collection. First, decide which database/search collection you want to use. The connections between the molecules from the specified database will be considered by the clustering algorithm to find the clusters in the input table. You can choose a search collection from the drop-down menu, as shown on the screenshot below. Four search collections are available: GeneWays, Reactome, TRANSPATH® (Species specific) and TRANSPATH® (TF specific).

If you are interested in applying TRANSPATH®, you have to choose either the Species specific or the TF specific collection. This choice depends on the input table. If you search for clusters among transcription factors, and your input table is a table of transcription factors, it is recommended to choose TRANSPATH® (TF specific). If your input table contains different genes/proteins, TFs and/or other functional groups, it is recommended to choose TRANSPATH® (Species specific).

By default the GeneWays database is applied. Here, the TRANSPATH® (Species specific) collection is chosen.

Molecules collection. Input the collection of molecules/genes for which you wish to find clusters. The input table type depends on the specified search collection. In case of the Geneways database as search collection, the input type should be table with Entrez gene IDs (). In case of TRANSPATH®, the input table should be a table with TRANSPATH® peptides (). In case of Reactome, the input table should be a table having Reactome protein IDs (). As soon as the search collection is specified, the icon for the required table type is automatically shown in the field Molecules collection, as shown in the two screenshots above for TRANSPATH® and GeneWays, respectively.

Tip Before input your table, check which kind of IDs this table has. If necessary, convert your table into any of these formats. You can use the Convert table method as mentioned in Section 16.1.2.

Search Direction. Select the direction which the algorithm should consider to find connections between the input molecules. It can be upstream of your input molecules, or downstream, or in both directions. By default the analysis searches in the upstream direction.

Max radius. Maximum number of steps which the algorithm should consider in the specified direction. By default the number is 3.

Display intermediate molecules. By default this option is unchecked, and in the resulting clusters only the molecules from the input set will be shown. If you prefer the intermediate molecules to be displayed as well, check this box.

Species. Specify human, mouse or rat species corresponding to the input table.

Output name. Specify the path to store the results and the name of the output folder.

Having filled the input form, launch the analysis with the [Run] button. Analysis progresses as shown below. Wait till the analysis is completed.

Results

As a result of this analysis, a folder with a specified name is formed, in this example: E2F target genes shortest path Upstream 3, shown below. This folder contains one table, which represents the list of all identified clusters, and several diagrams corresponding to the number of the identified clusters.

The table Clusters () contains a list of all identified clusters, here 9, shown below. Each row shows details for one cluster. The clusters are sorted by their size with the largest cluster on top. The symbol next to each cluster name in the column Diagram can be used for visualization. The column Hit names contains the names of the TRANSPATH® proteins in each cluster.

The visualization of Cluster 1 is shown below. The box Display intermediate molecules was unchecked, default setting. All the molecules shown are coming from the input gene/protein set. The numbers on the arrows correspond to the number of steps between two molecules.

When the box Display intermediate molecules is checked, the cluster is displayed as shown below. Molecules shown in blue color are coming from the input gene/protein list, and those in green are added by the algorithm when necessary for the connectivity between the input molecules. These green molecules are so-called intermediate molecules.

5.2. Further workflows in this area

For the other workflows that you can find in the area Proteomics, please refer to the following Sections:

Load protein list

See Chapter 3

Discover functional enrichment of DEG

See Section 10.3

Find drug targets

See Chapter 11

Epigenomics

6.1. Analyze genomic intervals

6.1.1. Identify and classify target genes near the intervals

For the three workflows subsumed under this topic, please refer to Section 7.2.1 and apply the steps explained there correspondingly.

6.1.2. Site search with TRANSFAC

6.1.2.1. Site search in a single interval list

This workflow helps to map putative TFBSs on peaks calculated from your
ChIP-seq data. Site search is done with the help of the TRANSFAC® library of positional weight matrices, PWMs, using the pre-computed profile vertebrate_non_redundant_minSUM.

The few steps to launch the workflow are described in the following.

Step 1. Open workflow input form from the Start page, it will be opened in the main Work Space and looks as it is shown below:

Step 2. Specify the input track in BED format in the field Input Yes track.
The input Yes track contains peaks from your ChIP-seq study. To specify the Yes track, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you select the input track. After having selected the track, press the [Ok] button.

Step 3. Specify the sequence source from the drop-down menu. Several human, mouse and rat sequence builds are available in the platform, as shown below. By default, the most recent Ensembl human genome, hg19, is specified. Make sure you selected the sequence source (the genome build) that corresponds to your input set, to get correct and meaningful results.

Step 4. Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 5. Specify No track in BED format in the field Input No track. Upon clicking on this field, a supplementary window will open, where you can select the No track from your project tree, or use one of our default No tracks for human, mouse or rat, respectively.

Step 6. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Results folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Step 7. Press the [Run workflow] button.

Ready!

Wait until the workflow is completed.

The results folder contains two tables and two tracks; for this example, let’s consider the results folder located under “Examples”. It is highlighted by blue in the figure below:

The tables Site optimization summary () and Transcription factors () are opened automatically in the Work Space as soon as the workflow is completed.

The table Site optimization summary includes the matrices the hits of which are over-represented in the Yes track versus the No track.

Please note that only the matrices with Yes-No ratio higher than 1 are included in this output table. The hits of these matrices can be interpreted as over-represented in the Yes set versus No set.

The table Site optimization summary shown below has been sorted by the values in the Yes-No ratio column.

Each row summarizes the information for one PWM. For each selected matrix, the columns Yes density per 1000bp and No density per 1000bp show the number of matches normalized per 1000 bp length for the sequences in the input Yes set and input No set, respectively. The Column Yes-No ratio is the ratio of the first two columns. Only matrices with a Yes-No ratio higher than 1 are included in the summary table. The higher the Yes-No ratio, the higher is the enrichment of matches for the respective matrix in the Yes set. The matrix cutoff values as they are calculated by the program at the optimization step are shown in the column Model cutoff, and the last column shows the P-value of the corresponding event.

Table Transcription factors:

This table includes transcription factors (TFs) that are associated with the PWMs that are listed in the table Site optimization summary, and each row shows details for one TF, including its Ensembl gene ID (column ID), gene symbol, gene description and biological species of the corresponding TF (columns Gene description, Gene symbol, and Species). The column Site model ID shows the identifier of the PWM associated with this TF, and several further columns repeat information that is also shown in the table Site optimization summary.

Tracks “Yes sites opt” and “No sites opt” ().

Each row presents details for each individual match for every PWM. Columns Sequence (chromosome) name, From, To, Length and Strand show the genomic location of the match including chromosome number, start and end positions, strand and length of the match, respectively. The column Type contains information about the type of the elements; in this case all matches are considered as “TF binding site”. Further columns keep information about PWM producing each match (column Property:matrix) as well as a score of the core (column Property:coreScore) and a score for the whole matrix (column Property:score). The column Property: siteModel contains an identifier for the site model, which is the matrix together with the cutoff applied (for details about these scores, please see Kel et al., Nucleic Acids Res. 31:3576-3579, 2003).

Tip. Further visualization of track files in the genome browser:
Having tracks “Yes sites opt” and “No sites opt” opened in the Work Space, the menu button can be applied to get a visualization. First, a supplementary window is opened where you can select one chromosome and press [Ok], as shown below.

In the second pop-up window, you can select tracks that can be visualized together with your track, e.g. “Yes sites opt” (see above), and press [Ok].

The resulting visualization, after applying the “zoom in” button , looks like it is shown below. Matches for different matrices are shown in colors, and the color schema can be customized.

Such a view may help to visually co-localize information on different tracks, e.g. putative TFBS with variations, repeats and genes. In the figure above, the cursor shows position 29444, and two variations are located at this position. You can immediately recognize that these variations are located within particular putative binding sites in the intron region of the WASH7P gene.

The same information is available not just as a picture, but also as a table under the tab “Sites” (shown below). For each element information is shown on chromosome, positions, length, strand, type of the track, and name of the element.

This table can be exported as a track, in several different formats including intervals, bed, wig, gff, gtf and more.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

6.1.2.2. Site search in multiple interval sets

This workflow is designed to search for TFBSs in DNA sequences identified by the ChIP-seq approach, for multiple datasets.

In the field Input Yes tracks, several different tracks can be simultaneously submitted. The same background dataset, Input No track, is used for comparison with each of the submitted Yes tracks. The default No track corresponds to far upstream regions of the house keeping genes, where no functional TFBSs are expected.

The steps of this workflow for a single input Yes track are described in Section 6.1.2.1. In this workflow, the same steps are performed next time for the 2nd Yes track, and so on iteratively for each of the input Yes tracks.

This workflow helps to save time and efforts, especially when you have several sets of ChIP-seq data, e.g. the peaks for a number of different TFs.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

6.1.3. Search for composite modules

6.1.3.1. Search for composite modules with TRANSFAC®

This workflow finds pairs of TFBSs that discriminate between two tracks, the Yes and the No track. As the Yes track, ChIP-seq peaks or intervals identified in analyses for histone modifications, or any other genomic fragments, can be considered.

In the first part of the workflow, the overrepresented TFBSs are identified by the method analyses/Methods/Site analysis/Site search on gene set (). Details about this individual method are given in Section 20.1.2. In the second part of this workflow, composite modules are identified by a genetic algorithm based on over-represented TFBSs. For more details about this CMA analysis refer to Section 20.1.5.

To launch the workflow, follow these steps:

Step1. Open the workflow input form. It will open in the main Work Space and looks as shown below:

Step 2. Input the Yes track (the track under study) from the tree. You can either drag-and-drop or select it from the Tree Area. Here the track published in the Gene Expression Omnibus, GSE54909, is used as an example.

In this study the HDAC inhibitors TSA and SAHA were used to treat primary human vascular endothelial cells. The effects on genome-wide histone acetylation were studied applying chromatin immunoprecipitation (ChIP) and then deep sequencing (ChIP-seq). Histone acetylation ChIP-seq profiles in SAHA-treated versus control samples were calculated and the peaks were published. The published track contains over 10000 fragments.

Please consider that such huge tracks cannot be submitted into the workflow. One cannot expect to find common composite modules in 10000 fragments.

Tip We recommend to filter the original track by several conditions, for example by the score of the calculated peaks, by the length of the fragments, by genes located near the peaks, etc. The recommended optimal number of the fragments in the input track is 200-400. Please consider that the time required for the workflow run directly depends on the size of the input tracks.

Here, the original published track was first filtered by the length of the fragments 500-600 bp, which resulted in 816 fragments. These fragments were sorted by the score of the peaks, and the top 200 fragments were selected.

The resulting track of 200 fragments is used as the input Yes track.

Step 3. From the drop-down menu in the field Sequence source choose the Ensembl species and build corresponding to your input track, here hg19.

Step 4. Input the No track from the tree area.

Tip for creating a No track specific for your track under study:

We recommend to apply the method Create random track as described in Section 16.2.6. This method helps to create a track with the same number of fragments and similar distribution of the fragment’s length. Important especially for the purposes of this workflow: The method Create random track ensures that the fragments in the No track are not overlapping with the fragments in the Yes track.

Here, a random track comprising 300 fragments was created and used as the No track.

Step 5. After input of the Yes and No sets, verify the species shown in the species field.

Step 6. Set up parameters for the composite module search. This workflow identifies pairs of sites. By default, the minimum and maximum numbers of pairs are given as 2 and 8. You can change these parameters according to the number of pairs you aim to identify. The number of iterations of the genetic algorithm is 300 by default, and can be adapted as required.

Step 8. Specify the result folder location and name and Press the button [Run workflow]. Wait till the workflow is completed.

Note. This workflow may take time depending on the size of the Yes and No tracks and on the number of iterations. The recommended size of the input tracks is 200-400 fragments with the length of the individual fragments not exceeding 1000 bp. The maximum recommended number of iterations is 300.

Results

The results folder contains the tables Transcription factors () and Site optimization summary (), the tracks Yes sites opt and no sites opt (), and the folder modules
().

The table Site optimization summary () contains those site models, here TRANSFAC® matrices, that are over-represented in the Yes track as compared to the No track.

Each row of the table represents the result for one PWM from the input profile. Only those PWMs with Yes-No ratio >1 are included in the output. For details of the output columns please refer to Section 20.1.2.

The Modules folder () is a result of the analysis Construct composite modules. It contains two tables, two tracks, one histogram, and one model view as shown below:

The Model View is a graphical summary for the hierarchically organized composite elements generated as a result of the CMA analysis. As mentioned above, this workflow is designed to identify pairs of sites, and we asked to identify 2 to 8 pairs. The composite module found here contains three pairs, and we can see by exactly which site models (matrices) these pairs are formed as well as the statistical parameters of the overall model.

Each of the tracks, yes track and no track, can be directly opened in the genome browser by double-clicking. Visualization of the composite module within one fragment of the input Yes track, is shown below.

Upon simultaneous visualization of this track together with default tracks provided by the platform, you can see that this composite module is actually located in the promoter region of MMP23B gene.

For more details on the individual output tables and tracks as well as for visualization of the identified composite modules in the genome browser please refer to Section 20.1.5.

The output table Transcription factors is a list of transcription factors linked to the site models in the composite module identified by the workflow. For each transcription factor, the Ensembl gene ID is provided, as well as a gene description, the HGNC gene symbol, species, and site model (TRANSFAC® PWM name).

Four TFs are found to be candidates to specifically bind the fragments in the input track of histone acetylation peaks in SAHA-treated human vascular endothelial cells. The binding motifs for these transcription factors are parts of the identified enriched composite modules.

The same workflow can be applied to find composite modules in the ChIP-seq peaks identified as binding profiles for particular transcription factors. The example of such application for E2F1 binding peaks is described in Section 7.2.3.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

6.1.4. Search for discriminative sites with TRANSFAC® (MEALR)

The tool MEALR finds combinations of TFBS matrices that discriminate between two sets of sequences (denoted as Yes and No sets). The Yes set may consist of genomic regions identified in a ChIP-seq experiment. No sequences are often other non-coding genomic regions not overlapping with the peaks.

MEALR differs from other tools in the following points.

No cutoff or threshold is used on matrix scores to determine potential binding sites. Instead, MEALR calculates threshold-free sequence scores.

MEALR builds a discriminative model for classification which is well-established and widely applied in statistical analysis called Sparse Logistic Regression. The model consists of a linear model that estimates the probability that a sequence belongs to the Yes set based on its binding site features.

The sparseness constraint enables MEALR to select a subset of matrices relevant for classification of Yes and No sequences from a possibly large matrix library. Therefore MEALR’s output differs from other tools by presenting a focused set of matrices.

While other site enrichment tools provided in the platform evaluate enrichment separately for each matrix, the model used in MEALR assesses the importance of matrices for discrimination in combination with other matrices of the library. Therefore, MEALR suggests (linear) combinations of transcription factor motifs.

MEALR calculates the score x of the ith sequence according to the kth matrix as , where Sw is the log-odds score of the wth window of matrix length. Each sequence is therefore associated with a vector of scores, one from each matrix, and a class (Yes, No).

Let us present an example analysis for a ChIP-seq data set consisting of 500 peak regions and 1000 sequences randomly sampled from regulatory regions across the human genome. The figure below depicts the input mask of the analysis tool.

Yes set: This is the set of sequence intervals that you want to analyze, for example these can be ChIP-seq peak regions.

No set: This is the set of background intervals (control set).

Sequence source: Both Yes and No track need to refer to a common source, such as a genome, as specified by this parameter. Note that you can apply a custom source, e.g. a specifically uploaded genome. Clicking on the “Custom” option will open a new field to choose the custom sequence source.

Input motif profile: The profile lists the PWMs (motifs) that are used to assign scores to Yes and No sequences. By default, this field is set to the profile last applied in your workspace. Note that cutoffs in the profile are ignored, because MEALR calculates whole sequence scores.

Output path: In this field you select a path in the workspace to store the output table.

The steps of an analysis can be described as follows:

Step 1. Input Yes set from the tree. As usual, you can drag-and-drop. Here, the set of YES intervals from the Example folder is used as input, highlighted blue on the screenshot below:

Step 2. Input No set (drag-and-drop). Our example uses the set of NO intervals:

Step 3. The sequence source should be set automatically upon specifying the interval sets. If not select the corresponding sequence source from the pull-down list:

Step 4. Select the TRANSFAC® or GTRD profile from the available profiles. In this example, we select the TRANSFAC® 2013.1 profile named “vertebrate_non_redundant”:

Step 4. Edit the output path (highlighted green in the figure above). After setting the Yes set, a default output path is suggested. The Example folder may not be writable for your account requiring selection of an alternative such as one of your own projects. A different selection can be made easily by clicking on the field.

Clicking the [Run] button will invoke the analysis. The summary table is automatically opened in a new tab when the analysis is completed. Here is a part of the output for our example:

A row of the output table contains matrix identifier and its logistic regression coefficient. The larger the coefficient value, the more important the corresponding matrix was for discriminating between Yes and No sequences. In our example, three of the five top matrices represent members of the transcription factor subfamily C/EBP.

6.2. Further workflows in this area

For the other workflows that you can find in the area Epigenomics, please refer to the following Sections:

Load chromatin or DNA modification genomic intervals

See Chapter 3

Discover functional enrichment of target genes

See Section 10.3

Analyze network of target genes

See Section 5.1

Find drug targets

See Chapter 11

ChIP-seq

7.1. Peak calling

7.1.1. MACS

MACS is a tool to identify peaks, regions likely bound by targeted protein, in ChIP-seq data. It empirically models the length of the sequenced ChIP fragments, which tends to be shorter than sonication or library construction size estimates, and uses it to improve the spatial resolution of predicted binding sites. Details about the MACS method can also be found here.

The platform provides two versions of the MACS algorithms, 1_3_7 and 1_4_0, whose parameters are explained in the following.

MACS 1_3_7

Parameters have the following meanings:

Track: Input track to search for peaks enriched with sequencing tags

Control track: A track that can be used as background (optional)

Use fixed lambda: Use a fixed local lambda for all peak regions

Lambda set: Three scopes of surrounding base pairs to calculate dynamic lambda

lambda 1: close surrounding region

lambda 2: medium width surrounding region

lambda 3: wide surrounding region

No model: Do not build the shifting model. In this mode (no model) a fixed shift size parameter is used.

Shift size: The custom shift size in bp. Used in “no model” mode.

Band width: Expected size of sonicated DNA fragments

Genome size: Effective genome size

Enrichment ratio: Cutoff for the high-confidence enrichment ratio against background

Tag size: Size of sequence tags / read length

P-value: P-value cutoff for peak detection

Future FDR: Adopt the new peak detection method. The default method only considers the peak location in the 1k, 5k, or 10kb regions of the control data. In contrast, the new method also considers the 5k or 10k regions of the test data to calculate the local bias.

Output name: Name of the output track with MACS peaks.

MACS 1_4_0

This is an advanced version of MACS 1.3.7. During model building, the new algorithm selects regions within a certain range of enrichment. By default, permissible enrichment values range from 10 to 30. If MACS fails to build the model, it will use resort to “no model”-settings with a shift size=100bps, to shift and extend each tags.

Track: Input track to search for peaks enriched with sequencing tags

Control track: A track that can be used as background (optional)

Genome size: Effective genome size

Tag size (0 = autodetect) (expert): Length of the tag sequences / read length in bp. If set to 0, it will be inferred from average read lengths over several first reads.

Band width: Expected size of sonicated DNA fragments

P-value: P-value cutoff for peak detection

MFOLD lower: Lower bound for high-confidence enrichment ratio used in building the paired-peak model

MFOLD upper: Upper bound for high-confidence enrichment ratio used in building the paired-peak model

Use fixed lambda (expert): Use a fixed local lambda for all peak regions

Small region for dynamic lambda: The close surrounding region (in base pairs) to calculate dynamic lambda. This is used to capture the bias near the peak summit region. Only applied with control data.

Large region for dynamic lambda: The large nearby region (in base pairs) to calculate dynamic lambda. Only applied with control data.

No auto pair process (expert): Whether to turn off the auto pair model process. If marked MACS will exit with an error message if the model building fails. If not marked, it will resourt to “no model”-settings

No model (expert): Do not build the shifting model. In this mode (no model) a fixed shift size parameter is used.

Shift size (expert): The custom shift size in bp. Used in “no model” mode.

Keep duplicates (expert): Consideration of replication tags which were assigned to the same genomic location.

Auto: value will be calculated based on binomial distribution using 1e-5 as p-value cutoff

All: Consider all tags

Number: Consider tags at most the specified number of replicated tags

Scale to small: Mark to scale larger dataset down to smaller one

Compute peak profile (expert): Mark to compute a peak profile

Output name: Name of the output track with MACS peaks

7.1.2. SIZER

The Galaxy tool SICER fulfills two main purposes:

1. Delineation of significantly ChIP-enriched regions, which can be used to associate with other genomic landmarks.

2. Identification of reads on the ChIP-enriched regions, which can be used for profiling and other quantitative analysis.

Parameters for SICER should be set as described in the following.

ChIP-Seq Tag File: Input track to search for peaks

ChIP-Seq Control File: A track that can be used as background (optional)

Fix off-by-one errors in output files: SICER creates non-standard output files, this option will fix these coordinates

Redundancy Threshold: The number of copies of identical reads allowed in a library

Window size: Resolution of SICER algorithm. For histone modifications, one can use 200 bp.

Fragment size: For determination of the amount of shift from the beginning of a read to the center of the DNA fragment represented by the read. FRAGMENT_SIZE=150 means the shift is 75.

Effective genome fraction: Effective Genome as fraction of the genome size. It depends on read length.

Gap size: Needs to be multiples of window size. Namely if the window size is 200, the gap size should be 0, 200, 400, 600, ...

Statistic threshold value: FDR (with control) or E-value (without control)

Output folder: The output folder contains

“Output summary” table with the fields ChIP_island_read_count, CONTROL_island_read_count, p_value, fold_change and FDR_threshold

A non-redundant version of the input track

A non-redundant version of the control track

A summary file in bedgraph format

A normalized output in Wig format

A track with significant islands

A normalized Wig file with islands

A track with island scores

Output summary: A summary output track with islands

7.2. Analyze ChIP-seq peaks

7.2.1. Identify and classify target genes near the peaks

This group of workflows helps to identify genes located near the ChIP-seq peaks or near other genomic intervals. The input can be any track, and the output contains a table of genes overlapping with the fragments of the input track. By default, the gene bound extensions are 10,000 bp 5’ relative to TSS and 10,000 bp 3’ relative to the last exon.

The three workflows in this group have a very similar structure. In the first step, the input track () is converted into a gene set using the Track to gene set analysis (), Section 16.2.4. The resulting Ensembl gene list is then submitted to Functional classification by several ontologies. In parallel, the same Ensembl gene list is subjected to Cluster by shortest path () analysis, Section 5.1.3. The difference between the workflows within this group is in the ontologies applied for functional classification as well as in a database used to find gene/protein clusters, either TRANSPATH® or GeneWays. In the three sections below, three individual workflows are demonstrated for the same input track available in one of the pre-prepared examples present in the Examples folder:
http://platform.genexplain.com/bioumlweb/#de=data/Examples/E2F1%20binding%20regions%20in%20HeLa%20cells%2C%20ChIP-Seq/Data/GSM558469_E2F1_hg19%20filtered%20chr%201

The results for each of the three workflows can be found in the folder:

data/Examples/E2F1 binding regions in HeLa cells, ChIP-Seq/Data

7.2.1.1. Classification by GO categories and metabolic pathways

In this workflow, the functional classification is done using the following ontologies: GO biological processes, GO cellular components, GO molecular function, TF classification, Reactome pathways, and HumanCyc pathways. In parallel, the same Ensembl gene list is subjected to Cluster by shortest path analysis. Gene/protein clusters are calculated based on the GeneWays interaction network.

For details, how to launch this workflow, please refer to Section 7.2.1.3. The results folder looks like this:

The input track contains 1889 in vivo binding fragments for E2F1 transcription factor. These fragments are found to overlap with 2187 Ensembl genes that are shown in the resulting table Genes Ensembl (). This table is shown in Section 7.2.1.3.

Functional classification by the HumanCyc pathways has found 8 metabolic pathways:

The top pathway visualization diagram can be opened in the work space upon a mouse click to the pathway ID:

The GeneWays clusters are calculated considering upstream direction from the identified with the maximal radius of 2 steps. The following GeneWays clusters are identified:

The picture below presents the fragment of the cluster 1.

Molecules shown in blue color are coming from the input protein list, and those in green are added by the algorithm when necessary for the connectivity between the elements of the cluster.

7.2.1.2. Classification by GO categories and signaling pathways

In this workflow, the functional classification is done using the following ontologies: GO biological processes, GO cellular components, GO molecular function, TF classification, Reactome pathways, and TRANSPATH® pathways. In parallel, the same Ensembl gene list is subjected to Cluster by shortest path analysis. Gene/protein clusters are calculated based on the TRANSPATH® signaling network.

For details, how to launch this workflow and look into the results, please refer to Section 7.2.1.1.

Functional classification by the TRANSPATH® pathways has found 43 signaling pathways and chains:

The pathway visualization diagrams can be opened in the work space upon a mouse click to the pathway ID. The fragment of the second top pathway, leptin signaling, is shown in force directed layout on the picture below. Important to mention, you can see protein complexes and modified forms on the TRANSPATH® diagrams.

Note. This workflow is available together with a valid TRANSPATH® license.
Please, feel free to ask for details (info@genexplain.com).

7.2.1.3. Classification by GO categories, signaling pathway, and diseases

In the first step of this workflow, the input track () is converted into a gene set using the Track to gene set analysis (), Section 16.2.4. The resulting Ensembl gene list is then submitted to Functional classification using the following ontologies: PROTEOME™ GO biological processes, PROTEOME™ GO cellular components, PROTEOME™ GO molecular function, PROTEOME™ disease, TRANSPATH® pathways, TF classification, Reactome pathways, and HumanCyc pathways. In parallel, the same Ensembl gene list is subjected to Cluster by shortest path analysis. Gene/protein clusters are calculated based on the TRANSPATH® network.

The input form when opened in the work space is shown below:

Step 1. Specify input track in BED format in the field Input track.

You can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field select element and a new window will be opened, where you can select the input track.

Here, further steps are demonstrated with the track available in one of the pre-prepared examples present in the Tree Area:
http://platform.genexplain.com/bioumlweb/#de=data/Examples/E2F1%20binding%20regions%20in%20HeLa%20cells%2C%20ChIP-Seq/Data/GSM558469_E2F1_hg19%20filtered%20chr%201

Step 2. After input of the track, the species (human, mouse or rat) is adjusted automatically. Verify the species shown in the Species field.

Step 3. Specify the path to store the results and the name of the output folder.

Step 4. Having filled in the input form, launch the analysis with the [Run] button. Wait till the workflow is completed.

Results

The results folder contains several files as shown below. All tables with the resulting classifications as well as the table with clusters and the diagram of the largest cluster are opened by default in the work space.

The table Genes Ensembl () contains those genes that are identified as located in the regions around the input peaks or fragments. By default this workflow considers the following regions around Ensembl genes: 10000 bp in 5’ direction from TSS and 10000 bp in 3’ direction from the last exon. The positions of each fragment on the input track are compared with positions of the extended gene regions. Genes overlapping with at least one input fragment are considered as resulting target genes. For the input track in this example, 2187 Ensembl genes are identified. The resulting table Genes Ensembl is shown below, sorted by the column Count.

Each row in this table contains information about one identified gene including Ensembl gene ID, chromosome, exact genomic positions and strand (1 or -1), gene symbol, and description. The column Count shows how many fragments on the input track are overlapping with each gene.

These genes are then converted into TRANSPATH® proteins, the output table Proteins Transpath (), shown below.

The structure of this table is very similar to that of Genes Ensembl, the critical difference is the column ID, which represents TRANSPATH® molecule IDs; Ensembl gene IDs are given in the second column. Here, 2187 Ensembl genes are converted into 1724 TRANSPATH® proteins.

Resulting TRANSPATH® proteins are clustered to get functional connections between the gene products. By default, the workflow considers upstream direction from the input TRANSPATH® proteins with a radius of 3. Resulting clusters are present in the folder Proteins clustered, shown below.

The table Clusters () contains a list of all identified clusters, here 11. Each row shows details for one cluster. The clusters are sorted by their size with the largest cluster on top. Symbol next to each cluster name in the column Diagram, can be used for visualization. The column Hit names contains names of TRANSPATH® proteins in each cluster.

A visualization of the largest cluster, Cluster 1, is opened automatically when the workflow is completed. The visualization of Cluster 1 in orthogonal layout is shown below:

Molecules shown in blue color are coming from the input protein list, and those in green are added by the algorithm when necessary for the connectivity between the elements of the cluster.

In parallel with clustering, the table Genes Ensembl undergoes to Functional classification. The results are shown in eight tables with the icon . Each table corresponds to a separate ontological category used for the classification in this workflow.

The resulting table, e.g. Mapping to Proteome GO (disease), looks like this:

Each row corresponds to one ontological category, which in this case is one of the diseases as they are annotated in the PROTEOME™ database. Commonly accepted disease identifiers are shown in the ID column. The disease names are shown in the column Title. The column Group size represents the number of genes linked to this disease in PROTEOME™, and the column Category demonstrates the functional type of the link between genes and disease; it can be causal, correlative or negative. For each row several parameters are calculated, the expected number of hits (Expected hits), the actual number of hits (Number of hits), P-value, as well as Hit names. IDs are hyperlinked to an external web page of CTD, the Comparative Toxicogenomics Database. With a click on each ID, a new tab will be opened displaying additional information about the disease.

Note. This workflow is available together with a valid PROTEOME™ license.
Please, feel free to ask for details (info@genexplain.com).

7.2.2. Site search with TRANSFAC®

7.2.2.1. Version 2.0 (Adjusted p-values, site search on track)

Single interval list

This workflow “Identify enriched motifs in tracks (TRANSFAC®)” is designed to map putative enriched TFBSs on peaks calculated from your ChIP-seq data (Yes set) as compared to a random background set (No set). Importantly, the No set is created automatically and contains by default 1000 intervals. In the first part of the workflow, the enriched motifs are identified by our proprietary MEALR approach (analyses/Methods/Site analysis/MEALR (tracks), icon ). Please refer to section 6.1.4 for details of this particular analysis method. Enriched motifs serve as a basis to construct a specific profile. At the next step this newly generated profile is run on the same list of input peaks applying the method analyses/Methods/Site analysis/Search for enriched TFBSs (tracks), icon . Please refer to section 20.1.4.2 for details of this particular analysis method. The workflow can be found under the section “Analyze ChIP-seq peaks” → Site search with TRANSFAC(R) → version 2.0 (Adjusted p-values).

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify the input track in BED format in the field Input Yes track.
The input Yes track contains peaks from your ChIP-seq study. To specify the Yes track, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you select the input track. After having selected the track, press the [Ok] button.

Step 3. Select the profile. This profile will be applied at the first part of the workflow for identification of the enriched motifs. The default profile is vertebrate_non_redundant_minSUM from the most recent TRANSFAC® release available.

Any other TRANSFAC® profile or user-specific profile can be selected. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 4. Specify the sequence source from the drop-down menu. Several human, mouse and rat sequence builds are available in the platform, as shown below. By default, the most recent Ensembl human genome, hg19, is specified. Make sure you selected the sequence source (species and the genome build) that corresponds to your input set, to get correct and meaningful results.

Step 5. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 6. Select a filter for the coefficient of the MEALR method. The default filter is set as >0.125 to have 75% or more of true discovery rate, TDR. For 90% TDR, you can type 0.270 in this field and for 50% TDR - 0.05593. The filtered motifs are included in the output as enriched motifs. At the later step, PWMs corresponding to the enriched motifs are used to make a new profile.

Step 7. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Result folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Step 8. Press the [Run workflow] button.

Wait until the workflow is completed.

The Result folder contains several tables and three tracks; for this example, let’s consider the results folder located under Examples. It is highlighted in blue in the figure below:

The tables Enriched motifs MEALR () and Transcription factors () are opened automatically in the Work Space as soon as the workflow is completed.

The table Enriched motifs MEALR includes enriched motifs in the Yes track versus the No track, filtered by the coefficient as specified.

Please note that by default only the matrices with a Coefficient >0.125 (75% True Discovery Rate) are included in this output table. These motifs can be interpreted as the best discriminating motifs between the Yes and NO sets.

The table Enriched motifs MEALR shown below has been sorted by the values in the Coefficient column. The larger the coefficient, the more important the corresponding motif was for discriminating between Yes and No sequences.

The table Profile is opened automatically and is an input-specific profile, based on the filtered enriched motifs MEALR from the first part of the workflow.

This profile is an intermediate result of the workflow and is used further for Site search on gene set analysis in the second part of the workflow.

Table Transcription factors Ensembl:

This table includes transcription factors (TFs) that are associated with the PWMs listed in the table Site search summary. Each row shows details for one TF, including its Ensembl gene ID (column ID), gene symbol, gene description and biological species of the corresponding TF (columns Gene description, Gene symbol, and Species). The column Site model ID shows the identifier of the PWM associated with this TF, and several further columns repeat information that is also shown in the table Site search summary.

For further visualization of resulting Yes sites opt track please refer to section 6.1.2.1.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

7.2.2.2. Version 1.2 (Classical)

Single interval list

This workflow helps to map putative TFBSs on peaks calculated from your
ChIP-seq data. Site search is done with the help of the TRANSFAC® library of positional weight matrices, PWMs, using the pre-computed profile vertebrate_non_redundant_minSUM.

The few steps to launch the workflow are described in the following.

Step 1. Open workflow input form from the Start page, it will be opened in the main Work Space and looks as it is shown below:

Step 2. Specify the input track in BED format in the field Input Yes track.
The input Yes track contains peaks from your ChIP-seq study. To specify the Yes track, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you select the input track. After having selected the track, press the [Ok] button.

Step 3. Specify the sequence source from the drop-down menu. Several human, mouse and rat sequence builds are available in the platform, as shown below. By default, the most recent Ensembl human genome, hg19, is specified. Make sure you selected the sequence source (the genome build) that corresponds to your input set, to get correct and meaningful results.

Step 4. Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 5. Specify No track in BED format in the field Input No track. Upon clicking on this field, a supplementary window will open, where you can select the No track from your project tree, or use one of our default No tracks for human, mouse or rat, respectively.

Step 6. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Results folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Step 7. Press the [Run workflow] button.

Ready!

Wait until the workflow is completed.

The results folder contains two tables and two tracks; for this example, let’s consider the results folder located under “Examples”. It is highlighted by blue in the figure below:

The tables Site optimization summary () and Transcription factors () are opened automatically in the Work Space as soon as the workflow is completed.

The table Site optimization summary includes the matrices the hits of which are over-represented in the Yes track versus the No track.

Please note that only the matrices with Yes-No ratio higher than 1 are included in this output table. The hits of these matrices can be interpreted as over-represented in the Yes set versus No set.

The table Site optimization summary shown below has been sorted by the values in the Yes-No ratio column.

Each row summarizes the information for one PWM. For each selected matrix, the columns Yes density per 1000bp and No density per 1000bp show the number of matches normalized per 1000 bp length for the sequences in the input Yes set and input No set, respectively. The Column Yes-No ratio is the ratio of the first two columns. Only matrices with a Yes-No ratio higher than 1 are included in the summary table. The higher the Yes-No ratio, the higher is the enrichment of matches for the respective matrix in the Yes set. The matrix cutoff values as they are calculated by the program at the optimization step are shown in the column Model cutoff, and the last column shows the P-value of the corresponding event.

Table Transcription factors:

This table includes transcription factors (TFs) that are associated with the PWMs that are listed in the table Site optimization summary, and each row shows details for one TF, including its Ensembl gene ID (column ID), gene symbol, gene description and biological species of the corresponding TF (columns Gene description, Gene symbol, and Species). The column Site model ID shows the identifier of the PWM associated with this TF, and several further columns repeat information that is also shown in the table Site optimization summary.

Tracks “Yes sites opt” and “No sites opt” ().

Each row presents details for each individual match for every PWM. Columns Sequence (chromosome) name, From, To, Length and Strand show the genomic location of the match including chromosome number, start and end positions, strand and length of the match, respectively. The column Type contains information about the type of the elements; in this case all matches are considered as “TF binding site”. Further columns keep information about PWM producing each match (column Property:matrix) as well as a score of the core (column Property:coreScore) and a score for the whole matrix (column Property:score). The column Property: siteModel contains an identifier for the site model, which is the matrix together with the cutoff applied (for details about these scores, please see Kel et al., Nucleic Acids Res. 31:3576-3579, 2003).

Tip. Further visualization of track files in the genome browser:
Having tracks “Yes sites opt” and “No sites opt” opened in the Work Space, the menu button can be applied to get a visualization. First, a supplementary window is opened where you can select one chromosome and press [Ok], as shown below.

In the second pop-up window, you can select tracks that can be visualized together with your track, e.g. “Yes sites opt” (see above), and press [Ok].

The resulting visualization, after applying the “zoom in” button , looks like it is shown below. Matches for different matrices are shown in colors, and the color schema can be customized.

Such a view may help to visually co-localize information on different tracks, e.g. putative TFBS with variations, repeats and genes. In the figure above, the cursor shows position 29444, and two variations are located at this position. You can immediately recognize that these variations are located within particular putative binding sites in the intron region of the WASH7P gene.

The same information is available not just as a picture, but also as a table under the tab “Sites” (shown below). For each element information is shown on chromosome, positions, length, strand, type of the track, and name of the element.

This table can be exported as a track, in several different formats including intervals, bed, wig, gff, gtf and more.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

Multiple interval sets

This workflow is designed to search for TFBSs in DNA sequences identified by the ChIP-seq approach, for multiple datasets.

In the field Input Yes tracks, several different tracks can be simultaneously submitted. The same background dataset, Input No track, is used for comparison with each of the submitted Yes tracks. The default No track corresponds to far upstream regions of the house keeping genes, where no functional TFBSs are expected.

The steps of this workflow for a single input Yes track are described in the previous section. In this workflow, the same steps are performed next time for the 2nd Yes track, and so on iteratively for each of the input Yes tracks.

This workflow helps to save time and efforts, especially when you have several sets of ChIP-seq data, e.g. the peaks for a number of different TFs.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

7.2.3. Search for composite modules with TRANSFAC®

This workflow finds pairs of TFBSs that discriminate between two tracks, the Yes and the No tracks. As the Yes track, the ChIP-seq peaks identified as binding profiles for particular transcription factors can be considered.

The ChIP-seq experimental technology is widely applied to a variety of biological problems, in particular to study genome-wide histone modification profiles, e.g. histone methylation and histone acetylation profiles. Correspondingly, the same workflow in the platform can be used to analyze histone modification profiles as well. The example of such an application to histone acetylation peaks in SAHA-treated human vascular endothelial cells is described in Section 6.1.3. Please refer to this section for details how to launch the workflow.

Here, let’s consider the results of the workflow application to find composite modules in the ChIP-seq peaks identified for in-vivo-bound fragments of transcription factor E2F1 in HeLa cells, published in Gene Expression Omnibus, GSM558469.

Input Yes track. The original track of genome-wide E2F1 binding fragments was filtered by the length shorter than 600 bp, which resulted in 249 fragments. This track of 249 fragments is used as the input Yes track. It can be found in the Examples folder under:

data/Examples/E2F1 binding regions in HeLa cells, ChIP-Seq/Data/GSM558469_E2F1_hg19 filtered exp1000 dist1000 L<600

Input No track. A track of the far upstream fragments of the human housekeeping genes located on chromosome 1 is taken as the No track. It can be found in the Examples folder under:

data/Examples/E2F1 binding regions in HeLa cells, ChIP-Seq/Data/ Housekeeping genes (Human) track -100000 to -98000, chr 1

The workflow input form is completed and the run is in progress:

The resulting folder can be found under:

data/Examples/E2F1 binding regions in HeLa cells, ChIP-Seq/Data/GSM558469_E2F1_hg19 filtered exp1000 dist1000 L<600 (CMA on track, TRANSFAC) Pairs-8 Iterations-300 v2

The table Site optimization summary () contains those site models, here TRANSFAC® matrices, that are over-represented in the Yes track as compared to the No track.

Each row of the table represents the result for one PWM from the input profile. Only those PWMs with Yes-No ratio >1 are included in the output. Upon sorting by the Yes-No ratio, matrices for E2F factors are among top 20 lines. Please note that the p-values of E2F matrices are extremely low, which demonstrates highest statistical significance of the results.

The Modules folder (). The composite module found contains two pairs, and we can see by exactly which site models (matrices) these pairs are formed as well as the statistical parameters of the overall model.

Both pairs contain matrices for E2F factors.

For more details on the individual output tables and tracks as well as for visualization of the identified composite modules in the genome browser please refer to Section 20.1.5.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

7.2.4. Search for discriminative sites with TRANSFAC® (MEALR)

Please, refer to Section 6.1.4 for detailed explanation how to use this function.

7.2.5. Search for enriched TF sites from tissue specific promoter tracks

This workflow searches for enriched transcription factor binding sites (TFBSs) in input tracks versus a random created track. The input track is converted to a gene set, which is used to extract promoter regions by mapping it against the TSS locations defined in CAGE data in the Fantom5 (Nature 507:462–470) database (see also 19.10). The over-represented sites identified with the MEALR method are converted into a profile, which is used for a second round of site search, and ends up with the identification of transcription factors. To launch the workflow, open the workflow input form from the Start page:

Step 1: To specify the Input Yes track, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you select the input track. After having selected the track, press the [Ok] button.

For this example, all further steps are demonstrated with the following input set:

http://platform.genexplain.com:8080/bioumlweb/#de=data/Examples/E2F1%20binding%20regions%20in%20HeLa%20cells%2C%20ChIP-Seq/Data/GSM558469_E2F1_hg19%20filtered%20exp1000%20dist1000

Step 2: Specify the biological species of the input set in the field Species by selecting the desired species from the drop-down menu.

Step 3: Specify the Sequence source from the drop-down menu. Several human, mouse and rat sequence builds are available in the platform, as shown below. By default, the most recent Ensembl human genome, hg38, is specified. Make sure you select the sequence source (the genome build) that corresponds to your input set in order to get correct and meaningful results.

Step 4: Specify the path of the CAGE_db. Select either the TSS folder from Fantom5-Cell database or the TSS folder from Fantom5-Tissue database. You can drag & drop it from the Databases within the tree area, as shown below.

Step 5: Tissue condition, select the cells/tissues for which you want to create the promoter track from the drop-down menu.

Step 6: The TSS selection should be performed if there are multiple transcription start sites. By default, the most active site is considered as TSS. You can select between most active, 5’ active, 3’ active and all (see below) from the drop-down menu.

Step 7: Define a TRANSFAC® profile. The default profile is vertebrate_non_redundant_minSUM. Any other TRANSFAC® profile or user-specific profile can be chosen. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 8: Select a filter for the coefficient of the MEALR method. The default filter is set as >0.125 to have 75% or more of true discovery rate, TDR. For a 90% TDR, you can type 0.270 in this field and for 50% TDR - 0.05593. The filtered motifs are included in the output as enriched motifs. At the later step, PWMs corresponding to the enriched motifs are used to create a new profile.

Step 9: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Results folder, and a new window will open, where you can select the location of the results folder and define its name.

Step 10: Press the [Run workflow] button. Wait until the workflow is completed, and take a look at the results.

Interpretation of results

The result folder contains several files and one profile (collection of matrices):

Tracks

The Random No track includes 1000 sequences from the same sequence source as the input track. The random no track and the input track are converted to genes, which are used as input for the tissue-specific promoter track from selected database, tissue and TSS. The resulting tracks are Tissue_track (from Yes track) and Random_tissue_track (from random track).

Enriched motifs

The list of motifs, which were found during the first part of the workflow, and were filtered by the coefficient >0.05, can be found in the table Enriched_motifs MEALR (). It contains those site models, here TRANSFAC® matrices, which are enriched in the Tissue_track in comparison with the Random_tissue_track. The example has x detected motifs with coefficient >0.05. The Profile contains the matrix collection of converted and filtered site models. The table Transcription factors Ensembl genes includes the corresponding x TFs from the second Site search summary and is shown below:

Note. This workflow is available together with valid TRANSFAC® and TRANSPATH® licenses. Please feel free to ask for details (info@genexplain.com).

7.2.6. Discover de-novo motifs using ChIPHorde and DiChIPHorde

ChIPHorde uses the fast heuristic of ChIPMunk, which is based on a greedy approach accompanied by bootstrapping, to search for multiple significant motifs in a given dataset using two independent filtering strategies. DiChIPHorde searches for di-nucleotide motifs that capture dependencies between neighboring motif positions.

The image below shows the ChIPHorde interface. The input mask of DiChIPHorde features only slight differences as pointed out in the section describing input parameters.

The parameters are described in the following. For further details, please also refer to the ChIPMunk manual.

Input sequences: Track with input reads

Start length: Start length of the matrix

Stop length: Stop length of the matrix

Motifs count limit: Maximum number of motifs to discover

Filtering mode: Whether to mask polyN ("Mask") or to drop entire sequence ("Filter")

Number of threads (expert): Number of concurrent threads when processing

Step limit (expert): The number of bootstrapping runs. With a large data set or if unsure which computational time to expect, try a small number, e.g. 10.

Try limit (expert): This is a number of general optimization runs. For a random seeding this is equal to the number of seeds. As with “step limit”, apply the parameter with the size of your data set in mind. It is always advisable to test with a small number first.

Local background (expert): *DiChIPHorde only* If checked, local background estimation is used. Otherwise uniform background estimation is used

GC percent (expert): *ChIPHorde only* Relative GC content to be used as nucleotide background (0..1). Set to -1 to use the observed GC content.

ZOOPS factor (expert): Sets the preference for ZOOPS (Zero-or-one[-motif occurrences]-per-sequence) versus OOPS (Only-one-per-sequence) mode.

Motif shape (expert): The type of motif shape prior to use. The parameter corresponds a prior on the number of informative regions within the motif.

Use peak profiles (expert): Whether to apply peak profiles (if available).

Output matrix library: Path to the matrix library to be extended or created.

Matrix name prefix: Prefix for matrix names. It will be appended with a number.

7.3. Further workflows in this area

For the other workflows that you can find in the area ChIP-seq, please refer to the following Sections:

Load data

See Chapter 3

Discover functional enrichment of target genes

See Section 10.3

Analyze networks of target genes

See Section 5.1

Sequence analysis

8.1. Analyze any DNA sequence

8.1.1. Search for TF binding sites

8.1.1.1 Search for TF binding sites with TRANSFAC®

This workflow is designed to search for putative transcription factor binding sites, TFBS, in any input DNA sequence in EMBL, Fasta or Genbank formats. Using this workflow you can analyze DNA sequences of any species and of any genomic regions.

The steps to launch the workflow are as follows.

Step 1. Open the workflow input form from the Start page, it will open in the main work area and looks as it is shown below:

Step 2. Specify an input file in the field Input sequence set. The input sequence set can be any sequence file having an EMBL, Genbank, or Fasta file extension. Sample EMBL, FASTA and Genbank sequences are as shown below:

Sample EMBL file:

Sample Fasta file:

Sample Genbank file:

After loading into the platform, the sequences in these formats are shown in the tree area with the icon ().

In this example, sample sequences of Arabidopsis thaliana are used, which were downloaded from the TAIR database in FASTA format (http://www.arabidopsis.org/tools/bulk/sequences/index.jsp). The example input sequence set contains a set of ten promoter sequences 500 bp upstream of the TSS, located on chromosome 1.

Step3. Select the TRANSFAC® profile from the available profiles. The default profile is vertebrate_non_redundant_minSUM. In this example we use the profile called Plants. It can be found here:

http://platform.genexplain.com/bioumlweb/#de=databases/TRANSFAC(R)%202014.4/Data/profiles/plants

Step 4. Specify the result folder location and name in your Project area. Then press the button [Run workflow]. Wait till the workflow is completed.

Results

The results folder consists of a summary table and a track with sites as shown below:

Input sequence Sites: This track () shows TFBSs that are found in the input sequences. As the input sequence set in this example is called Arabidopsis_Chromosome 1, the resulting track is called Arabidopsis_Chromosome 1 Sites. When opened as a table, this track looks like:

Each row corresponds to one resulting TFBS and includes sequence names, site positions (the columns From and To), site Length and Strand, score calculated by the algorithm and a site model (here, TRANSFAC® matrix). This table can be exported as a track in several different formats including intervals, bed, wig and more. DNA sequences can be exported in multi-FASTA format.

The same track, when opened in the genome browser, looks as shown below:

In the field Sequence (chromosome) you can find a dropdown menu, highlighted by the red oval. This feature helps to easily switch between visualizations of the sequences in the input set. In this particular example the input sequence set comprises ten individual promoter sequences, and each individual promoter can be visualized in the genome browser.

The table Summary () gives the site density per thousand bp for each matrix in the input sequence. When opened in the work space looks as shown below:

Each row summarizes the information for one site model (PWM, matrix).

For each row, the column Site density per 1000bp shows the number of matches normalized per 1000 bp length for the sequences in the input set.

TFBSs can be further visualized in the input sequences. For this, having the Summary table opened, select one or several rows, and then click the report on selected matrices button () on the control panel. In this example, all matrices with a site density <5 were selected. The visualization results are shown below:

There are ten rows corresponding to the individual sequences in the input set. The column Sites view schematically represents the sequence length with mapped TFBSs. Matches for different matrices are shown in different colors. You can select individual matches by mouse click and get additional information in the Info box.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

8.1.1.2. Search for TF binding sites with GTRD

This workflow is similar to the one described above. The difference is in the default profile applied for the TFBS search; in this workflow it is the profile from the GTRD database called moderate threshold. It can be found under the tab Databases, in the folder GTRD/Data/profiles/

Correspondingly, the site search results from these two workflows are different.

8.1.2. Analyze any DNA sequence for site enrichment

8.1.2.1. Analyze any DNA sequence for site enrichment with TRANSFAC®

This workflow is designed to search for enriched transcription factor binding sites, TFBSs, in any input DNA sequence as compared to a background DNA sequence. The central part of this workflow is performed by two individual methods, Site search on track (), and Site search result optimization (). Both individual methods can be found under the tab Analyses in the folder Methods/Site analysis/.

With this workflow you can analyze sequences of any species and any genomic region.

The few steps to launch the workflow are as follows.

Step 1. Open the workflow input form from the Start page, it will be opened in the main Work Space and looks as it is shown below:

Step 2. Specify the input Yes and No sequence sets. The Yes and No sequence sets can be in EMBL, FASTA or GenBank format. After loading into the platform, these sequences are shown in the tree area with the icon ().

To specify the Input sequence sets, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you can select the input sequence.

In this example, the sample sequences of Arabidopsis thaliana are used, which were downloaded from TAIR database http://www.arabidopsis.org/tools/bulk/sequences/index.jsp.

Here, the Yes sequence collection contains a set of four promoter sequences 1000 bp upstream of TSS. These genes play an important role in auxin biosynthesis. The No sequence collection contains a set of four promoter sequences 1000 bp upstream of TSS. These genes are involved in different functions.

Step3. Select the TRANSFAC® profile from the available profiles. The default profile is vertebrate_non_redundant_minSUM. In this example we use the profile called Plants. It can be found here:

http://platform.genexplain.com/bioumlweb/#de=databases/TRANSFAC(R)%202014.4/Data/profiles/plants

Step 4. Specify the result folder location and name in your Project area. Then press the button [Run workflow]. Wait till the workflow is completed.

Results

The results folder consists of several tables and tracks as shown below:

The table Summary () gives the TFBSs enriched in the Yes set as compared with the No set. It looks as shown below:

Each row summarizes the information for one site model (PWM, matrix).

For each row, the columns Yes density per 1000bp and No density per 1000bp show the number of matches normalized per 1000 bp length for the sequences in the input Yes set and input No set, respectively. The Column Yes-No ratio is the ratio of the first two columns. The higher the Yes-No ratio, the higher is the enrichment of matches for the respective matrix in the Yes set. The matrix cutoff values as they are calculated by the program at the optimization step are shown in the column Model cutoff, and the last column shows the p-value of the corresponding event.

TFBSs can be further visualized in the Yes sequences. For this, having the Summary table opened, select one or several rows, and then click the report on selected matrices button () on the control panel.

In this example, all matrices having a Yes-No ratio>3 were selected. The visualization results are shown below:

There are four rows corresponding to the individual sequences in the input Yes set. The column Sites view schematically represents the sequence length with mapped TFBSs. Matches for different matrices are shown in different colors. You can select individual matches by mouse click and get additional information in the Info box.

Yes (No) sites optimized tracks. These optimized tracks () present those TFBSs that are over-represented in the Yes sequences versus the No sequences. Scores of the putative sites are optimized by the algorithm. As the Yes set in this example is called Auxin biosynthesis, the resulting track is called Auxin biosynthesis sites optimized. When opened as a table, this track looks like:

Each row corresponds to one resulting TFBS, and includes its position in the Yes sequences (the columns From and To), length and strand, as well as a score calculated by the algorithm and a site model (matrix). This table can be exported as a track, in several different formats including intervals, bed, wig and more. DNA sequences can be exported in multi-FASTA format.

The same track, when opened in the genome browser, looks as shown below:

In the field Sequence (chromosome) you can find a drop down menu, highlighted by the red oval. This feature helps to easily switch visualization between the sequences in the input set. In this particular example the Yes sequence set comprises four individual promoter sequences, and each individual promoter can be visualized in the genome browser.

The tables Transcription factors Ensembl () and Transcription factors Entrez () aim at showing transcription factors linked to the identified site models (matrices). These are potential candidate regulators of genes in the input Yes set. They are supposed to regulate transcription of Yes-genes via the identified enriched TFBSs.

Currently this feature is available for human, mouse and rat. For all other species, this table will be empty, as in this example for Arabidopsis sequences.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

8.1.2.2. Analyze any DNA sequence for site enrichment (GTRD)

This workflow is similar to the one described above. The difference is in the default profile applied for the TFBS search; in this workflow it is the profile from the GTRD database called moderate threshold. It can be found under the tab Databases, in the folder GTRD/Data/profiles/

Correspondingly, the enriched motifs resulting from these two workflows are different.

8.2. Further workflows in this area

For the other workflows that you can find in the area Sequence analysis, please refer to the following Sections:

Load data

See Chapter 3

Discover de-novo motifs using ChIPHorde and DiChIPHorde

See Section 7.2.6

miRNA

When you open this Area, a list of workflows optimized for working with microRNA data will show up, which looks as follows:

For detailed explanation of their function and how to operate them, please refer to the following chapters:

Load gene or protein list

See Chapter 3

Detect differentially expressed miRNA genes

See Chapter 10.2

9.1. Prediction of miRNA binding sites

MicroRNAs, or miRNAs, post-transcriptionally affect (mostly: repress) the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the vast majority of messenger RNAs (mRNAs). This workflow can help to predict miRNA binding sites. Starting from a gene list, first a collection of 3’ untranslated regions (3’ UTRs) is created. This collection is used to map against a miRmap library, which derived from the mirBase database (release 20; http://www.mirbase.org). The last step of the workflow is based on the miRmap method (analyses/Galaxy/microRNA/mirmap; http://mirmap.ezlab.org), published by Vejnar & Zdobnov, Nucleic Acids Res. 40:11673-11683, 2012. The result of the workflow is a table with all predicted miRNA binding sites and a track for visualization in the created sequence collection. The workflow can be found on the Start page via the miRNA button.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input gene table. The input gene sets might be lists of differentially regulated genes or any gene or protein list of interest. You can drag them from your project within the tree area and drop them in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated on the example file, which can be found in the geneXplain platform under:

data/Examples/miRNA binding site prediction (miRNA-155 target genes)/Data/hsa-miR-155-5p published target genes

This table contains four genes that are already published targets for one particular miRNA, hsa-miR-155-5p, for which several target genes have been published as well; we have derived this information from the TRANSFAC® database.

Step 3. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 4. In the field Transcript region, choose the gene region from the drop-down menu. Here, the 3’ UTR is selected as default with a fixed length of 300bp. The CDS information is ignored.

Step 5. Selection of miRBASE collection. By clicking in the this field, six different miRBase collections from release 20 are available for Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio and Caenorhabditis elegans.

The miRBase library can be opened as a table as shown below.

The miRBase library table has three columns for each miRNA. Identifiers in the column ID are hyperlinked to miRBase (http://www.mirbase.org/). Two other columns are the miRNA accession numbers and the mature sequence, which is used for binding site identification.

You can use the whole library or you can create a subset of several or even just one miRNA of interest to focus on predicting binding sites for this particular miRNA.

In this example, let’s cerate a subset containing just one miRNA, hsa-miR-155-5p, for which several target genes have been already published. To create a subset from the table, apply Filter tab in the operations field. The resulting customized miRBase contains just one row.

Step 6. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Result folder, and a new window will be opened, where you can select the location of the result folder and define its name.

Step 7. Press the [Run workflow] button.

When the workflow is completed, the result folder is opened by default.

Let’s consider the results.

data/Examples/miRNA binding site prediction (miRNA-155 target genes)/Data/hsa-miR-155-5p published target genes (miRNA binding sites)

The result folder contains the following tables: Ensembl transcript (),Site output () with all predicted miRNA binding sites, Site output track () for visualization of predicted miRNA sites, Summary output () and Transcript region track .

The table Ensembl transcripts corresponds to the input gene table converted to the Ensembl transcript IDs (first column ID). The other columns of this table are the same as they are in the input table. In this example, 4 Ensembl genes are converted into 26 Ensembl transcripts.

These Ensembl transcript IDs are needed to construct the Transcript region track, in this workflow especially containing 3’ UTRs of genes given in the input table. The workflow takes 300 bp regions as 3’ UTRs.

The Site output table contains all identified miRNA binding sites; each row in this table corresponds to one identified miRNA binding site, and each site/row has an ID assigned. The column Sequence corresponds to the 300 bp 3’ UTR of the indicated Ensembl transcript, where the search for miRNA sites has been done. The column miRNA contains the name of miRNA binding to this site, in this example hsa-miR-155-5p. The columns Start and End are positions within the input sequence and the column Binding site contains the sequence of the site.

The Site output track enables the visualization of the predicted sites in the 3’ UTRs of the input gene list. With a double-click on () in the results folder, the dialog box Configure genome for track is opened.

Within this dialog box, first set the field Sequence source to Custom…

and [Save] it. The dialog box is automatically adjusted, and the field

Sequence collection becomes available.

You can drag and drop the sequence collection Transcript region track from the workflow results folder and [Save] it. The visualization is opened automatically in the work area.

You can modify visualization settings by opening the tab Tracks in the Operations Field, highlighted by the red boxes on the screenshot above, and press the button [Options]. The dialog box Site output track will be opened. You can modify different settings according to your preferences and [Save] them. Here, the settings in the highlighted check-boxes were changed as shown below.

These changes resulted in the following visualization.

The Summary output table presents those transcripts where miRNA binding sites are identified, in this example four transcripts.

Each row corresponds to one transcript. The column Sequence contains the titles of the Ensembl transcripts, and the column miRNA presents name of the miRNA. The number of binding sites for a particular miRNA in each transcript is shown in the column #Sites.

Microarrays

10.1. Normalize data

10.1.1. Experiment vs. control or multiple conditions

If your expression data haven’t been normalized yet, as we assume in this example, you have to go now to the second group of options, “Normalize data”. Make your choice according to the experimental platform you have used (Affymetrix, Agilent or Illumina). The next form will ask you to define the data file(s). For this, you have two options:

When you click in either field, a window with the title “Select data element” opens allowing you to select a file or, more likely at this step, a number of files by mouse click or by typing their names. Selection by mouse click works as usual for a range of files (keep the Shift key pressed when selecting the last file of the range), or for a number of distinct files (keep the Ctrl key pressed when selecting the second and further files from the list).

Make sure that all hybridizations (i.e., all CEL files) of your experiment are included into one normalization procedure. It should comprise all CEL files, including all multiple repetitions, of all conditions to be compared with each other at a later step, i.e. all tests and controls, at least those that you want to compare later on.

If your readings were from a dual-channel experiment, please tick the checkbox (Agilent only).

In the field Output name, you find a suggested name for the output file. The default name is “Normalized (<Normalization_method>)”, which you can edit (just click into this field and change the default name). An accordingly named file with the icon will appear in the Tree Area after the procedure run successfully.

You may also have noticed that some further information about the analyses to be employed is displayed in both the Info Box as well as in the Operations Field (“My description” tab). Sometimes, they are identical by default, but in the latter field, you can edit the contents and add your own comments.

Now launch the normalization routine by pressing the [Run] button. The program will now normalize all your data across all experiments done, i.e. through all CEL files selected. The results will be stored in two different tables, one with the experimental, the other with the control values.

To have a closer look into the full content of one of the results tables, just click with the right mouse button onto the respective file name in the Tree Area, and choose “Open table” from the little menu that appears, or double-click on the file name. The table will open under a new tab in the Work Space. It should look like this, with the probeset IDs in the first and the normalized expression values from the different CEL files in the following columns, each hybridization being represented in one column (picture below).

Important note. In the geneXplain platform, the probeset IDs are mapped to genes based on the Ensembl database. If some of the probeset IDs are not annotated in Ensembl, they cannot be mapped to genes and cannot be used for further analysis. That means, you can normalize data and calculate differentially expressed probes. These steps can be done on the probeset ID level, before conversion to genes. The step of converting probeset IDs into genes is depending on the annotation provided by the Ensembl database.

10.1.2. Heatmap

This tool creates a heatmap for the numerical data matrix provided with the input data table. The heatmap is limited to input tables with at most 5000 input rows. It is a graphical representation of data where the individual values contained in a matrix are represented as colors. The output folder contains a TIFF image of the heatmap as well as the ordered lists of row ids (e.g. RNA or gene ids) and column ids. The output tables can be used to extract subsets of correlated rows or columns revealed by the hierarchical clustering and/or the heatmap presentation.

An example can be found here: data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Normalized (RMA) subset_heatmap

Step 1: Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2: Specify the Input table with e.g. normalized data for different experimental conditions. To specify the input table, you can drag & drop it from your project within the tree area.

Step 3: Specify the Transformation to be applied to data values from the drop-down menu. Possible transformations are Log or Rank; the default is None.

Step 4: Select Width and Height as well the Resolution of the output image.

Step 5: Specify layout heights (Lhei). This should be a list of 2 (when no column groups are specified) or 3 (with column groups) float values separated by comma that adjust the heights of the layout parts. Please refer to the documentation of R's heatmap.2 {gplots} tool for details.

Step 6: Please check Row dendrogram and/or Column dendrogram if you want to have a raw dendogram and/or a dendrogram with column in the output image.

Step 7: Specify the conditions/groups names for up to five groups (expert level).

Step 8: Specify the conditions/groups. They are shown as columns of the input table. You can select the column names for each condition/group via the drop-down menu.

Step 9: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output folder, and a new window will open, where you can select the location of the results folder and define its name.

Start the method by pressing the [Run workflow] button.

Output are two tables (heatmap_columns and heatmap_genes) and one image file in .tiff format. This image file needs to be downloaded and can be used in any graphical program/presentation.

10.1.3. Normalization quality plots

This tool can be applied plot densities of columns of a data table. As its name implies the intended use case is to inspect the quality of results of normalization as conducted in microarray experiments.

Example outputs, a box plot and a density plot, are shown at the end of this section. Colors were automatically assigned to selected columns.

The input parameters are described in the following.

Input table: This table contains the numerical columns to analyze.

Column subset: Here you can select the set of columns to show in plots.

Input log-base: Densities and box plots will be computed for data on the log₂ scale. Here you can specify the actual scale of the input data. If the log-base is log₂, the tool will use the data values as is.

Output folder: The output folder will contain a density plot and a box plot for the specified columns.

10.1.4. Principal Component Analysis (PCA)

PCA is a statistical method that transforms data in a way, so that a maximum amount of variance within the data can be expressed in fewer or, at most, as many dimensions as the original data. The new dimensions onto which data are projected are the principal components. They capture the original variance in decreasing order, so that the first principal component presents most of the variance. PCA is often used reduce the complexity of (to compress) or to identify groups in high-dimensional data.

This tool applies PCA to a table of numerical data, e.g. to normalized microarray measurements. For visualization purposes one can assign columns to one of up to five groups, which will be differentially colored in the generated output (scatter plot).

The output is stored in a specified folder and consists of three files. The PCA Scatter plot shows the items of specified groups at their transformed coordinates according to the first two principal components. The entire set of coordinates is available in PCA Transformed coordinates. Finally, the table PCA Component importance provides information about the relative importance of each principal component with respect to the proportion of explained variance.

The input parameters for PCA are described in the following.

Input table: This table contains the numerical columns to analyze.

1-5. Condition / group name: One can specify up to five groups of columns. These fields contain the names that will be shown in outputs. Please note that unnamed groups are not considered, a name is not assigned automatically.

1-5. Columns: These fields contain the selected columns. Please note that column selections are not considered without a corresponding name. Columns can only be specified once.

Output folder: The output folder will contain the described output files.

10.2. Detect differentially expressed genes

After the microarray results are normalized, the next step is to compute differentially expressed genes (DEG).There are four different statistical methods provided for DEG calculation in the platform: T-test, hypergeometric analysis, Limma, and EBarrays. For DEG calculation by T-test and hypergeometric analysis, there are predefined workflows, which take as input two tables with the normalized data, for two different conditions, referred to as Experiment normalized and Control normalized. The methods Limma and EBarrays require one input table with all the conditions, and you can specify up to five different conditions for one run of each of these two methods.

If you applied the normalization method “Experiment vs. control”, you can detect DEGs applying T-test and/or hypergeometric analysis to the workflows, highlighted in green in the picture above. If you applied the normalization method “Multiple conditions”, you can detect DEGs with Limma and/or EBarrays, highlighted in red.

In this chapter, the predefined workflows for DEG calculation with T-test and with hypergeometric analysis are described in detail. For a description of Limma and EBarray please refer to the sections 4.2.1 and 4.2.2, respectively.

10.2.1. Detect differentially expressed genes with T-test

This workflow is designed to find the set of up-regulated and down-regulated genes applying Student’s T-test. There are three workflows designed for different experimental platforms (Affymetrix, Agilent and Illumina).

In the first step p-values for normalized files are calculated for all probes using the “Up and Down Identification” analysis. This analysis applies Student’s T-test for p-value calculation, thus the number of data points should be at least three for each experiment and control.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the tables with normalized data in the fields Experiment normalized and Control normalized. You can drag it from your project within the tree area and drop it in the pink box of the fields. Alternatively, you may click on the pink field (select element) and a new window will be opened, where you can select the input tables.

The further steps of the workflow are demonstrated by means of the tables in one of the pre-prepared examples. You can find these tables in the Examples folder, under data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma

Step 3. Specify the biological species of the input sets in the field Species by selecting the required biological species from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field (select element) in the field Results folder, and a new window will be opened, where you can select the location of the results folder and define its name.

After entering all input fields press [Run workflow] and wait till the workflow is completed.

The output is a folder with several files as shown below:

The table Genes, fold change and P-value, non-filtered. This table contains all genes with LogFoldChange and p-value calculated; each row corresponds to one gene.

The columns ID, Gene description and Gene symbol present Entrez identifiers for the genes, a full name for each gene, and a standard gene symbol, respectively. The column Species shows the corresponding taxonomic species. The column AffymetrixID contains the probe set IDs corresponding to each gene, and you can see sometimes more than one Affymetrix probe corresponding to one gene. The column LogFoldChange shows the base 2 logarithm of the ratio between expression value in experiment vs. control. The column –log(P-value) shows the negative base 10 logarithm of the p-value.

Please note that the column –log(P-value), according to a widely accepted convention, has algebraic signs according to being up- (positive values) or down-regulated (negative values).

In the course of worklow progression, this table has been filtered by several conditions in parallel to identify up-regulated, down-regulated, and non-changed Affymetrix probeset IDs and genes.

The filtering criteria used are:

For up-regulated probes: LogFoldChange>0.5 and -log_P_value_>3

For down- regulated probes: LogFoldChange<-0.5 and -log_P_value_<-3

For non-changed genes : LogFoldChange<0.002 and LogFoldChange>-0.002

The table Upregulated Ensembl genes. You can find the number of the resulting up-regulated genes written on top of each output table (highlighted by the red circle):

The table Downregulated Ensembl genes. The structure and the meaning of the columns in the tables are the same as in the Upregulated Ensembl genes table.

The table Non-changed Ensembl genes.

In this example the number of up-regulated, down-regulated and non-changed genes are 503, 241, and 99, respectively.

These individual output files can be used further as input for running other workflows as described in the following sections.

The plot () contains a histogram of the log fold change distribution for all genes:

The Report. The workflow summarizes all results and automatically produces a report. In addition you can have a look at the list of both up-regulated and down-regulated genes.

This report can be exported in html format.

10.2.2. Detect differentially expressed genes by hypergeometric analysis

This workflow is very similar to the one described above in section 9.2.1. The principal difference is in the statistical method for calculation of the p-value. In this workflow, the p-value is calculated by hypergeometric analysis (Y.V.Kondrakhin, R.N.Sharipov, A.E.Kel, F.A.Kolpakov. (2008) Identification of Differentially Expressed Genes by Meta-Analysis of Microarray Data on Breast Cancer, In Silico Biology, 8: 383-411).

Tip If you have just two or even one data point in each experiment and control (e.g. one CEL file in experiment and one CEL file in control), you can apply hypergeometric analysis to calculate DEGs. In contrast to the T-test which requires at least three data points, hypergeometric analysis can make calculations for two and even one data point in each normalized experiment and normalized control files. This allows to calculate DEGs to compare, for instance, one patient data set with one healthy data set.

The workflow input form looks as shown below:

The output folder and the structure of the individual tables, as well as the report, are similar to those described in 9.2.1.

10.2.3. Detect differentially expressed genes with Limma

This workflow is designed to find sets of up-regulated and down-regulated genes starting with the normalized table of your expression data. Please refer to section 4.2.1 for details on this particular analysis method. This workflow is designed for different experimental platforms (Affymetrix, Agilent and Illumina).

In the first step this workflow computes the differential expression between up to five conditions / groups. Each group corresponds to one experimental condition (time point, treatment, cell type, etc.) or control. You can specify 2 to 5 conditions. An input table is a data table that contains several columns with normalized measurement values, e.g. from a normalized microarray experiment. All possible contrasts between groups are considered and their output is stored in a common folder. Conditions are compared in the specified order from first to fifth; e.g. for the given conditions named 1, 2 and 3, the output will contain the contrasts “Condition 1 versus Condition 2”, Condition 1 versus Condition 3” and “Condition 2 versus Condition 3”. ”. The workflow can be found on the Start page, under the button Microarrays, under the section “Detect differentially expressed genes”.

Step1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the table with normalized data in the field Input table. You can drag it from your project within the tree area and drop it in the pink box of the fields. Alternatively, you may click on the pink field (select element) and a new window will be opened, where you can select the input table.

The further steps of the workflow are demonstrated by means of the tables in one of the pre-prepared examples. You can find these tables in the Examples folder, under

data/Examples/Cytokine-triggered gene expression in cell cycle stages, GSE52465, Agilent-014850 microarray/Data/Agilent normalized DEGs with limma

Step 3. Specify the biological species of the input table in the field Species by selecting it from the drop-down menu.

Step 4. Specify the conditions / groups. They are shown as columns of the input table. You can select the column names for each condition/group via drop-down menu.

Step 5. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field (select element) in the field Results folder, and a new window will be opened, where you can select the location of the results folder and define its name.

After entering all input fields press [Run workflow] and wait till the workflow is completed. The output is a folder with 10 result folders (e.g. Condition_1 vs. Condition_2 for DEG calculation), a folder for each individual comparison (Output limma) and one Output plots folder, as shown below:

The Normalized density boxplot and the Normalized density plot in the folder Output plots show a quality control of the input normalized data table. Please refer to section 10.1.3 for details on this particular analysis method.

The tables in the folder Output limma are the output tables from the limma method, sorted via adjusted p-values.

The 10 output folders for each comparison e.g. Condition_1 vs. Condition_5 contain the results of the identified up-, down- and non-regulated Ensembl genes.

The table UpDown reg genes Ensembl in the Folder Condition_1 vs. Condition_5 contains all differentially expressed genes filtered by LogFoldChange and p-value for up- and down-regulated genes; each row corresponds to one gene.

The columns ID, Gene description and Gene symbol represent Ensembl identifiers for the genes, a full name for each gene, and a standard gene symbol, respectively. The column Species shows the corresponding taxonomic species. The column AffymetrixID contains the probe set IDs corresponding to each gene, and you can see sometimes more than one Affymetrix probe corresponding to one gene. The column logFC shows the base 2 logarithm of the ratio between expression values in experiment vs. control. The column adj.P.Val shows the adjusted p-value (Benjamini-Hochberg).

In the course of workflow progression, this table has been filtered by several conditions in parallel to identify up-regulated, down-regulated, and non-changed probeset IDs that were then converted into Ensembl gene identifiers.

The filtering criteria used are:

For up-regulated genes: logFC >0.5 and adj.P.Val <0.05

For down- regulated genes: logFC <-0.5 and adj.P.Val <0.05

For non-changed genes :logFC <0.002 and logFC >-0.002

10.2.4. Detect differentially expressed genes with EBarrays

Similarly to the workflow described above, this workflow is designed to find the set of up-regulated and down-regulated genes starting with a normalized table of your expression data, but using a different statistical method, EBarrays. Please refer to section 4.2.2 for details on this particular analysis method. This workflow is designed for different experimental platforms (Affymetrix, Agilent and Illumina).

In the first step the workflow computes the differential expression between up to five conditions/groups. Each group corresponds to one experimental condition (time point, treatment, cell type, etc.) or control. You can specify 2 to 5 conditions. An input table is a data table that contains normalized measurement values, e.g. from a normalized microarray experiment. All possible contrasts between groups are considered and their output is stored in a common folder. Conditions are compared in the specified order from first to fifth. E.g. given conditions named 1, 2 and 3, the output will contain the contrasts “Condition 1 versus Condition 2”, Condition 1 versus Condition 3” and “Condition 2 versus Condition 3”.

The workflow can be found on the Start page, under the button Microarrays, under the section “Detect differentially expressed genes”.

Step1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the table with normalized data in the field Input table. You can drag it from your project within the tree area and drop it in the pink box of the fields. Alternatively, you may click on the pink field (select element) and a new window will be opened, where you can select the input table.

The further steps of the workflow are demonstrated by means of the tables in one of the pre-prepared examples. You can find these tables in the Examples folder, under

data/Examples/Cytokine-triggered gene expression in cell cycle stages, GSE52465, Agilent-014850 microarray/Data/Agilent normalized DEGs with EBarrays

Step 3. Specify the biological species of the input table in the field Species by selecting it from the drop-down menu.

Step 4. Specify the conditions/groups of the input table. You can select the column names for each condition/group via drop-down menu.

Step 5. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field (select element) in the field Results folder, and a new window will be opened, where you can select the location of the results folder and define its name.

After entering all input fields press [Run workflow] and wait till the workflow is completed.

The output is a folder with several result folders and files as shown below:

The Normalized density boxplot and the Normalized density plot in the folder Output plots show a quality control of the input normalized data table. Please refer to section 10.1.3 for details on this particular analysis method.

The table EBarrays result and two plots in the folder Output EBarrays are the output table and plots from the EBarrays method.

The tables for each condition (except control) e.g. Condition_2 downreg Ensembl and Condition_2 upreg Ensembl contain all differentially expressed genes with filtered LogFoldChange and p-value for up- and down-regulated genes; each row corresponds to one gene.

The columns ID, Gene description and Gene symbol present Ensembl identifiers for the genes, a full name for each gene, and a standard gene symbol, respectively. The column Species shows the corresponding taxonomic species. The column Agilent ID contains the probe set IDs corresponding to each gene, and you can see sometimes more than one Affymetrix probe corresponding to one gene. The direction of differential expression can be derived from the fold change column e.g. Condition_2 FC, which contains the log2-fold changes. The EBarrays method estimates a critical posterior probability cutoff for the given FDR level on the basis of the fitted mixture model. Probes / genes exceeding this cutoff in some treatment are indicated by a value of 1 (instead of -1) in the output column named e.g. Condition_2 Sig.

In the course of workflow progression, this table has been filtered by several conditions in parallel to identify up-regulated and down-regulated genes.

The filtering criteria used are:

For up-regulated genes: Condition_x FC >0.5 and Condition_x Sig = 1

For down- regulated genes: Condition_x FC <-0.5 and Condition_x Sig = 1

In the folder Output plots there are two diagnostic plots named EBarrays CCV and EBarrays Marginal fit. These plots enable a judgment about whether the assumptions of the approach hold and how well the fitted model represents the data (please refer to the documentation of the EBarrays Bioconductor package for further details).

Note. In the input field Condition 1 always the control condition should be specified. Each of the input conditions 2, 3, 4, and 5 are compared with condition 1.

10.3. Discover functional enrichment

This set of workflows helps to identify certain functional groups in your input list of genes or proteins, namely those that are particularly affected in a statistically significant manner.

The first approach to do this is the Gene Set Enrichment Analysis (GSEA). Applying this method to genes/protein in focus, the workflows will find out whether any category of Gene Ontology (GO), Reactome pathways, TRANSPATH® pathways or PROTEOME™ disease terms are statistically overrepresented among them, and if so, whether this overrepresentation is valid for the up- or down-regulated genes if expression values are present in your input table.

An alternative approach to GSEA is Functional classification, or mapping to ontologies. As input, you can use a table of genes/proteins. The difference of this option to GSEA is that no enrichment of the categories is calculated, but that all genes in the list are mapped to GO categories or other ontologies. For example, you can use tables with up-regulated or down-regulated genes coming from the previous analysis steps of microarray or RNA-seq experiments, or proteins identified in proteomics experiments, the lists of genes located nearby of ChIP_seq peaks, etc.

10.3.1. Gene Set Enrichment Analysis (GSEA)

There are two types of workflows, depending on the format of the input tables. You can start the GSEA with the normalized microarray tables, before calculating DEGs. The program first computes fold changes, which are then used to dynamically detect functional groups of genes that are differentially affected by the experimental conditions.

Alternatively, you can start the GSEA with any gene or protein table having a numerical column that can be used for enrichment calculations, e.g. expression fold change after calculating DEGs.

10.3.1.1. GSEA by GO categories and metabolic pathways

Gene set enrichment analysis (GSEA) with GO categories or with metabolic pathway annotation in REACTOME can be done by either starting from raw data of any of the widely used experimental platforms (Affymetrix, Agilent, or Illumina), or from a single gene table that you may have composed yourself.

Affymetrix, Agilent, or Illumina data sets

The three workflows under this category are designed to perform the GSEA by the three branches of Gene Ontology, biological process, molecular function and cellular component as well as by the Reactome pathways:

Gene Set Enrichment Analysis (Affymetrix probes),

Gene Set Enrichment Analysis (Agilent probes),

Gene Set Enrichment Analysis (Illumina probes).

Each of the three workflows differs in the format of the input data, for Affymetrix, Agilent or Illumina microarray platforms, respectively. The analysis performed by these workflows and the interpretation of the results are the same. As an example, let’s consider an Affymetrix-specific workflow.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It opens in the main Work Space and looks as shown below:

Step 2. Input the Experiment normalized and Control normalized tables from the tree. You can either drag-and-drop or click on the select element box to specify the tables in the Tree area. Here, the tables from the Example folder/ HCV infection in liver GSE31193, Affymetrix U133 Plus 2.0 are used. We aim to find out the enriched functional categories of gene expression upon treatment with IFN type III after 24 hours versus non-treated cells.

It is important to note that for this workflow, the input tables should have Affymetrix probeset IDs in the ID column. Such tables have an () icon in the tree area and look like:

You can see Affymetrix probeset IDs in the ID column, and several columns with the normalized values; each column corresponds to one CEL file.

Step 3. Choose human, mouse, or rat species from the drop-down menu.

Step 4. Specify location and name of the Results folder. Important: the results folder should be located in your Project in the tree.

Step 5. Press the button [Run workflow] and wait till the workflow is completed.

Results

The results folder contains four tables with the results of the enrichment analysis () divided by the three branches of Gene Ontology, biological process, molecular function and cellular component, as well as by the Reactome pathways.

The tables with the enriched categories look like:

The GSEA results are described in details in a separate section below.

The table Ensembl genes annot contains Ensembl genes as a result of Affymetrix IDs convertion into Ensembl gene IDs:

For each gene, gene symbol, gene description, and Affymetrix probeset ID are shown. Additionally, the LogFoldChange value is calculated for each gene.

The distribution of LogFoldChange values is shown in the Histogram:

GSEA by GO categories and metabolic pathways for a single gene table

This workflow performs the GSEA divided by the three branches of Gene Ontology, biological process, molecular function and cellular component, as well as by the Reactome pathways, for any input gene or protein table. It is important to note that such a table should have a column which can be used as a weight column for enrichment, e.g. expression value.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It opens in the main Work Space and looks as shown below:

Step 2. Input a gene table with FoldChange (LogFoldChange) calculated. You can either drag-and-drop or click on the select element box to specify the table in the Tree area. Here, the table from the Example folder/HCV infection in liver GSE31193, Affymetrix U133 Plus 2.0 is used. We aim to find out the enriched functional categories of gene expression upon treatment with IFN type III after 24 hours versus non-treated cells.

The input table may look like the one shown below. This table contains the column logFC (LogFoldChange). This table is an output of the Limma method (Section 4.2.1).

For best GSEA results, input the table with all genes analyzed, e.g. all genes present on the chip in the microarray experiment.

Step 3. As soon as you specified the input table, the drop-down menu in the field Enrichment Weight Column becomes active. It presents all numerical columns in the input table. Select which column should be used for enrichment calculations. Here, the column logFC is selected.

Step 4. Choose human, mouse, or rat species from the drop-down menu.

Step 5. Specify location and name of the Results folder. It is important to note that the results folder should be located in your Project in the tree.

Step 5. Press the button [Run workflow] and wait till the workflow is completed.

Results

The results folder contains four tables with the results of the enrichment analysis () corresponding to the three branches of Gene Ontology, biological process, molecular function and cellular component as well as by the Reactome pathways.

The GSEA results are described in detail in a separate section below.

10.3.1.2. GSEA by GO categories, signaling pathways and diseases

Also this type of gene set enrichment analysis (GSEA) can be done by either starting from raw data of any of the widely used experimental platforms (Affymetrix, Agilent, or Illumina), or from a single gene table that you may have composed yourself.

Affymetrix, Agilent and Illumina microarrays

The three workflows under this category are similar to those described under 10.3.1.1, 1st part, requiring exactly the same two normalized input tables, and the same steps to launch these workflows. The difference is in the ontologies applied. The three workflows under this category are designed to perform a GSEA utilizing the three branches of the PROTEOME™-curated gene ontology, PROTEOME™ biological process, PROTEOME™ molecular function and PROTEOME™ cellular component as well as the TRANSPATH® pathways:

Gene Set Enrichment Analysis PROTEOME (Affymetrix probes),

Gene Set Enrichment Analysis PROTEOME (Agilent probes),

Gene Set Enrichment Analysis PROTEOME (Illumina probes).

The GSEA results are described in detail in a separate section below.

Note. This workflow is available together with a valid PROTEOME™/TRANSPATH® license. Please feel free to ask for details (info@genexplain.com).

Single gene table

This workflow is similar to the one described under 10.3.1.1, 2nd part, it requires exactly the same format of the input table, and the steps to launch this workflow are the same. The difference is in the ontologies applied. This workflow is designed to perform a GSEA utilizing the three branches of the PROTEOME™-curated gene ontology, PROTEOME™ biological process, PROTEOME™ molecular function and PROTEOME™ cellular component, as well as the TRANSPATH® pathways.

The GSEA results are described in detail in a separate section below.

Note. This workflow is available together with a valid PROTEOME™/TRANSPATH® license. Please feel free to ask for details (info@genexplain.com).

10.3.1.3 GSEA with a selected ontology

This workflow performs a GSEA with one selected ontology for an input gene or protein table. It is important to note that such a table should have a numerical column which can be used as a weight column for enrichment, e.g. expression value or fold change.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It opens in the main Work Space and looks as shown below:

Step 2. Input a gene table with FoldChange (LogFoldChange) calculated. You can either drag-and-drop or click on the select element box to specify the table in the Tree area. Here, the table from the Example folder/ HCV infection in liver GSE31193, Affymetrix U133 Plus 2.0 is used. We aim to find out the enriched functional categories of gene expression upon treatment with IFN type III after 24 hours versus non-treated cells.

The input table may look like the one shown below. This table contains the column logFC (LogFoldChange). This table is an output of the Limma method (Section 4.2.1).

For the best GSEA results, input the table with all genes analyzed, e.g. all genes present on the chip in the microarray experiment.

Step 3. As soon as you specified the input table, the drop-down menu in the field Enrichment Weight Column becomes active. It presents all numerical columns in the input table. Select which column should be used for enrichment calculations. Here, the column logFC is selected.

Step 4. Choose human, mouse, or rat species from the drop-down menu.

Step 5. In the field Classification, choose the ontology from the drop-down menu. Here, GO biological process is selected.

Step 6. Define a minimal number of hits in one group which you would like to consider in the field Min number of hits to group. By default it is 30.

Step 7. Specify location and name of the Results folder. Please note that the results folder should be located in your Project in the tree.

Step 5. Press the button [Run workflow] and wait till the workflow is completed.

Results

The results folder contains one table with the results of the enrichment analysis () by the selected ontology.

The results are described in detail in a separate section below.

10.3.2. Functional classification

An alternative approach to GSEA is another group of workflows, Functional classification, which comprises several “Mapping to ontologies” workflows. The difference of this option to GSEA is that no enrichment of the categories is calculated, but that all genes in the list are mapped to GO categories or other ontologies. For example, you can use tables with pre-calculated up-regulated or down-regulated genes, as they are obtained as output of the workflow “Detect differentially expressed genes”, and use these as input into the workflows „Mapping to ontologies“.

The output tabulates which and how many genes from your list (“hits”) fall into which category, how many known genes are in this category, how many hits would have been expected by chance, and what the P-value for the found number of hits being obtained by chance is.

The difference between the workflows within this group is in the ontologies applied as well as in the number of input tables.

10.3.2.1. Mapping to GO categories and metabolic pathways

Single gene or protein table

This workflow is designed to classify an input gene set based on several ontologies, and to identify terms hits for which are overrepresented in the input set. The input file can be any gene or protein table. There is only one obligatory column, the column with gene or protein IDs; all other columns are optional.
In the first step, the input table is converted into a table with Ensembl Gene IDs. This table with Ensembl Gene IDs is subjected to a functional classification.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input table. The input gene set might be a list of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated for the genes shown to be up-regulated in one of the pre-prepared examples. The pertinent example file can be found in the geneXplain platform under:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/detect deferentially expressed genes

Step 3. Specify the biological species of the input set in the field Species by selecting the required biological species from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Results folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Step 5. Press the [Run workflow] button.

The workflow is running as shown below, wait till it is completed.

The results folder contains several tables with the resulting mapping, one table each for the applied ontological groups (), as well as one gene table () as shown below.

When the workflow is completed, all output tables are opened by default.

Let’s consider the output tables.

Mapping to the three GO branches, biological processes, cellular components, and molecular functions (). The tables with the enriched categories look like:

Each row presents details about one ontological term. The column ID comprises the identifier of the ontological category, here identifiers of Gene Ontology biological process terms. These identifiers are hyperlinked to the page http://www.ebi.ac.uk/QuickGO/ where you can get further information about this ontological term.

The columns Title and Group size contain further details about the ontological terms, its title and the number of genes linked to this term in the corresponding database, here in GO. The column Expected hits shows the number of genes expected to fall into this category by random chance, based on the size of the input set and the size of the category. The column Number of hits shows how many genes from the input table fall into this category. P-value and Adjusted p-value are calculated for the difference between expected and real numbers of hits. The genes mapped to each category are explicitly listed in the column Hit names. As the lists can get quite long, only a few genes are shown by default in each row. To get the full list, press [more].

Tip The hits for one or several selected rows can be saved as a separate gene table. This can be done with the button Save hits in the top control menu. Such genes tables can be analyzed further, e.g. to find master regulatory molecules in the networks, and to identify transcription factors that might commonly regulate these genes.

The table HumanCyc pathways (). In the column ID the identifiers of the HumanCyc pathways are given. Upon a mouse click, a diagram of the corresponding metabolic pathway opens in the workspace:

The table Reactome pathways (). In the column ID you can find the identifiers of the Reactome pathways.

Upon a mouse click, a diagram of the corresponding pathway opens in the workspace.

The table TF classification (). Your input table is mapped to the classification of Transcription factors (Nucleic Acids Res. 41, D165-D170 (2013)), which is also integrated in the platform. In the column ID the identifiers of the TF classification are shown. They are hyperlinked to the corresponding classification categories.

The table Ensembl genes ( ). The input gene or protein table is converted to a table with Ensembl gene IDs, and the result is shown in this table. For example, if your input was a table with UniProt IDs, it is converted into Ensembl gene IDs and included in the results folder of this workflow.

2 Gene sets and comparison

This workflow is designed to map two input tables to all Gene Ontology categories (biological process, molecular function and cellular component) to identify terms hits and to compare the results. While the workflow described above allows for selecting one particular ontology, this workflow runs three branches of the GO ontology in parallel, and the comparison between two gene sets is done regarding all three GO branches at once. The input files can be any gene or protein table. In the first step, the input tables are converted into two tables with Ensembl Gene IDs. These new tables are subjected to a Gene Ontology mapping. As result two mapped tables (for each GO category) are stored and further compared via P-values. This last comparison step is based on the method analyses/Methods/Statistical analysis/Compare analysis results, icon . Please refer to section 16.3 for details on this particular analysis method. The comparison can help to reveal items that show different enrichment across certain conditions.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input tables 1 and 2. The input gene sets might be lists of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated for the genes shown to be up-regulated (Top100) and down-regulated (Top100) in one of the pre-prepared examples. The pertinent example file can be found in the geneXplain platform under:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Mapping to ontologies and compare/Up_Down Ensembl Top100 (Mapping to ontologies and compare)

Step 3. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Result folder, and a new window will be opened, where you can select the location of the result folder and define its name.

Step 5. Press the [Run workflow] button.

When the workflow is completed, the result folder is opened by default.

The result folder contains the three subfolders GO for all three GO categories applied in this workflow and two tables (). The two tables correspond to the input tables with the identifiers converted into Ensembl gene IDs. Each subfolder contains two tables () with the mapped ontology results, one table () with the analysis comparison result and one plot (). Please refer to section 10.3.2.1 for the description of the resulting tables.

Multiple gene sets

This workflow is designed to classify several sets of genes or proteins based on the three GO branches, Reactome and HumanCyc pathways and TF classification. Several input gene or protein tables should be located in one folder.

The input is a folder with several gene or protein tables. The steps of this workflow for each individual gene or protein table are the same as described in the section above. The same steps are performed iteratively for each of the gene or protein tables in the input folder.

The output is a folder which contains subfolders with the results for each individual input table. The subfolders are automatically given the same names as the input tables.

10.3.2.2.Mapping to GO categories and signaling pathways

Single gene or protein table

The steps of this workflow are the same as described above in the section 9.3.2.1. The difference is in the ontologies applied. In this workflow, your input table is mapped to GO biological processes, GO cellular components, GO molecular functions, Reactome, HumanCyc, TF classification and TRANSPATH® pathways.

The genes in the input table are mapped to the TRANSPATH® pathways using the latest TRANSPATH® release available in the platform. The columns ID, Title and Group size present information about the TRANSPATH® pathways significantly enriched among your input genes.

The pathway identifiers provide a link to open the corresponding pathway as a diagram in the work space. In the screenshot below a fragment of the IFN alpha/beta pathway is opened.

Tip You can work with the pathway diagrams as with other diagrams in the platform. For example, you can map expression data, save a copy, and export in a number of different formats. To save all genes linked to this diagram as a separate gene table, you need to select the corresponding row in the table with the classification results and apply the Save hits button from the top control menu.

Note. This workflow is available together with a valid TRANSPATH® license.
Please feel free to ask for details (info@genexplain.com).

2 Gene sets and comparison

Mapping to GO ontologies and comparison for two gene sets (PROTEOME™):

The overall idea of this workflow is similar to that the one described above. However, this workflow is designed to map two input tables, to identify hits and to compare the results according to the eight particular ontologies. These are five proprietary ontologies (BIOBASE GmbH), namely the PROTEOME™-curated Gene Ontology categories (PROTEOME™ biological process, PROTEOME™ molecular function and PROTEOME™ cellular component), PROTEOME™ disease and TRANSPATH® pathways, as well as three public ontologies, Reactome pathways, HumanCyc metabolic pathways and the transcription factor classification. Similarly to the workflows described above, the input can be any gene or protein tables. In the first step, the input tables are converted into two tables with Ensembl Gene IDs. These tables are then subjected to a functional mapping to the eight listed ontologies in parallel. The last comparison step is based on the method analyses/Methods/Statistical analysis/Compare analysis results, icon . Please refer to Section 16.3.1 for details on this particular analysis method. The comparison helps to reveal ontological categories that are different between two input data sets. To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input tables 1 and 2. The input gene sets might be lists of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated for the genes shown to be up-regulated (Top100) and down-regulated (Top100) in one of the pre-prepared examples. The pertinent example file can be found in the geneXplain platform under:

data/Examples/Brain Tumor GSE1825%2C Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Mapping to ontologies and compare/Up_Down Ensembl Top100 (Mapping to ontologies and compare (PROTEOME))

Step 3. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Result folder, and a new window will be opened, where you can select the location of the result folder and define its name.

Step 5. Press the [Run workflow] button.

When the workflow is completed, the result folder is opened by default.

The result folder contains eight subfolders; one subfolder for each applied ontology and two tables (). The two tables correspond to the input tables with the identifiers converted into Ensembl gene IDs. Each subfolder contains two tables () with the mapped ontology/pathway/classification results, one table () with the analysis comparison result and one plot (). Please refer to section 9.3.2.4. for the description of the resulting tables.

Note. This workflow is available together with a valid PROTEOME™ license. Please, feel free to ask for details (info@genexplain.com).

Multiple gene sets

This workflow is designed to classify several sets of genes or proteins based on the three GO branches, Reactome and HumanCyc pathways, TF classification, and TRANSPATH® pathways. Several input gene or protein tables should be located in one folder.

The input is a folder with several gene or protein tables. The steps of this workflow for each individual gene or protein table are the same as described in 10.3.2.1, last section The same steps are performed iteratively for each of the gene or protein tables in the input folder.

The output is a folder which contains subfolders with the results for each individual input table. The subfolders are automatically given the same names as the input tables.

Note. This workflow is available together with a valid TRANSPATH® license.
Please, feel free to ask for details (info@genexplain.com).

10.3.2.3.Mapping to GO categories, signaling pathways and diseases

Single gene or protein table

The steps of this workflow are the same as described above in the sections 9.3.2.1 and 9.3.2.3. The difference is in the ontologies applied. In this workflow, your input table is mapped to PROTEOME™ biological processes, PROTEOME™ cellular components, PROTEOME™ molecular functions, PROTEOME™ disease, Reactome, HumanCyc, TF classification and TRANSPATH® pathways.

The results folder contains several tables with the resulting mapping, one table each for each applied ontological group (), as well as one gene table () as shown below.

When the workflow is completed, all the output files are opened by default. The output file PROTEOME (disease) when opened in the work space looks is shown here:

The columns ID, Category, Title and Group size present information about the diseases, as they are curated in the PROTEOME™ database, significantly enriched among your input genes.

Each disease identifier is hyperlinked to an external web page, the Comparative Toxicogenomic Database, where you can find more details about this disease:

http://ctdbase.org/

Note. This workflow is available together with a valid PROTEOME™/TRANSPATH® license. Please, feel free to ask for details (info@genexplain.com).

2 Gene sets and comparison

Mapping to ontologies and comparison for two gene sets (TRANSPATH®)

The specialty of this workflow in comparison with the one described above is in the ontologies applied. This workflow is designed to map two input tables to the seven following ontologies, the public Gene Ontology categories (biological process, molecular function and cellular component), TRANSPATH®, Reactome and HumanCyc pathways as well as transcription factor classification to identify hits and to compare the results. Similar to the workflows described above, the input can be any gene or protein tables. In the first step, the input tables are converted into two tables with Ensembl Gene IDs. These tables are subjected to a functional mapping. The last comparison step is based on the method analyses/Methods/Statistical analysis/Compare analysis results, icon . Please refer to section 16.3 for details on this particular analysis method. The comparison can help to reveal items that show different enrichment across certain conditions.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input tables 1 and 2. The input gene sets might be lists of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated for the genes shown to be up-regulated (Top100) and down-regulated (Top100) in one of the pre-prepared examples. The pertinent example file can be found in the geneXplain platform under:

data/Examples/Brain Tumor GSE1825%2C Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Mapping to ontologies and compare/Up_Down Ensembl Top100 (Mapping to ontologies and compare (TP))

Step 3. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Result folder, and a new window will be opened, where you can select the location of the result folder and define its name.

Step 5. Press the [Run workflow] button.

When the workflow is completed, the result folder is opened by default.

The result folder contains the seven subfolders; one subfolder for each applied ontology and two tables (). The two tables correspond to the input tables with the identifiers converted into Ensembl gene IDs. Each subfolder contains two tables () with the mapped ontology/pathway/classification results, one table () with the analysis comparison result and one plot (). Please refer to section 9.3.2.4. for the description of the resulting tables.

Note. This workflow is available together with a valid TRANSPATH® license. Please, feel free to ask for details (info@genexplain.com).

Multiple gene sets

Please refer to 10.3.1.1, last section, for the description of the resulting tables.

10.3.2.4. Mapping with selected classification

Single gene set

This workflow is designed to map one input tables to one selected ontology classification. The input can be any gene or protein table. In the first step, the input table is converted into one table with Ensembl Gene IDs. The table with Ensembl Gene ID is subjected to a functional classification. As result the mapped table is stored.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input table. The input gene set might be the list of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The pertinent example file can be found in the geneXplain platform under:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Experiment normalized (RMA) (Differentially expressed genes Affy)/Upregulated Ensembl genes filtered (LogFC>2)

Step 3. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 4. In the field Classification, choose the ontology from the drop-down menu. Here, GO biological process is selected.

Step 5. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Result folder, and a new window will be opened, where you can select the location of the result folder and define its name.

Step 6. Press the [Run workflow] button.

When the workflow is completed, the result folder is opened by default.

The result folder contains 1 tables () with the converted Ensembl table and one table ( ) with the mapped ontology results.

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Upregulated Ensembl genes filtered (LogFC>2) (GO (biological process))

Let’s consider the output table with the mapping results. The tables with the mapped selected category () look like:

Each row presents details about one ontological term. The column ID comprises the identifiers of the ontological categories, here identifiers of Gene Ontology biological process terms. These identifiers are hyperlinked to the page http://www.ebi.ac.uk/QuickGO/ where you can get further information about this ontological term.

The columns Title and Group size contain further details about the ontological terms, its title and the number of genes linked to this term in the corresponding database, here in GO. The column Expected hits shows the number of genes expected to fall into this category by random chance, based on the size of the input set and the size of the category. The column Number of hits shows how many genes from the input table fall into this category. P-value and Adjusted P-value are calculated for the difference between expected and real numbers of hits. The genes mapped to each category are explicitly listed in the column Hit names. As the lists can get quite long, only a few genes are shown by default in each row. To get the full list, press [more].

2 Gene sets and comparison

Mapping to ontology - select a classification (2 Gene tables)

This workflow is designed to map each of the two input tables to one selected ontology classification, to identify term hits and to compare the results. The input can be any gene or protein table. In the first step, the input tables are converted into two tables with Ensembl Gene IDs. These tables with Ensembl Gene IDs are subjected to a functional classification. As result two mapped tables are stored and further compared via P-values. This final comparison step is based on the method analyses/Methods/Statistical analysis/Compare analysis results, icon . Please refer to section 13.3 for details on this particular analysis method. The comparison can help to reveal terms that show different enrichment across certain conditions.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It looks as shown below:

Step 2. Specify the input tables 1 and 2. The input gene sets might be the lists of differentially regulated genes or any gene or protein list of interest. You can drag it from your project within the tree area and drop it in the pink box of the field Input table. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

The further steps of the workflow are demonstrated for the genes shown to be up-regulated (Top100) and down-regulated (Top100) in one of the pre-prepared examples. The pertinent example file can be found in the geneXplain platform under:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Mapping to ontologies and compare/Up_Down Ensembl Top100 (Mapping to ontology (GO (biological process))

Step 3. Specify the biological species of the input set in the field Species by selecting it from the drop-down menu.

Step 4. In the field Classification, choose the ontology from the drop-down menu. Here, GO biological process is selected.

Step 5. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field select element in the field Result folder, and a new window will be opened, where you can select the location of the result folder and define its name.

Step 6. Press the [Run workflow] button.

When the workflow is completed, the result folder is opened by default.

The result folder contains 2 tables () with the converted Ensembl tables, two tables with the mapped ontology results, two tables () with the analysis comparison result annotated and one plot ().

Let’s consider the output tables. The tables with the mapped selected category () look like:

Each row presents details about one ontological term. The column ID comprises the identifiers of the ontological categories, here identifiers of Gene Ontology biological process terms. These identifiers are hyperlinked to the page http://www.ebi.ac.uk/QuickGO/ where you can get further information about this ontological term.

The columns Title and Group size contain further details about the ontological terms, its title and the number of genes linked to this term in the corresponding database, here in GO. The column Expected hits shows the number of genes expected to fall into this category by random chance, based on the size of the input set and the size of the category. The column Number of hits shows how many genes from the input table fall into this category. P-value and Adjusted P-value are calculated for the difference between expected and real numbers of hits. The genes mapped to each category are explicitly listed in the column Hit names. As the lists can get quite long, only a few genes are shown by default in each row. To get the full list, press [more].

The Analysis comparison annot table lists identifiers, annotation of identifiers, P-values for the first input set of genes and P-values for the second input set of genes (-log). The column Difference shows the absolute difference between two P-values. The columns Difference P-value and Difference FDR show the statistical significance of the absolute difference and upon sorting by one of these two columns on top you can see those ontology terms that are statistically most significantly different between two input gene sets. The comparison reveals GO terms that show different enrichment across the two input gene lists.

The Analysis comparison plot is a scatter plot of P-values on the log-scale together with the diagonal and the difference cutoffs at FDR < 0.05. Every dot corresponds to one particular GO term. On the X-axis, the –log(p-value) for this GO term in the first input table is shown, and on the Y-axis, the –log(p-value) for the same GO term in the second input table is shown. The red dots correspond to those GO terms that are statistically significantly different between two input tables, with FDR<0.05. The black dots located close to the diagonal, between two dotted lines, are not significantly different between two input datasets.

Multiple gene sets

Please refer to 10.3.1.1, last section, for the description of the resulting tables.

10.3.2.5. Cross-species mapping to ontologies, using ortholog information (PROTEOME™)

This workflow is very similar to the workflows described above in sections 10.3.2.1 and 10.3.2.3. The Input can be any gene or protein table for mouse or rat. The workflow will convert the list to desired species output and mapp it to various ontologies. In this workflow, your input table is mapped to PROTEOME™ biological processes, PROTEOME™ cellular components, PROTEOME™ molecular functions, PROTEOME™ disease, Reactome, HumanCyc, TF classification and TRANSPATH® pathways. It can be found under the tab Workflows, in the folder PROTEOME™/Cross-species mapping to ontologies, using ortholog information (PROTEOME™). The input form of the workflow looks as shown below:

Step 1: Input the gene or protein table of any species for which you wish to map gene ontologies. You can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you can select the input table.

Here further steps are demonstrated with the track available in one of the pre-prepared examples present in the Tree Area:

data/Examples/Transcriptional biomarkers to predict mouse liver tumors, GSE18858/Data/Normalized (RMA) DEGs with EBarrays/Naphthalene_20ppm upreg Ensembl Select the species of the input table

Step 2: Select the desired species of the output table.

Specify the path to store the results and the name of the output folder.

Having filled in the input form, launch the workflow with the [Run] button. Wait till the workflow is completed.

Here the input mouse data is functionally classified and mapped to human data.

In the first step of the workflow, the input gene table is converted to the desired Ensemble gene table using the method ‚convert table via homology‘. In the next step the converted Ensembl gene table is functionally classified using the Proteome database with a p_value threshold of 0.05. The output results folder contains diverse files as shown below:

Mapping to the three GO branches, biological processes, cellular components, and molecular functions ( ). The tables with the enriched categories look like:

For each ontological term several parameters are calculated, including expected number of hits, actual number of hits, p-value, as well as hit names and the link to the corresponding ontological term. For more details on the details of each column please refer to section 10.3.2.1.

The Tables Reactome pathways, Transpath pathways, and humanCyc pathways give the results of the mapping of the input gene set to each of these pathways. Each table has a unique identifier for the corresponding pathway; upon a mouse click, a diagram opens in the workspace.

The table TF classification (). Your input table is mapped to the classification of Transcription factors (Nucleic Acids Res. 41, D165-D170 (2013)), which is also integrated in the platform. In the column ID the identifiers of the TF classification are shown. They are hyperlinked to the corresponding classification categories:

10.4. Analyze regulatory regions

This set of workflows helps to find putative TF binding sites in the DNA sequences under study. There are several workflows in this group that perform searches in different genomic regions, either in promoters, in the peaks calculated from ChIP-seq data, or in any input DNA sequences. This group of workflows is designed using the core functionality of a “site search on gene set” analysis as described in Section 20.1.2.

10.4.1. Motif quality analysis

This tool analyzes the quality of a motif model. The “Motif quality analysis” item is located in the NGS folder of the analysis methods (analyses/Methods/Site analysis/Motif quality analysis) and in the start page group ‘Microarrays’ under section ‘Analyze regulatory regions’.

Step 1. Open the analysis form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. The Sequences input is a track file with sequences containing the motif.

The following link directs to an example input:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Encode%20TFBS%20CEBPB%20in%20H1-hESC%20cells/Data/CEBP%20in%20H1-hESC%20cells%20YES

Step 3. Select a Site model from a profile which can be used to compare the input motif. The model can result from a workflow generated ‘Profile’, can be selected from the TRANSFAC® database or can be built from the ‘Create profile from matrix library’ method (input is ChIPHorde or DiChIPHorde motif).

For this example we selected the profile: http://platform.genexplain.com/bioumlweb/#de=data/Examples/Encode%20TFBS%20CEBPB%20in%20H1-hESC%20cells/Data/ChIPMunk/CEBP%20H1-hESC%20cells%20motif%20profile

CEBP H1-hESC cells motif profile

Step 4. Specify the total Number of points for sensitivity and FDR calculation. By default, the analysis uses 11 points. For the example we use 50 points.

Step 5. Specify the number of Shuffle counts. This is the number of times sequence characters are shuffled to generate random sequences for FDR estimation. By default this number is 10.

Step 6. Select a Seed for the random number generator or keep the default of 0.

Step 7. Declare the Output path to store results in the tree area.

After entering the input parameters, press ‘RUN’. The method starts as shown below:

Post completion the output table is opened in the work space in a new tab and consists of a table like the one shown below.

The output table can be used to create a ROC curve for the visualization of the motif quality and for comparison of different motifs.

10.4.2. Create matrix logo

This tool creates logo representations for position weight or frequency matrices of transcription factor binding sites.

The input can be a profile with a set of matrices or a single matrix.

The input form is as shown below:

Each individual parameter is described below:

PWM (profile or matrix) – Specify the input profile or a single matrix. You can drag it from your project within the tree area and drop it in the pink box of the field PWM. Alternatively, you may click on the pink field “select element” and a new window will be opened, where you can select the input gene set as shown below.

Logo size – The method gives an option to select one of four different sizes for the Matrix logo image. It ranges from small to extra-large.

Reverse – Check this box to create logos for the reverse orientation. By default this box is unchecked.

Adjust height to information –Check this box to adjust total height of bases to information content of position.

Sort bases –Check this box to sort bases, the most important on top.

Plot lines - Checked this box to draw lines behind bases partitioning plot region into four sections.

Output folder – Specify the name and path of the output folder for the created logos.

Here, we take a profile created by the workflow ‘Identify enriched composite modules in promoters (TRANSFAC®)’ as input.

Keeping all other parameters as default, the method runs as shown below:

The output folder contains one PNG image for each matrix of the specified input. Existing files in the output folder are not overwritten. In case of name conflicts the tool suffixes a number to the file name as shown below:

:

The matrix logo output image is as shown below:

Each matrix image can be exported in either .jpeg, .png, or .bmp file formats using the ‘Export document’ button.

10.4.3. Identify enriched TF sites in promoters

10.4.3.1. Version 2.0 (Adjusted p-values)

TRANSFAC®

This workflow is designed to find individual motifs enriched in the promoters of the input gene set as compared with a background set (No set). In the first part of the workflow, the enriched motifs are identified by the method analyses/Methods/Site analysis/Search for enriched TFBSs (genes), icon . Please refer to section 20.1.4 for details on this particular analysis method. Filtered enriched motifs serve as a basis to construct a specific profile, and this profile is run on the promoters of the input gene set, method analyses/Methods/Site analysis/Site search on gene set, icon . Details about this individual method are given in section 6.1.2.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Input the Yes set from the tree. You can either drag-and-drop or select the Yes set from the Tree area. Here, the set of up-regulated genes from the following Examples folder is used:

data/Examples/HCV infection in liver GSE31193, Affymetrix U133 Plus 2.0/Data/

The Yes set in this example contains 125 genes up-regulated in human liver cells treated with interferon- (IFN) as compared with non-treated cells.

Step 3. Similarly input the NO set from the tree area. By default the workflow uses a subset fo 300 genes randomly taken out of the human housekeeping genes. The default NO set can be found here:

http://platform.genexplain.com/bioumlweb/#de=data/Public/Data%20sets/Data/Housekeeping%20genes%20(Human)%20300

Here, the set of non-changed genes from the Examples folder is used:

data/Examples/HCV infection in liver GSE31193, Affymetrix U133 Plus 2.0/Data/

The No set in this example contains 237 genes whose expression was unchanged in human liver cells treated with IFN as compared with non-treated cells.

Step 4. Select the profile. This profile will be applied at the first part of the workflow for identification of the enriched motifs. The default profile is vertebrate_human_p0.001 from the most recent TRANSFAC® release available. It can be found here:

http://platform.genexplain.com/bioumlweb/#de=databases/TRANSFAC(R)%202014.4/Data/profiles/vertebrate_human_p0.001

The number of matrices in the profile shown here is 4321.

Any other TRANSFAC® profile or user-specific profile can be selected. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 5. After input of the Yes and No sets, the species (human, mouse or rat) is adjusted automatically. Verify the species shown in the species field.

Step 6. Filter by TFBS enrichment fold: In this field you can specify the enrichment fold (FE) to filter the motifs. By default it is 1.0, which means all motifs with FE>1.0 will be reported in the resulting table and the same motifs will serve to create a specific profile. If you want to use highly-enriched motifs, you can specify higher thresholds, e.g. 1.1, 1.2 etc, or even 2.0 or 3.0 depending on your Yes and No sets. It is recommended that you run it with default parameters first, check the results, and then run again with the desired filter value.

Step 7. Specify the promoter region relative to TSS as they are annotated in Ensembl. The default promoter region is -1000 to +100 relative to the TSS. You can edit the fields Start promoter and End promoter as required.

Step 8. Specify the result folder location and name and Press the button [Run workflow]. Wait till the workflow is completed.

Results.
The results folder consists of several files and folders as shown below:

The table Enriched Motifs () contains those site models, here TRANSFAC® matrices, which are enriched in the Yes set in comparison with the No set as shown below.

Each row of the output table represents the result for one PWM from the input profile. Only those PWMs with adj. site FE >1 are included in the output. For details on the output columns please refer to section 20.1.4.1. Recommended sorting, as shown on the screenshot above, is by column Adj. site FE (adjusted fold enrichment for sites) with the highest values on top.

Please note that out of 4307 matrices in the initial profile, hits for 697 matrices are enriched with adj. site FE >1. These matrices are considered to create profiles specific for the input Yes and No sets.

Motifs for IRF, STAT, ICSBP transcription factors are highly enriched, with adj. site FE >2, as shown in the screenshot above. This is a very relevant result considering that here the effect of IFN on liver cells is studied.

The table Profile () presents details for PWMs with adj. site FE >1.

This profile is an intermediate result of the workflow and is used further for Site search on gene set analysis.

Site search analysis output () serves to visualize enriched motifs in the promoters. This folder contains four tracks ():

Each track can be opened in the genome browser by double-clicking. A visualization of the track yes sites is shown below:

The same track can be opened as a table; for this use right mouse click on the track name in the tree area or Ctrl +mouse click for Mac users.

With the same menu, you can apply other functions to the selected track, e.g. export it in available formats or delete.

Table view on the track yes sites is the following:

The output table Transcription factors Ensembl genes is a list of transcription factors linked to the enriched motifs. For each transcription factor, the Ensembl gene ID is provided, as well gene description, HGNC gene symbol, species, and site model (TRANSFAC® PWM name).

This table can be further annotated to add a column with expression values, as shown below. Details for annotation of the tables are given in the section 16.1.1.

Twelve TFs are found to be highly up-regulated under the same conditions, all members of Ets, STAT, IRF, MEF2 families. The role of these TFs in regulation of the input Yes genes is suggested by two independent lines of evidence: first, genes encoding these TFs are highly up-regulated, and second, their binding motifs are significantly enriched in the promoters of Yes genes.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

GTRD

This workflow is designed to search for putative transcription factor binding sites, TFBS, in the promoters of an input gene set. It is very similar to the workflow described above in Section 10.4. The only difference is in the PWM library applied. Here, site search is done with the help of the GTRD library (see 19.8 for further details about this library).

For the input form and description of the results folder, please refer to Section 10.4.

10.4.3.2. Version 1.2 (Classical)

TRANSFAC®

This workflow is designed to search for putative transcription factor binding sites, TFBS, in the promoters of an input gene set. Site search is done with the help of the TRANSFAC® library of positional weight matrices, PWMs, namely with the profile vertebrate_non_redundant_minSUM.

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify input gene or protein set in the field Input gene set.
The input table contains the genes under study, and it is also called the 'Yes' set. To specify a gene set, you can drag & drop it from your project within the Tree Area, and drop it in the pink box of the field Input gene set. Alternatively, you may click on the pink field “select element”, and a new window will open, where you can select the input gene set as shown below. After you have selected the gene set, press [Ok].

Step 3. Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 4. Define where the folder with the results should be located in the tree. You can do so by clicking on the pink field “select element” in the field Results folder, and a new window will open where you can select the location of the results folder and define its name as shown below.

Step 5. Press the [Run workflow] button.

Wait until the workflow is completed, which is shown below:

The results folder contains several files:

The tables called summary (), TF Ensembl genes, and TF Entrez genes are opened automatically in the work area as soon as the workflow is completed.

For more details about the results, please refer to Section 20.1.4.

Tip. You can easily create a similar workflow with parameter values adjusted to your needs. For example, you can select another profile from the list of available TRANSFAC® profiles, or specify different promoter positions relative to the TSS (default is -1000 to +100).

To do this, you need first to open the workflow in the “Edit workflow” mode, and save a copy in your project area. The [Edit workflow] button is located near the button [Run workflow] (see above, Step 1). Upon clicking on [Edit workflow], the workflow diagram will open in the Work Space, and you can select one of the analyses you would like to modify. For the screenshot below the "Site search on gene set" analysis was selected, and in the Operations Field, on the tab "Workflow", all the parameters are visible. Under this mode, you can either check what the default parameters are, or modify them according to your needs.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

GTRD

This workflow is designed to search for putative transcription factor binding sites, TFBS, in the promoters of an input gene set. It is very similar to the workflow described above in Section 10.4. The only difference is in the PWM library applied. Here, site search is done with the help of the GTRD library (see 19.8 for further details about this library).

For the input form and description of the results folder, please refer to Section 10.4.

10.4.4. Identify composite modules in promoters

10.4.4.1. Version 2.0 (Adjusted p-values) with TRANSFAC®

This workflow is designed to find pairs of sites in the promoters of the input gene set. This workflow enables the identification of combinations of several enriched TFBSs in the promoters of the genes under study (Yes-set). The resulting composite module differentiates the Yes-set from a background set (No-set).

In the first part of the workflow, the enriched motifs are identified by the method analyses/Methods/Site analysis/Search for enriched TFBSs (genes), icon . Please refer to Section 20.1.4.1 for details on this individual analysis method.

Motifs with an enrichment of >1.0 fold serve as a basis for constructing a specific profile, and this profile is run on the promoters of the input gene set, method analyses/Methods/Site analysis/Site search on gene set, icon . Details about this individual method are given in the section 20.1.2. In the second part of this workflow, composite modules are identified based on the enriched TFBSs. For more details about CMA analysis refer to section 20.1.5.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Input the Yes set from the tree. You can either drag-and-drop or select the Yes set from the Tree area. Here, the set of up-regulated genes from the following Examples folder is used:

data/Examples/HCV infection in liver GSE31193, Affymetrix U133 Plus 2.0/Data/

The Yes set in this example contains 125 genes up-regulated in human liver cells treated with interferon- (IFN) as compared with non-treated cells.

Step 3. Similarly input the NO set from the tree area. By default the workflow uses a subset of 300 genes randomly taken out of the human housekeeping genes. Here, the set of non-changed genes from the same Examples folder is used.

Step 4. After input of the Yes and No sets, the species (human, mouse or rat) is adjusted automatically. Verify the species shown in the species field.

Step 5. Select the profile. The selected profile will be applied at the first part of the workflow for identification of enriched motifs. The default profile is vertebrate_human_p0.001 from the most recent TRANSFAC® release available. Any other TRANSFAC® profile or user-specific profile can be chosen. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected. The number of matrices in the default profile, which is used here, is 4307.

Step 6. Set up parameters for the composite module search. This workflow identifies pairs of sites. By default, the minimum and maximum numbers of pairs are given as 2 and 8. You can change these parameters according to the number of pairs you aim to identify. The number of iterations of the genetic algorithm is 300 by default, and can be adapted as required.

Step 7. Specify the promoter region relative to TSS as they are annotated in Ensembl. The default promoter region is -1000 to +100 relative to the TSS. You can edit the fields Start promoter and End promoter as required.

Step 8. Specify the result folder location and name and Press the button [Run workflow]. Wait till the workflow is completed.

Note. This workflow may take more time depending on the size of the Yes and No sets and on the number of iterations. The recommended size of the Yes set is 150 genes maximum, and the recommended size of the No set is 300 genes maximum. The maximum recommended number of iterations is 300.

Results
The results folder consists of several folders and files as shown below:

The table Enriched Motifs () contains those site models, here TRANSFAC® matrices, which are enriched in the Yes set as compared to the No set as shown below.

Each row of the output table represents the result for one PWM from the input profile. Only those PWMs with adj. site FE >1 are included in the output. For details on the output columns please refer to section 9.5.1. Recommended sorting, as shown in the screenshot above, is done by highest Adj. site FE (adjusted fold enrichment for sites).

Please note that out of 4307 matrices in the initial profile, hits for 697 matrices are enriched with adj. site FE >1. These matrices are considered to create profiles specific for the input Yes and No sets.

Motifs for IRF, STAT, ICSBP transcription factors are highly enriched, with adj. site FE >2, as shown in the screenshot above. This is a very relevant result considering that here the effect of IFN on liver cells is studied.

The table Profile () presents details for PWMs with adj. site FE >1.

This profile is an intermediate result of the workflow, and it is used further for Site search on gene set analysis.

Site search analysis output () is an intermediate result of the workflow, and its results are used further for the identification of composite modules. Details about the individual output files in this folder can be found in Section 20.1.4.

The Modules folder () is a result of the analysis Construct composite modules. It contains two tables, two tracks, one histogram, and one model view as shown below:

The Model View is a graphical summary for the hierarchically organized composite elements generated as a result of the CMA analysis. As mentioned above, this workflow is designed to identify pairs of sites, and we asked to identify 2 to 5 pairs. The Model view presents four pairs, and we can see by exactly which site models (matrices) these pairs are formed as well as statistical parameters of the overall model.

Each track can be opened in the genome browser by double-clicking. Visualization of the composite modules on the promoter of ISG15, one of the Yes set genes, is shown below.

For more details on the individual output tables and tracks as well as for visualization of the identified composite modules in the genome browser please refer to Section 20.1.4.

The output table Transcription factors Ensembl genes is a list of transcription factors linked to the site models in the composite module identified by the workflow. For each transcription factor, the Ensembl gene ID is provided, as well as a gene description, the HGNC gene symbol, species, and site model (TRANSFAC® PWM name).

Twelve transcription factors shown in the table above are candidate regulators of genes in the input Yes set. They are suggested to regulate transcription of Yes-genes via the identified composite elements. This table can be further annotated to add a column with expression values, as shown below. Details for annotation of the tables are given in Section 16.1.1.

Three TFs are found to be highly up-regulated under the same conditions, IRF1, 6 and 7. The role of these TFs in regulation of the input Yes genes is suggested by two independent lines of evidence: genes encoding these TFs are highly up-regulated, and their motifs are parts of the identified enriched composite modules.

Note. This workflow is available together with a valid TRANSFAC® license.
Please, feel free to ask for details (info@genexplain.com).

10.4.4.2. Version 1.2 (Classical) with TRANSFAC®

This workflow enables the identification of combinations of several TFBSs in the promoters of the genes under study (Yes-set). Such combinations of sites are referred to as composite modules. The resulting composite module differentiates the Yes-set from a background set (No-set).

In the first part of the workflow a Site search on gene set ( ) is performed with your selected Yes-set, No-set and a specified profile of matrices. You can refer to section 9.3 for details of this method. In the second part of this workflow, composite modules are identified () based on the identified single sites in the Yes and No sets. For more details about the hierarchical structure of the composite modules, search for composite modules, visualization and interpretation of the results refer to section 20.1.5.4.

To launch the workflow, follow these steps:

Step1. Open the workflow input form from the Start page. It opens in the main Work Space and looks as shown below:

Step 2. Input the Yes set from the tree. You can either drag-and-drop or select the Yes set from the Tree area. Here, the set of genes from the Example folder is used as input Yes set.

Step 3. Similarly input the No set from the tree area. By default the workflow uses a subset fo 300 genes randomly taken out of the human housekeeping genes. The default NO set can be found here:

http://platform.genexplain.com/bioumlweb/#de=data/Public/Data%20sets/Data/Housekeeping%20genes%20(Human)%20300

Step 4. After input of the Yes and No sets, the species (human, mouse or rat) is adjusted automatically. Verify the species shown in the species field.

Step 5. Select the profile. This profile will be applied in the first part of the workflow for identification of TFBSs. The default profile is vertebrate_non_redundant_minSUM from the most recent TRANSFAC® release available. Any other TRANSFAC® profile or user-specific profile created with TRANSFAC® matrices can be chosen. With a mouse click on the field Profile, a pop-up window opens, where a profile can be selected. The profile used in this example is:

http://platform.genexplain.com/bioumlweb/#de=databases/TRANSFAC(R)%202014.4/Data/profiles/vertebrate_human_p0.001

Tip If you are interested in finding site models for particular TFs, and see them eventually in the resulting composite modules, you need to be sure that such matrices are present in the selected profile.

Step 6. Set up parameters for the composite module search. This workflow identifies pairs of sites. By default, the minimum and maximum number of pairs are fixed as 2 and 8, respectively. You can change these parameters according to the number of pairs you want to identify. The number of iterations of the genetic algorithm is 300 by default, and can be adapted as required.

Step 7. Specify promoter regions relative to the TSS as they are annotated in Ensembl. The default promoter region is -1000 to +100 relative to the TSS. You can edit the fields Start promoter and End promoter as required.

Step 8. Specify the result folder location and name and press the button [Run workflow].

Note. This workflow may take more time depending on the size of the Yes and No sets and on the number of iterations. The recommended size of the Yes set is 150 genes maximum, and the recommended size of the No set is 300 genes maximum.

Results

The results folder consists of two folders and one table as shown below:

Site search analysis output () The summary table of the site search is shown below. Identified TFBSs are used further for the identification of composite modules. Details about the individual output files can be found in Section 20.1.2.

With a double-click on the folder modules, the visualization of the composite modules in the promoters of the Yes set will be opened in the work space. Simultaneously, in the operations field, under the tab My description, a plot with a schematic representation of the composite modules and statistical parameters are shown. In the Info box you can see the list of parameters this particular run of the workflow was done with.

The modules folder can be expanded in the tree area. It contains two tables (), two tracks (), and two plots ():

The plot Model View is a graphical summary for all composite modules generated as a result of the CMA analysis, and it can be opened in the work space.

The plot Histogram is a distribution of scores for individual promoters:

For a detailed interpretation of the histogram as well as for a visualization of the identified composite modules in the genome browser, please refer to section 20.1.5.5.

The output table Transcription factors Ensembl genes is a list of transcription factors linked to the site models in the composite module. For each transcription factor, the Ensembl gene ID is provided, as well a gene description, HGNC gene symbol, species, and site model (TRANSFAC® PMW name):

Note. This workflow is available together with a valid TRANSPATH® license.
Please, feel free to ask for details (info@genexplain.com).

10.4.5. Cross-species identification of enriched motifs in promoters using ortholog information (TRANSFAC®)

This workflow is designed to find individual motifs enriched in the promoters of the input gene set as compared with a background set (No set). It is very similar to the workflow described in section 10.4.3.1 except that here you can use an input table for any species and get the output for the desired species.

The workflow can be accessed from the start page here:

analyses/Workflows/TRANSFAC/Cross-species identification of enriched motifs in promoters, using ortholog information (TRANSFAC(R))

In the first part of the workflow, enriched motifs are identified by the method analyses/Methods/Site analysis/Search for enriched TFBSs (genes), icon . Please refer to section 20.1.4 for details on this particular analysis method. Filtered enriched motifs serve as a basis to construct a specific profile, and this profile is applied to the promoters of the input gene set, method analyses/Methods/Site analysis/Site search on gene set. Details about this individual method are given in section 6.1.2. The last step is a conversion to homology transcription factors.

The input form looks as shown below:

Step 1: Select an Input Yes gene set from the tree. You can either drag-and-drop or select the Yes set from the Tree area.

Here, the set of up-regulated genes from the following Examples folder is used:

data/Examples/Transcriptional biomarkers to predict mouse liver tumors, GSE18858/Data/Normalized (RMA) DEGs with EBarrays/Naphthalene_20ppm upreg Ensembl Select the species of the input table

Step 2: Specify the Species of the input set and the Species of the output set.

Step 3: Select Input No gene set from the tree area. By default, the workflow uses a subset from 300 genes randomly taken out of the human housekeeping genes.

Step 4: The profile will be applied in the first part of the workflow for the identification of enriched motifs. The default profile is vertebrate_human_p0.001 from the most recent TRANSFAC® release available. Any other TRANSFAC® profile or user-specific profile can be selected. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 5: Filter by TFBS enrichment fold: In this field you can specify the enrichment fold (FE) to filter the motifs. By default, it is 1.0, which means all motifs with FE>1.0 will be reported in the resulting table and the same motifs will serve to create a specific profile. If you want to use highly-enriched motifs, you can specify higher thresholds, e.g. 1.1, 1.2 etc., or even 2.0 or 3.0 depending on your Yes and No sets. It is recommended that you run it with default parameters first, check the results, and then repeat with the desired filter value.

Step 6: Specify the promoter region relative to TSS as they are annotated in Ensembl. The default promoter region is -1000 to +100 relative to the TSS. You can edit the fields Start promoter and End promoter as required.

Step 7: Specify the Result folder location and name and Press the button [Run workflow]. Wait till the workflow is completed.

The result folder consists of several files as shown below:

The table Enriched Motifs () contains those site models, here TRANSFAC® matrices, which are enriched in the Yes set in comparison with the No set. More details on the result can be found in section 10.4.3.1.

The table Molecules Orthologs have a list of molecules from the input gene set with their site search results as shown below:

Every gene is linked to the corresponding matrix molecule by the Yes-No ratio. More details on each column of the above results can be found in section 6.1.2.

The table Molecules_human contain the site models of the converted input table. In this case the output species is Human hence this table is Molecules_human with mapping to human Ensembl genes with corresponding matrices. If the output species is mouse, then this table will have mouse Ensembl genes.

The table Transcription factor Ensemble genes contains

The output table Transcription factors Ensembl genes is a list of transcription factors linked to the enriched motifs. For each transcription factor, the Ensembl gene ID is provided, as well as gene description, HGNC gene symbol, species, and site model (TRANSFAC® PWM name). This table can be further annotated to add a column with expression values, as shown below. Details for annotation of the tables are given in the section 16.1.1.

10.4.6. Visualization of site search results

This method visualizes results of the site search analyses. It can be found under the tab Analyses, in the folder Methods/Site analysis/Site search report (). Here the default input form is shown:

In the following, we will consider the input fields one by one.

Result of site search analysis. You can drag & drop the site search result (must contain summary table with p-value column) from your project within the tree area. Alternatively, you may click on the pink field “select element”, and a new window will open, where you select the site search result. After having selected the result, press the [Ok] button.

For this example, all further steps are demonstrated with the following input set:

data/Examples/Transcriptional biomarkers to predict mouse liver tumors, GSE18858/Data/Naphthalene_20ppm upreg Ensembl (enriched motifs_TRANSFAC(R))/Site search -1000 100

Number of best models. Choose the number of best models (according to p-value) to include in the report. The default value are the three top models.

Add columns with site positions. If you are interested in the promoter positions of the single sites, please check the appropriate box.

Target report path. Define where the table with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field, and a new window will open, where you can select the location of the resulting table and define its name.

Press the [Run] button and wait until the method is completed. The result opens automatically and looks like this:

We see a visualization of two promoters from the genes FXR1 and SLCO1B3. The TSS is on the right side, where the blue box is located. All arrows are identified sites of the best models and marked with different colors. Also the orientation of the individual sites is shown by the arrow head. The number Total count gives the number of all sites according to the three best models. The numbers of sites for every model (1-3) are given in the next three columns. As an example, model V$POU3F1_03 finds 49 sites in the promoter of FXR1. The column V$POU3F1_03 positions contains all single site positions for the first model in the promoter of FXR1, e.g. -994 from TSS.

10.5. Further workflows in this area

For the other workflows that you can find in the area Microarrays, please refer to the following Sections:

Load data

See Chapter 3

Analyze networks

See Section 5.1

Find drug targets

See Chapter 11

Drug targets

11.1. Find drug targets with TRANSFAC® and GeneWays

This is very similar to the workflow described in greater detail below, Section 11.2, with the only difference being the pathway database applied for network analysis. Here the search for master regulatory molecules is performed with the protein-protein interaction network of the GeneWays database. Because of the high connectivity between molecules in GeneWays, by default 4 steps upstream of the input list of TFs are considered for the search for master regulators, as compared with 10 steps when applying the TRANSPATH® database. A diagram for the top master regulator as suggested from a GeneWays-based analysis is shown below in a force-directed layout.

For the input form of this workflow and for the description of the corresponding results folder, please refer to Section 11.2.

Note. This workflow is available together with a valid TRANSFAC® license.
Please feel free to ask for details (info@genexplain.com).

11.2. Find drug targets with TRANSFAC® and TRANSPATH®

The geneXplain upstream analysis is an integrated promoter (TRANSFAC®) and pathway (TRANSPATH®) analysis to discover unanticipated causal relationships in your data.

11.2.1. Complete upstream analysis (TRANSFAC® and TRANSPATH®)

To launch the workflow, open the workflow input form from the Start page:

Step 1: Specify a gene set under study, e.g. a list of differentially regulated genes, as the Input Yes gene set. You can drag & drop it from your project within the Tree Area and drop in the pink box of the field Input gene set.

For this example, the further steps are demonstrated with the following input set:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E1)

Step 2: Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 3: Input a No gene set. This is the set of background genes or control set. The default No set used for this workflow is data/Examples/Sample data/Data/Housekeeping genes (Human). If your Yes set is from mouse or rat, you may wish to adjust the No set accordingly. With a mouse click on the field Input No gene set, a pop-up window will be opened as shown below. You can select mouse or rat housekeeping genes from these pre-compiled sample sets, or you can alternatively select any of your specific gene sets from your project. When selection is done, press [Ok].

Step 4: Define a TRANSFAC® profile. The default profile is vertebrate_non_redundant_minSUM. Any other TRANSFAC® profile or user-specific profile can be chosen. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 5: Define the promoter regions to be analyzed. The default promoter region is from-1000 to 100 relative to the TSS as annotated in the Ensembl database. You can adjust Start of promoter and End of promoter by typing in the corresponding fields.

Step 6: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Results folder, and a new window will open, where you can select the location of the results folder and define its name.

Step 7: Press the [Run workflow] button. Wait until the workflow is completed, and take a look at the results.

Visualization and interpretation of results

The result folder contains several files and one subfolder:

Master regulators

The primary result table Master regulators upstream 10 () is a list of master regulatory molecules that were identified at a distance of up to 10 steps upstream of the input TFs. Each master regulatory molecule is characterized by a Score, Z-score, FDR, and Ranks Sum.

Further details about the columns of this table and how to work further with it are given in Section 5.1.1. The selection of the best master regulatory molecules based on Score, Z-score and Ranks sum is explained in there under “Interpretation of the results”.

The three Top 3 regulators diagrams ()visualize the networks for each of the three top master regulators. By default, the top regulators are identified upon sorting the Master regulators upstream 10 table () by the column Ranks sum with the lowest rank on top.

Master regulatory molecule

Input molecules

for the network analysis

Intermediate molecules

suggested by the algorithm

Tip for working with the diagrams

By default network diagrams are shown in the vertical hierarchical layout. The layout can be interactively changed into horizontal hierarchical, or force directed, or orthogonal layouts as described in Chapter 23. Expression data can be mapped on the diagrams as described in the Section 21.3.

Results of the promoter analysis

Along with the master regulatory molecules, this workflow returns the results of the promoter analysis, including TFBSs enriched in the promoters of the Yes set as compared with the No set, see summary (). The tracks with the Yes and No promoters and with the TF binding sites () are also included in the output. In the screenshot below the results of this workflow are shown with the subfolder Site search -1000 +100 opened:

The corresponding tables and tracks are described in detail in Section 16.2.5.

Note. This workflow is available together with valid TRANSFAC® and TRANSPATH® licenses. Please feel free to ask for details (info@genexplain.com).

11.2.2. Enriched upstream analysis

This workflow enables a complete upstream analysis using the newest algorithm to detect enriched transcription factor binding sites (version 2.0), resulting in the identification of master regulators upstream from the transcriptional key molecules. To launch the workflow, open the workflow input form from the Start page:

Step 1: Specify a gene set under study, e.g. a list of differentially regulated genes, as the Input Yes gene set. You can drag & drop it from your project within the Tree Area and drop in the pink box of the field Input gene set.

For this example, all further steps are demonstrated by means of the following input set:

http://platform.genexplain.com:8080/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/New%20workflow%203.1.1/Upregulated%20Ensembl%20genes%20filtered_2

Step 2: Input a No gene set. This is the set of background genes or control set. The default No set used for this workflow is data/Examples/Sample data/Data/Housekeeping genes (Human). If your Yes set is from mouse or rat, you may wish to adjust the No set accordingly.

Step 3: Define a TRANSFAC® profile. The default profile is vertebrate_human_p0.001. Any other TRANSFAC® profile or user-specific profile can be chosen. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 4: Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 5: Filter by TFBS enrichment fold: In this field you can specify the enrichment fold (FE) to filter the motifs. By default, FE is 1.0, which means all motifs with FE>1.0 will be reported in the resulting table and the same motifs will serve to create a specific profile. If you want to use highly-enriched motifs, you can specify higher thresholds, e.g. 1.1, 1.2, or even 2.0 or 3.0 depending on your Yes and No sets. It is recommended that you run the workflow with default parameters first, check the results, and then run again with the desired filter value.

Step 6: Define the length of the promoter regions to be analyzed. The default promoter region is from-1000 to 100 relative to the TSS as annotated in the Ensembl database. You can adjust Start of promoter and End of promoter by typing in the corresponding fields.

Step 7: Checking Allow big input enables analysis of more than 500 promoters.

Step 8: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Results folder, and a new window will open, where you can select the location of the results folder and define its name.

Step 9: Press the [Run workflow] button. Wait until the workflow is completed, and take a look at the results.

Visualization and interpretation of results

The result example folder can be found under data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/New workflow 3.1.1/Enriched upstream analysis_TF and TP/Upregulated; itcontains several files:

Enriched motifs

The list of motifs, which were identified during the first part of the workflow and filtered with enrichment fold >1 can be found in the table Enriched_motifs (). It contains those site models, here TRANSFAC® matrices, which are enriched in the Yes set in comparison with the No set as shown below.

The example has 86 detected motifs with enrichment fold >1.

Please refer to section 10.4.3 to learn more about the site enrichment results. The table Profile () presents details for PWMs with adj. site FE >1. This profile is an intermediate result of the workflow and is used further for Site search on gene set analysis.

Site search analysis output () serves to visualize enriched motifs in the promoters. This folder contains four tracks (). The output table Transcription factors Ensembl genes is a list of transcription factors linked to the enriched motifs. For each transcription factor, the Ensembl gene ID is provided, as well gene description, HGNC gene symbol, species, and site model (TRANSFAC® PWM name).

This list of transcription factors is the input for the second part of the workflow, the master regulator search.

Master regulators

The primary result table Master regulators upstream 10 () is a list of master regulatory molecules that were identified at a distance of up to 10 steps upstream of the input TFs. Each master regulatory molecule is characterized by a Score, Z-score, FDR, and Ranks Sum.

Further details about the columns of this table and how to work further with it are given in Section 5.1.1. The selection of the best master regulatory molecules based on Score, Z-score and Ranks sum is explained therein under “Interpretation of the results”.

The three Top 3 regulators diagrams () visualize the networks for each of the three top master regulators. By default, the top regulators are identified upon sorting the Master regulators upstream 10 table () by the column Ranks sum with the lowest rank on top.

Please refer to section 5.1.1 for more details about master regulator results.

Note. This workflow is available together with valid TRANSFAC® and TRANSPATH® licenses. Please feel free to ask for details (info@genexplain.com).

11.2.3. Focused upstream analysis

This workflow searches for enriched transcription factor binding sites (TFBSs), and selects those transcription factors (TFs), which were detected via direct (from input genes) regulator search method. To launch the workflow, open the workflow input form from the Start page:

Step 1: Specify a gene set under study, e.g. a list of differentially regulated genes, as the Input Yes gene set. You can drag & drop it from your project within the Tree Area and drop in the pink box of the field Input Yes gene set.

For this example, all further steps are demonstrated with the following input set:

http://platform.genexplain.com:8080/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/UpDownReg%20Ensembl%20genes

Step 2: Input an Input No gene set. This is the set of background genes or control set. The default No set used for this workflow is data/Examples/Sample data/Data/Housekeeping genes (Human). If your Yes set is from mouse or rat, you may wish to adjust the No set accordingly.

Step 3: Define a TRANSFAC® profile. The default profile is vertebrate_human_p0.001_non3d. Any other TRANSFAC® profile or user-specific profile can be chosen. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 4: Specify the biological species of the input set in the field Species by selecting the desired species from the drop-down menu.

Step 5: Filter by TFBS enrichment fold: In this field you can specify the enrichment fold (FE) to filter the motifs. By default, it is 1.3, which means all motifs with FE>1.3 will be reported in the resulting table, and the same motifs will serve to create a specific profile. If you want to use highly-enriched motifs, you can specify higher thresholds, e.g. 1.5, 1.6 etc., or even 2.0 or 3.0 depending on your Yes and No sets. It is recommended that you run it with default parameters first, check the results, and then repeat with the desired filter value.

Step 6: Define the promoter regions to be analyzed. The default promoter region is from -1000 to 100 relative to the TSS as annotated in the Ensembl database. You can adjust Start of promoter and End of promoter by typing in the corresponding fields.

Step 7: Checking Allow big input enables analysis of more than 500 promoters.

Step 8: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Results folder, and a new window will open, where you can select the location of the results folder and define its name.

Step 9: Press the [Run workflow] button. Wait until the workflow is completed, and take a look at the results.

Interpretation of results

The result folder contains several files and one profile (collection of matrices):

Enriched motifs

The list of motifs, which were found during the first part of the workflow, and were filtered with enrichment fold >1.3, can be found in the table Enriched_motifs (). It contains those site models, here TRANSFAC® matrices, which are enriched in the Yes set in comparison with the No set. The example has 260 detected motifs with an enrichment fold >1.3. The table Transcription factors Ensembl genes includes the corresponding 161 TFs from the site models as shown below. The Profile contains the matrix collection of converted and filtered site models.

Effectors

The result table Effectors () is a list of identified regulatory molecules found with effector search method from the input gene list. Each effector molecule is characterized by a Score, Z-score, FDR, and Ranks Sum.

Focused transcription factors

The final table of focused_TFs is a list of 5 linking molecules between detected TFs and the regulatory elements from effector search analysis. The final list contains CUX1, RAD21, ILF3, POU2F1 and HDAC2 as upstream regulators of the identified TFs.

Note. This workflow is available together with valid TRANSFAC® and TRANSPATH® licenses. Please feel free to ask for details (info@genexplain.com).

11.2.4. Upstream analysis with feedback loop

This workflow enables a complete upstream analysis, detecting enriched transcription factor binding sites and resulting in the identification of master regulators upstream from the transcriptional regulators. Some master regulators with expression values (fold changes) from the input set are identified (with feedback loop). The results of this workflow include master regulators from all transcription factors and master regulators only with expression values from the input set (=feedback loop). To launch the workflow, open the workflow input form from the Start page:

Step 1: Specify a gene set under study, e.g. a list of differentially regulated genes, as the Input Yes gene set. You can drag & drop it from your project within the Tree Area and drop in the pink box of the field Input gene set.

For this example, all further steps are demonstrated with the following input set:

http://platform.genexplain.com:8080/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/UpDownReg%20Ensembl%20genes

Step 2: Specify the biological species of the input set in the field Species by selecting the required species from the drop-down menu.

Step 3: Input a No gene set. This is the set of background genes or control set. The default No set used for this workflow is data/Examples/Sample data/Data/Housekeeping genes (Human). If your Yes set is from mouse or rat, you may wish to adjust the No set accordingly.

Step 4: Define a TRANSFAC® profile. The default profile is vertebrate_non_redundant_minSUM. Any other TRANSFAC® profile or user-specific profile can be chosen. With a mouse click on the field Profile, a pop-up window will open, where a profile can be selected.

Step 5: Define the promoter regions to be analyzed. The default promoter region is from -1000 to 100 relative to the TSS as annotated in the Ensembl database. You can adjust Start of promoter and End of promoter by typing in the corresponding fields.

Step 6: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Results folder, and a new window will open, where you can select the location of the results folder and define its name.

Step 7: Press the [Run workflow] button. Wait until the workflow is completed, and take a look at the results.

Visualization and interpretation of results

The example result folder is here: data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Upstream analysis with feedback loop (Transfac and Transpath) and contains several files.

Master regulators

The primary result table Master regulators upstream 10 () is a list of master regulatory molecules that were identified at a distance of up to 10 steps upstream of the input TFs. Each master regulatory molecule is characterized by a Score, Z-score, FDR, and Ranks Sum.

Further details about the columns of this table and how to work further with it are given in Section 5.1.1. The selection of the best master regulatory molecules based on Score, Z-score and Ranks sum is explained therein under “Interpretation of the results”.

The three Top 3 regulators diagrams ()visualize the networks for each of the three top master regulators. By default, the top regulators are identified upon sorting the Master regulators upstream 10 table () by the column Ranks sum with the lowest rank on top.

The three Top 3 regulators diagrams with Expression () visualize the networks for each of the three top master regulators, which have expression values from the input table. These master regulators with expression values are the results of a feedback loop; they are regulated by themselves. By default, the top regulators with expression values are identified upon sorting the Master regulators with input expression_filtered () by the column Ranks sum with the lowest rank on top.

The output of the workflow shows the top master regulator huntingtin, which was found as a feedback-loop regulated molecule according to the input parameters.

Note. This workflow is available together with valid TRANSFAC® and TRANSPATH® licenses. Please feel free to ask for details (info@genexplain.com).

11.3. Further workflows in this area

Load gene or protein list

See Chapter 3

Pathways

All the workflows that constitute this Area have already been explained in other Chapters, where pathway-related functions contribute. Therefore, please refer to the Chapters and Section listed below.

Load gene or protein list,

Load pathways and models

See Chapter 3

Discover pathway enrichment

See Section 10.3

Analyze networks

See Section 5.1

NGS

Many of the workflows that constitute this Area have already been explained in other Chapters, please refer to the Chapters and Section listed below.

Load gene or protein list

See Chapter 3

Discover pathway enrichment

See Section 10.3

ChIP-seq

See Chapter 7

Genomic variants

Genomic variants can be uploaded in the platform in different formats. The files uploaded in bed format are shown in the tree area as tracks (). Genomic variants can be also uploaded as a table, where the ID column contains standard SNP IDs (e.g. rs10010325). When imported into the platform, the tables with this type of ID have a special icon () in the tree area. An example of such a table can be found in the Examples folder:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/SNPs%20linked%20to%20human%20height/Data/SNP_height_hg19

14.1. Find genome variants and indels from full-genome NGS

This workflow is based on a framework to discover genotype variations in full-genome NGS data by De Pristo et al., Nature Genetics 43:491-498, 2011. The process includes initial read mapping, local realignment around indels, base quality score recalibration, SNP discovery and genotyping to find all potential variants.

In the first part of the workflow the input sequences are mapped using the BWA tool (Galaxy). BWA is a fast light-weight tool that aligns relatively short sequences to a sequence database, such as the human reference genome (published by Li & Durbin, Bioinformatics 25:1754-1760, 2009).

The second part includes local realignment around indels, base quality score recalibration, SNP discovery and genotyping to find all potential variants. After the first part, and after identification of duplicates and covariates, the workflow creates a first output as a new BAM file. Then the recalibrated BAM file is used as an input for SNP discovery and genotyping to find all potential variants by GATK (Genome Analysis Toolkit).

To launch the workflow, follow these steps:

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Input the Forward and Reverse fastq files. You can either drag&drop or select the files from the Tree area. Here, a set of files from the Example folder is used as input.

data/Examples/Chronic Myeloid Leukemia Patient Genotyping/Data/SRR944150 forward.fastq

data/Examples/Chronic Myeloid Leukemia Patient Genotyping/Data/SRR944150 reverse.fastq

Step 3. Specify the OutputFolder location and name and press the button [Run workflow].

All results are saved in the result folder:
data/Examples/Chronic Myeloid Leukemia Patient Genotyping/Data/SRR944150 forward.fastq (Genome variants and indels from RNA-seq)

In the first step the input fastq sequences are subjected to the BWA method from Illumina. BWA is a software package for mapping low-divergent sequences against a large reference genome, such as the human genome. It consists of three algorithms: BWA-backtrack, BWA-SW and BWA-MEM. The first algorithm is designed for Illumina sequence reads up to 100bp, while the other two algorithms are designed for longer sequences ranging from 70bp to 1Mbp. BWA-MEM and BWA-SW share similar features such as long-read support and split alignment, but BWA-MEM, which is the latest, is generally recommended for high-quality queries as it is faster and more accurate. BWA-MEM also has better performance than BWA-backtrack for 70-100bp Illumina reads.

In the next step the sorted files are subjected to the Mark Duplicates method.

This method removes duplicates. The purpose is to mitigate the effects of PCR amplification bias introduced during library construction. Two read pairs are considered duplicate if they align to the same genomic position. The resulting MarkDuplikates1.log file is stored in the log folder and the MarkDuplikates1.stat file is stored in the stat folder.

The next step is a local realignment. Read mapping algorithms operate on each read independently, locally realigning reads such that the number of mismatching bases is minimized across all reads. Output files are Realigner.log and TargetCreator.log in the log folder, ddup1.bam, Realigned.bam and realigner.intervals in the tmp folder.

The realigned BAM file is used again to remove duplicates (output MarkDuplicates2.log and MarkDuplicates2.stat), because realignment may change genomic positions of read pairs, after this step additional duplicates can be identified. The next step is a recalibration of base quality values. For each base in each read the method calculates various covariates (such as reported quality score, position in read, dinucleotide, read GC-content). Using these values it builds the model that predicts sequencing errors. Then it applies this model to calculate an empirical base quality score and overwrites the phred quality score in the read. Output is a new BAM file (Good.bam).

The user can view the Good.bam files in the genome browser by double-clicking on it. The browser shows each aligned read and also shows nucleotides mismatching between the reads and the reference genome sequence.

This file is used then for the unified GATK (Genome Analysis Toolkit) genotyper method to detect the SNP-indels. It generates a table in VCF format, which can be viewed either as a table or as a track in the genome browser (right mouse button click and select either “Open track” or “Open table”).

In the track visualization the information about each variation (either a base substitution or an indel) is shown in the info box when clicking on each variation.

14.2. Visualize variants in genome browser

The genomic variants shown in the tree area as tracks () can be directly visualized in the genome browser. Tables with SNP IDs ( ) should be first processed into the tracks. For this, you can apply the method called SNP matching (); for details please refer to the section 17.1.1.

A mouse click on the links Human, Mouse or Rat immediately opens up a genome browser for the corresponding species in the work space, and the corresponding Ensembl database appears in the tree area.

14.2.1. Human

When Human is selected, the genome browser opens up the latest Ensembl build, hg19 chromosomes GRCh37, highlighted by the red oval.

In the pop-up window Add tracks to genome browser you can select which tracks among those available in Ensembl should be opened together with your track of the genomic variants. Two tracks are selected by default, GC-content and Genes. When the selection is ready, push the [Ok] to get the following view:

Now you can drag & drop your track with the genomic variants on the genome browser to add it to the default tracks. As an example, the following track is shown here, in the screenshot below:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/SNPs%20linked%20to%20human%20height/Data/SNP_height_hg19%20(Analyse%20SNP%20list%20(TRANSFAC))/All%20SNPs/SNP_height_hg19%20track

For further details regarding visualizations, please refer to the basic operations with tracks, Section 2.3.3.

14.2.2. Mouse

When Mouse is selected, the genome browser opens up the latest Ensembl build for mouse, mm10 chromosomes GRCm38

The further steps of the visualization are similar to the human tracks.

14.2.3. Rat

When Rat is selected, the genome browser opens up the latest Ensembl build for rat, rn5 chromosomes Rnor_5.0

The further steps of the visualization are similar to the human tracks.

14.3. Identify and classify genes with genomic variants

Genomic variants are represented in the same format as genome intervals or ChIP-seq peaks, i.e. with their absolute chromosomal positional locations. Therefore, please apply correspondingly the workflows explained in detail under 6.1.1 or 7.2.1.

14.4. Identify TFBS affected by genomic variations

14.4.1. Enriched TF sites around regulatory SNPs and SIFT analysis

Analysis with TRANSFAC®

The input form of this workflow, when opened form the Start page, is the following:

Step 1. Specify an input table in the field Input SNP table. A table with standard SNP IDs in the format like rs10010325 can be used as an input. The tables with this type of IDs have a special icon ( ) in the tree area. In this example the following input table with 180 SNPs is used:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/SNPs%20linked%20to%20human%20height/Data/SNP_height_hg19

Step 2. Specify the region around each SNP in the field 5’ and 3’ gene bound extension. By default this region is 10000 bp long. Genes located within the region of 10000 bp around each SNP in the input list will be considered as SNP target genes.

Step 3. Specify a TRANSFAC® profile in the field Profile. The workflow uses the default profile vertebrate_non_redundant_minFN from the TRANSFAC® library, but another TRANSFAC profile can be chosen as needed.

Step 4. Specify the region around each SNP that will be analyzed for potential TFBSs in the field SNP surrounding region. The default length of this region is 30 bp on each flank.

Step 5. Select a species corresponding to your input table from the drop-down menu in the field Species.

Step 6. Specify the path to store the results and the name of the output folder in the field Results folder.

Step 7. Having filled the input form, launch the analysis with the [Run] button. Wait till the workflow is completed.

Results

The output is a result folder with three subfolders named all SNPs, SNPs in exons and SNPs regulatory, respectively, containing all resulting tables and tracks:

The results shown here can be found in the trea area: data/Examples/SNPs linked to human height/Data/SNP_height_hg19 (Analyze SNP list (TRANSFAC))

Subfolder All SNPs

This folder includes one gene table and one track.

The table SNPs on genes, schematic map () contains all genes that were identified in the region of 10000 bp on both flanks around each SNP, in this example 119 genes.

Each row of this table corresponds to one gene; the column ID presents Ensembl gene IDs, and HGNC gene symbols are listed in the column Gene symbol. The title of the last column Schematic also contains the name of the input table. This column represents a schematic view for each gene, where blue boxes correspond to exons, and the lines between exons symbolize introns, drawn in logarithmic scale. SNPs are shown by vertical red lines. This schema provides an overview of SNP location within genes.

A track () represents the results of the SNP mapping to genomic positions. In this example, out of 180 input SNPs 148 were mapped to the genome, and for them the following information is shown:

For each SNP the tabulated view of the track contains information about chromosomal location, absolute positions, length, and strand. In the column Type the value variation is shown for all SNPs, and in the column Property: name SNP IDs are shown.

Subfolder SNPs in exons

This folder includes two tables, both present information for those SNPs that are located in exons. In our example, 19 out of 148 SNPs mapped to the genome are located in exons.

One of the tables contains standard SNP IDs in the ID column, and has the same icon as the input SNP table ().

This table contains general information about SNPs that are located in exons. Each row in this table corresponds to one SNP. The columns Ensembl ID and Gene symbol refer to the gene in which this particular SNP is located. The column Location confirms that all SNPs are located in exons. The absolute genomic positions of the SNPs are shown in the columns SNP_matching-Chromosome, SNP_matching-Position and SNP_matching-Strand. The column SNP_matching-Allele shows which nucleotide exactly varies at the listed position.

If your input SNP table contains more columns in addition to IDs, all these columns will be preserved and will be added to the right side of this table.

The other table in this subfolder results from the SIFT analysis, and is represented by an icon for a general table (). SIFT is a widely accepted method to check whether a particular variation is synonymous or non-synonymous, and in case of a non-synonymous variation whether it is damaging or tolerated. More details about SIFT can be found under http://sift.jcvi.org/www/SIFT_help.html.

This table also has 19 rows according to the number of SNPs identified in exons. There are many columns in this table, we will consider the most important ones in the following.

The columns Codons and Substitution show which nucleotide in a codon varies and which amino acid is substituted by which. The column SNP Type shows if it is a synonymous or a non-synonymous variation, and in case of non-synonymous variations the column Prediction shows if it is damaging or tolerated. An extension of this table to its right side is shown below, starting with the column Prediction:

The columns Gene ID, Gene Name, Gene Desc show information about which genes and gene products are affected and might be even damaged by a given variation.

Subfolder SNPs regulatory

This subfolder contains three tables and one track.

In this example, 129 out of 148 SNPs mapped to the genome are located in introns or gene flanking regions. The table () contains standard SNP IDs in the ID column, and has the same icon as the input SNP table, and as the table with SNPs in exons. The structure of the latter was described above in detail, under the subheading Subfolder SNPs in exons.

The other two tables and one track in this subfolder present the results of the TFBS search in the SNP surrounding regions.

The table Summary: TFBSs around regulatory SNPs () shown below has been sorted by the values in the Yes-No ratio column.

Each row summarizes the information for one PWM. The columns Yes density per 1000bp and No density per 1000bp show the number of matches normalized per 1000 bp length for the sequences around SNPs and in the sequences around random genomic positions, respectively. The column Yes-No ratio is the ratio of the first two columns. Only matrices with a Yes-No ratio higher than 1 are included in the Summary table. The higher the Yes-No ratio, the higher the enrichment of matches is for the respective matrix in the sequences around regulatory SNPs. The matrix cutoff values calculated by the program at the optimization step are shown in the column Model cutoff, and the last column shows the P-value of the corresponding event.

The table TFs binding around regulatory SNPs () includes transcription factors (TFs) that are associated with the PWMs listed in the table above, and each row shows details for one TF, including its Ensembl gene ID (column ID), gene symbol, gene description of the corresponding TF (columns Gene description, Gene symbol). The column Site model ID shows the identifier of the PWM associated with this TF, and several further columns repeat information that is also shown in the table above.

These TFs are suggested to have their binding sites in close proximity or even overlapping with SNPs, and their binding might be affected by a given SNP.

The track TFBSs around regulatory SNPs () gives information about the genomic positions of the identified TFBSs.

Each row presents details for an individual TFBS. The columns Sequence (chromosome) name, From, To, Length and Strand show the genomic location of the match including chromosome number, start and end positions, strand, and length of the match. The column Type contains information about the type of the elements; in this case all matches are assigned the type TF binding site. Further columns keep information about the site model producing each match (column Property:siteModel) as well as a score of the core (column Property:coreScore), and a score for the whole site model (column Property:score).

Tip. Further visualization of tracks in the genome browser:
When having tracks opened in the Work Space, the menu button on the top panel can be applied to visualize it. A supplementary window Add tracks to genome browser will open. Here you can select tracks that can be visualized together with your track (shown below).

After pressing [Ok] you will get the picture shown below with your track in focus at the top position.

A number of options are available to navigate the browser and get the desired view. You can use several buttons at the top panel to zoom in and out, and to shift the visible part of the map left or right.

With the help of the small triangles next to the track names you can jump to the next or previous element of this track.

With the drop-down menu shown below, you can jump between different chromosomes, or specify exact positions in the Position window.

When having a track opened in the genome browser, you can drag & drop any other track over the same picture to add it to the browser, where you can find it below the bottom-most track. You can drag & drop it then to any track into desired position.

For example, you can add the track with all input SNPs from the folder All SNPs, and shift it to the top position, and then jump to the position of the 1st SNP along the chromosome. The resulting picture is shown below.

Next, you can zoom in down to the nucleotide level, and get the following picture, where one of the identified regulatory SNPs, rs2284746, is overlapping with several TFBSs, e.g. with the binding sites for c-Maf and PPAR.

Note. This workflow is available together with a valid TRANSFAC® license.
Please feel free to ask for details (info@genexplain.com).

Analysis with GTRD

This workflow is similar to the one described above. The difference is in the database applied for the TFBS search; in this workflow it is the GTRD database. Correspondingly, the results for regulatory SNPs overlapping with TFBSs might be different.

The results of this workflow can be found under:

data/Examples/SNPs linked to human height/Data/SNP_height_hg19 (Analyze SNP list (GTRD))

14.4.2. Find enriched TF binding sites in variation sites

This workflow is designed to study variations (mutations) located especially within promoter regions. It helps to address the questions, which TFBSs are enriched around the variations, and which TFs are responsible for the regulation of the corresponding promoters.

As input, you submit two tracks, one track in vcf format with variations (mutations), and another track with the promoters in focus; by default all promoters from Ensembl 65.37 are used. You also need to specify a profile (a set of PWMs), and a window (region) around each variation point, e.g. 10-20 bp, where TFBS analysis is to be performed.

As output you get a summary table with enriched TFBSs, a table with transcription factors as well as a track with the enriched TFBSs, which can be used for visualization in the genome browser.

The workflow can be found on the Start page, in the section “Genomic variants”.

Step 1. Open the workflow input form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. Specify the Input Variation track in vcf format.
To specify the track, you can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you can select the input track. After having selected the track, press the [Ok] button.
You can use either a newly imported track in vcf format, or a track that has been calculated within the platform, e.g. as a result of the workflow Find genome variants and indels from RNA-seq or the workflow Find genome variants and indels from full-genome NGS. Both workflows can be found under http://platform.genexplain.com/bioumlweb/#de=analyses/Workflows/Common/

In the following example we took as input the track SNP_indels.vcf, which can be found at: data/Examples/Chronic Myeloid Leukemia Patient Genotyping/Data/SNP_indels.vcf

This vcf file was produced by the workflow Find genome variants and indels from full-genome NGS.

Step 3. Specify the Gene promoters. By default all promoters -1000 to +100 bp relative to the TSS from Ensembl 65.37 genome version are used.

Step 4. Select the Profile. This profile will be applied for the identification of the enriched motifs around variation sites. The default profile is vertebrate_non_redundant_minSUM from the most recent TRANSFAC® release available.

Step 5. Specify the Variation surrounding region in base pairs. By default 15 bp are used. Within these region/window the search for enriched TFBSs will be performed.

Step 6. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Results Folder, and a new window will be opened, where you can select the location of the results folder and define its name.

Start the workflow by pressing the [Run workflow] button.

Below you can see the result folder for the example: data/Examples/Chronic Myeloid Leukemia Patient Genotyping/Data/SNP_indels.vcf (Enriched TF binding sites (TRANSFAC))/

The output folder contains on sub-folder with a track and two tables.

The Summary table: This table contains a list of site models (PWMs) over-represented around variation (mutation) sites in promoters, in this case 14 site models. By default this table is sorted by the Yes-No ratio, with the most over-represented model on top.

For a visualization of the over-represented TF binding sites in the variation sites you can open the SNP_indels.vcf TFBS around regulatory Variations track in the genome browser.

The table SNP_indels.vcf TFBS around regulatory Variations presents the list of TFs corresponding to the over-represented site models, in this case 14 TFs. This table contains the Site model IDs of over-represented binding sites, as well as the Gene description and Gene symbol for each transcription factor. The results can be sorted by Yes-No ratio, as it is shown on the screenshot below.

14.4.3. Mutation effect on sites analysis

This method allows to find transcription factor binding sites (TFBSs) affected by variations or mutations.

The analysis “Mutation effect on sites” can be found in the NGS folder of the analysis methods (analyses/Methods/NGS/Mutation effect on sites) or under the start page button ‘Genomic variants’ under section ‘Identify TFBS affected by genomic variations’.

Step 1. Open the analysis form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. The Input VCF track is a track file with mutations and should be in vcf format.

One input example is here on the platform:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Chronic%20Myeloid%20Leukemia%20Patient%20Genotyping/Data/SNP_indels.vcf

Open the track file as a table, and for each variation point you can see several columns with genomic position, chromosome, alternative nucleotide, etc., as shown below.

Step 3. Verify the Sequences source and use the drop-down menu for different Ensembl genome annotations of human, mouse and rat, as shown below.

Alternatively, you can choose 'Custom' from the same menu, if you would like specify another genome, e.g. a particular patient genome imported into the platform before. As soon as the option 'Custom' is chosen, an additional field, Sequence collection, automatically appears on the input form (screenshot below), and you can specify the sequences location manually.

Step 4. Select the Profile. This profile will be applied for the identification of transcription factor binding sites overlapping with the variation positions. The default profile is vertebrate_non_redundant_minSUM from the most recent TRANSFAC® release available.

Step 5. The Score difference from unaffected to affected site is per default 5.0. This parameter is a threshold for the difference between the TFBS score in the reference genome and the TFBS score at the same position with a variation in the alternative sequence. For TRANSFAC matrices adjust this parameter between 0.1 and 0.5.

All TFBSs with score differences above this specified value will be reported in the output track.

The lower this value, the more TFBS will be reported as the result, because even a small change in the score will be considered. If you are interested in those TFBSs that are more strongly affected by a variation, set this parameter to 0.5 (see also example output with score difference = 0.5).

Step 5. Specify the path and name of the Output track.

After completion the output track file (SNP_indels.vcf affected sites) is opened by default in the work space. One example of the affected identified site is shown in the red box (V$EBOX_Q6_01).

This resulting track can be found in the Examples folder under the URL: http://platform.genexplain.com/bioumlweb/#de=data/Examples/Chronic%20Myeloid%20Leukemia%20Patient%20Genotyping/Data/Affected%20binding%20sites

Opening the track as a table shows all affected TFBSs in table format.

The upper example highlighted by the red box has ID=1 in the table. The columns From and To define the positions of the affected site within the genome on chromosome 1 (Sequence (chromosome) name). The column Length shows the length of the binding motif, here 10. The Type TF binding site shows that a transcription factor binding site is affected with a score difference of 0.90404.

The column Property: Score difference shows the arithmetical difference between TFBS score in the reference genome and the TFBS score at the same position with a variation (in the alternative sequence). The score difference can be positive or negative. A positive score indicates a disrupted site and a negative score predicts a new site (site appears).

A positive score difference means that the given TFBS had a better score in the reference sequence, and it was decreased by the variation. In the other words, the given variation disrupted a TFBS which occurred in the reference sequence.

A negative score means that the given TFBS has a better score in the sequence with the variation as compared to the reference sequence. A given TFBS became stronger or even appears after the variation. The conclusion can be made that a given variation created or enhanced corresponding TFBS.

The last column Property: siteModel gives a link to the matrix model and can be opened in the workspace to view the matrix logo.

14.4.4. SIFT (Sorting Tolerant From Intolerant) analysis

The SIFT analysis tool predicts whether a single amino acid substitution (AAS) affects protein function, based on sequence homology and the physical properties of amino acids. SIFT can be applied to naturally occurring non-synonymous single nucleotide polymorphisms (nsSNP) and laboratory-induced missense mutations. This tool uses a SQLite databases containing pre-computed SIFT scores and annotations for all possible nucleotide substitutions at each position in the human exome. Allele frequency data are from the HapMap frequency database, and additional transcript and gene-level data are from Ensembl BioMart. The updated version of SIFT is published in Nat Protoc. 4:1073-1081, 2009.

The tool can be found in the Galaxy section of the geneXplain platform (analyses/Galaxy/Human Genome Variation/SIFT) or on the start page button ‘Genomic variants’ under section ‘Identify TFBS affected by genomic variations’.

Step 1. Open the analysis form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. The input Dataset must contain columns for the chromosome, position, and alleles. The alleles must be two nucleotides separated by '/', usually the reference allele and the allele of interest. The strand must either be in another column.

Input example format:

chr3      81780820      +      T/C
chr2      230341630      +      G/A
chr2      43881517      +      A/T
chr2      43857514      +      T/C
chr6      88375602      +      G/A
chr22      29307353      -      T/A
chr10      115912482      -      G/T
chr10      115900918      -      C/T
chr16      69875502      +      G/T

One example input table can be found here on the platform: data/Examples/SNPs linked to human height/Data/SNP_height_hg19 (Analyse SNP list (TRANSFAC))/SNPs in exons/SNP_height_hg19 matched SNPs in exons

Step 3. Determine the Genome ID. The tool currently works only for genome builds hg18 or hg19.

Step 4. Define the Column with chromosome. In our example above it is column 5 (SNP_matching-Chromosome).

Step 5. Define the Column with position. In our example above it is column 6 (SNP_matching-Position).

Step 6. Selection if Position coordinates are one-based (default) or zero-based counted.

Step 7. Define the Column with allele. In our example above it is column 7 (SNP_matching-Allele).

Step 8. Define whether the Strand info is a column in the dataset (default).

Step 9. Define the Column with strand. In our example above it is column 8 (SNP_matching-Strand).

Step 10. Select Include comment column for additional comments.

Step 11. Possibility to select multiple output columns and Include the following additional fields in the output table.

Step 3. Define where the output table should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output, and a new window will be opened, where you can select the location of the table and define its name.

Start the SIFT analysis by pressing the [Run workflow] button.

An example output table can be found here: data/Examples/SNPs linked to human height/Data/SNP_height_hg19 (Analyse SNP list (TRANSFAC))/SNPs in exons/SNP_height_hg19 matched SNPs in exons SIFT

The column Codons in the output table shows the originally and changed codon of the input position coordinates. The Transcript ID and Protein ID give information about the affected gene/protein. If the SNP Type is Nonsynonymous, the Prediction of the protein function is DAMAGING, which means that the functionality of the protein is predicted as being compromised.

Selected additional fields like Gene ID, Gene Name and others are shown in the output table.

14.5. Further workflows in this area

For the other workflows that you can find in the area Genomic variants, please refer to the following Sections:

Load genome variation data

See Chapter 3

Identify and classify variant genes

See Sections 6.1.1, 7.2.1

Discover functional enrichment among variant genes

See Section 10.3

Analyze networks of with variant genes

See Section 5.1

Metabolism

15.1. Analyze metabolic networks

15.1.1. Find longest metabolic chain

The goal of this analysis is to find longest chains which contain as many elements from the input collection as possible. Here chain means a path which starts and ends with the elements from the input collection. In this path the length between two elements from the input collection is limited by the maximum search radius. 

This method can be found under the analyses tab using the path analyses/Methods/Molecular networks/Find longest connected chains.

The input form of the method looks as shown below:

Step 1: Specify the Molecules collection, which can be any molecule, protein or gene list.

To specify the input table, you can drag & drop it from your project within the tree area.

Step 2: Specify the Search direction, either upstream, downstream reactions or both directions.

Step 3: Selection of Maximal search radius, the default is 10.

Step 4: Selection of Maximal depth which will be used by the Dijkstra search algorithm, the default is 100.

Step 5: Specify the Score cutoff – Molecules with a Score lower than specified will be excluded from the result.

Step 6: Specify the Search collection, which can be one of the drop-down menu shown below.

Step 7: Specify the biological species of the input set in the field Species by selecting the desired species from the drop-down menu.

Step 8: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Output name, and a new window will open, where you can select the location of the results folder and define its name.

Example:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Find Master regulator_Transpath/Upregulated_Ensembl/Regulators upstream 10 Proteins Transpath peptides

Using all other default parameters, press run and wait for the method to complete.

The result is a table which opens by default as shown below:

All chains within the radius 10 are included in the results. You can click on each row and visualize the results as shown below:

Similarly other long chains can be visualized from the result table.

15.1.2. Find metabolic clusters by shortest path

Please refer to Section 3 and apply the steps explained there correspondingly.

15.1.3. Find metabolic clusters by all path

This analysis allows you to generate a cluster of genes/molecules upstream or downstream or both by taking reactions and all intermediate molecules from a specified search collection. To launch the analysis, open the method form from the Start page:

Step 1: Specify the Molecules collection, which can be any molecule, protein or gene list.

To specify the input table, you can drag & drop it from your project within the tree area.

Step 2: Select the maximum Input size (expert modus).

Step 3: Specify the Search direction, either upstream, downstream reactions or both directions.

Step 4: Selection of Maximal search radius, the default is 3.

Step 5: Specify the Search collection, which can be one of the drop-down menu shown below.

Step 6: Specify the biological species of the input set in the field Species by selecting the desired species from the drop-down menu.

Step 7: Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Output name, and a new window will open, where you can select the location of the results folder and define its name.

15.1.4. Flux Balance Analysis

To launch this workflow, open the workflow input form from the Start page:

Step 1: Specify a gene set under study, e.g. a list of differentially regulated genes, as the Input Yes gene set. You can drag & drop it from your project within the Tree Area and drop in the pink box of the field Input gene set.

For this example, all further steps are demonstrated with the following input set:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E1)

Step 2: Specify the metabolism database (MetDB) by selecting Recon2 or HMR from the drop-down menu. As default the Recon2 database is selected.

Step 3: Define the Score column, Max column and Objective function column by selecting one numerical column from your input table from the drop down menu.

Step 4: Define the Max radius, which is the maximal number of steps within the selected metabolism database. The default value is 2.

Step 5: Specify the Search direction by selecting from the drop-down menu; default is both.

Step 6: Define where the output table should be located in your project tree. You can do so by clicking on the pink box (select element) in the field Output flux table, and a new window will open, where you can select the location of the results table and define its name.

Step 7: Press the [Run workflow] button. Wait until the workflow is completed, and take a look at the results.

Visualization and interpretation of results

The results consist of several files and one folder:

The first step of the workflow is to convert the input genes into enzymes (output example: Obesity upreg Ensembl Enzymes). Next step is to match the enzymes with metabolites (output example: Obesity upreg Ensembl Metabolites). The 634 metabolites are listed in table format with Recon2 IDs.

Parallel to the described conversion and match steps, the enzyme list is converted into an enzyme reaction table (output example: Obesity upreg Ensembl Reactions), and 663 reactions are listed in table format with Recon2 IDs. Then a Flux Balance Table (Obesity upreg Ensembl FBC) is calculated, where the input parameters Score column, Max column and Objective function column are used. After that, a table with flux data is constructed. From this Obesity upreg Ensembl Flux table, together with the first cluster, a new diagram (Cluster 1 flux) is created, which shows the input proteins from cluster 1 with added and calculated flux information. A screen from the output is shown below:

The folder Obesity upreg Ensembl Shortest path Db-Recon2 Radius-2 Direction-Both contains all results from the cluster analysis within the workflow.

15.2. Further workflows in this area

For the other workflows that you can find in the area Metabolism, please refer to the following Sections:

Load list of gene, proteins or metabolites

See Chapter 3

Load metabolic pathways

See Chapter 3

Discover metabolic pathway enrichment

See Section 10.3

Popular functions

16.1. Operations with tables

Any table may be opened by double-clicking the corresponding name in the Tree Area. It will open under a new tab in the Work Space.

The contents of the table are sorted according to the values in one of its columns. Being opened for the first time, a default column is defined for sorting, usually the ID column. This default column is indicated by a blue arrowhead. If this arrowhead points upwards, the table rows are sorted in ascending order of this column’s values. Clicking on this arrowhead will change it into a downwards pointing one, while the values are sorted in descending order. Correspondingly, you may sort the table according to the values of any other column in ascending or descending order by clicking on the up- or downwards pointing gray arrowhead on top of this column, respectively.

On top of the table, you can navigate between the individual pages of the table; it is also shown on which page out of how many pages in total you are, and in the right top corner, the page size in terms of number of entries (rows) is shown and can be adjusted.

You can edit the contents of a table by pressing the [Edit] button in the right upper corner. Now, you can manually edit the contents of each cell in the table. With the [Apply] option, you will save this change, while [Cancel] quits it.

Even without activating the Edit function, you can select

- individual rows with a left-mouse click,

- several ones by keeping the Ctrl key pressed,

- a range of rows with the Shift key pressed when clicking on the last row of the range to be selected, or

- [Select all] by clicking on the corresponding button.

The selected rows can be saved as a separate file, which by default is given the name <original file name> subset, but you can change this name.

Changing the table structure in the Operations Field

Having opened a table in the Work Space, e.g. by double clicking on its name in the Tree Area, it is possible to edit its structure in the Operations Field under the tab Columns.

For instance, if you have opened a table with data about Enrichment GO Molecular Mechanism (resulting from having run a GSEA), this field may look like this:

Recognizably, you can change the column headers, the data type in the column, or its (usually hidden) descriptions. You may add an Expression, which may be a mathematical formula, formulated in Java script; you find detailed explanations for this when you press the Edit key () next to this field. In the last column, you can specify which columns are visible or shall be hidden (unmarking a column here does NOT delete it, it hides it from the currently displayed table).

If you hide a column by unmarking it, you have to refresh the Work Space by pressing the button in the control panel right on top of the Operations Field. Here, you can also add new () columns. Before removing a column with the button , you have to mark it by clicking somewhere in the background of the line specifying this column; the selected item will be highlighted in blue. But be careful: Deleting it from the table will irrevocably erase the column including all its contents!

16.1.1. Annotate table

The analysis method Annotate table () can be found in the Tree Area, under the Analyses tab in the folder Methods, subfolder Data manipulation. The complete path to this method is:

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Data%20manipulation/Annotate%20table

Applying the Annotate table method, you can add columns to any gene or protein table in the tree. The source for annotation columns can be a database or any other table.

The input table will not be changed. As a result of the analysis, a new table with additional columns will be created.

The input form of this method, when opened in the work space, is shown below:

In the following, we will consider the input fields one by one:

Experiment – Input a table for which you wish to add annotation. In order for this analysis to work properly, the ID column of this table should contain recognizable biological identifiers that can be mapped to the annotation source identifiers.

Species - Species corresponding to the input table. By default, human is selected. If your input table corresponds to a mouse or rat dataset, please specify it.

Annotation source – Select the data collection, a database or any table in the tree area, which you plan to use as a source of additional columns. Below two examples are given, with the Ensembl database and with a user-specific table as possible annotation sources.

Annotation Columns - As soon as the annotation source is specified, all columns of this table are visible in the drop-down menu. There you can select one or several columns from the drop-down menu, which will be added to the input table.

Output table – Select the location in the tree area where the resulting table will be stored, and define a name for the new table. If a table with the same name already exists at the same location, it will be replaced.

Annotate table of Affymetrix probe IDs with gene description and gene symbol

As input, a table with normalized Affymetrix probes is selected. This input file can be accessed with the URL:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)

In the field Species Human is selected, because the input table corresponds to a human dataset.

By default, the Ensembl database, namely its gene table, is selected in the field Annotation source. All columns present in this table are available in the drop-down menu of the field Annotation columns. As shown in the screenshot below, two columns are selected, Gene description and Gene symbol:

Next, the output path is defined, and you can press the [Run] button.

After completion of the analysis the output file is opened automatically in the work space as shown below:

In this result table two new columns are added, Gene description and Gene symbol, to the right of the ID column. The ID column itself, and all the other columns are exactly the same as they were in the input table.

Annotate a gene or protein table with expression values

You may wish to see the expression values in any gene or protein table. In this example, let’s consider the annotation of the master regulatory molecules table with fold change values. Such a table can be generated, e.g. by the workflows Find master regulators in networks, described in Section 5.1.1.

Further steps are shown with the following input table:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E1)%20(Master%20regulators%20Transpath)/Regulators%20upstream%2010

In the field Species Human is selected, because the input table corresponds to a human dataset.

As Annotation source you can use a table with expression values corresponding to this dataset. You may have such a table in your tree area, e.g. a table with differentially expressed genes. In this example, the following table is used as annotation source:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E1)

As soon as the annotation source table is selected in the field Annotation source, you can see all available columns in the drop-down menu of the Annotation columns field. The LogFoldchange column is selected as shown below:

Next, the output path is defined, and you can press the [Run] button.

After completion of the analysis the output file is opened automatically in the work space as shown below:

In this result table one new column is added, LogFoldChange, to the right of the ID column. The ID column, and all the other columns are exactly the same as they were in the input table.

16.1.2. Convert identifiers for a gene table

Single gene table

The analysis method Convert table () can be found in the tree area, on the Analyses tab in the folder Methods, subfolder Data manipulation. The complete path to this method is:

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Data%20manipulation/Convert%20table

This method changes the type of identifiers using the internal chain of BioHubs. BioHubs is an internal, proprietary database that maps the IDs of a wealth of data source to each other. For example, this method converts the Genes: Ensembl type of identifiers into Proteins: Ensembl. If a direct conversion between two selected types is impossible, this analysis will create an optimal chain of several BioHubs and use them subsequently.

The analysis input form when opened in the work space is shown below:

In the following, we will consider the input fields one by one:

Input table: Input the data table for which you wish to convert the identifiers.

Input type: Type of identifiers in the input table. This is automatically detected in the majority of cases. However, if there are two columns with different identifiers in the input table, you can manually select the identifier you wish to convert.

Output type: Type of identifiers into which you wish to convert the input type.

Species: Select human, mouse or rat, corresponding to the input table.

Numerical value treatment rule: Select one of the rules for treating the values in the numerical columns of the input table. Rule selection is important, when several rows are merged into a single one. We have to take into account that one identifier of a given type may correspond to several identifiers of another type, each of which is associated with a numerical value in the Leading Column (for this, see below). To choose which of these numerical values has to be taken into the merged row, a rule has to be defined. It is to be chosen from a drop-down menu. By default the “extreme” rule is selected, which is equivalent to the maximal value in case of positive numbers, but corresponds to the minimal value in case of negative numbers. In cases of "average", "average w/o 20% outliers" and "sum", the selected rule is applied to all numerical columns of the table.

In case of the "minimum", "maximum" and "extreme" rules a new option appears below which requests the user to select a Leading column. The chosen rule is applied then to the values in the selected Leading column (e.g. in the Leading column the maximum value is computed among all merged rows). All other numerical values of the table will be taken from that row which corresponds to the selected value in the leading column.

Output table: Path to store the resulting table in the tree.

Note that several non-trivial situations might occur during conversion:

- A single source ID matches to several target IDs. In this case the source row will be copied several times, one copy per target ID.

- A source ID doesn't match to any target ID. In this case the source row will be removed from the result.

- Several source IDs match to a single target ID. In this case two options are available:

If you have specified the leading column, only one out of all suitable source rows will be shown in the resulting table, based on the specified rule. For example, if you specified 'maximum' as a rule, the source row with maximal value in the main column will be selected from suitable rows.

If you have not specified a leading column, all the corresponding source rows will be merged together using merging rules. Non-trivial columns like 'Graph' will not be shown in the resulting table. Text columns will have all values joined into a sorted comma-separated list with duplicates removed. Numerical columns will be merged based on the selected rule. For example, if you select 'average' as a rule, then the mean value will appear in the resulting table. If your source column has an integer type, it might be changed into float.

Example: Conversion of Ensembl gene IDs to UniProt IDs

The input table with Ensembl gene IDs can be accessed via URL:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes

Fill the input fields and press [Run] as shown below:

Upon completion of the analysis the output file is opened by default as shown below:

The column ID now contains UniProt IDs. The column Ensembl ID, which was the ID column in the input table, is also present in the output table, as the second column to the right of the new ID column.

All the other columns of the input table are included in the output table as well. Numerical values are calculated according to the selected rule.

Similarly, any other table in the tree area with gene or protein identifiers can be converted into the desired type of identifiers.

Multiple gene sets

The input is a folder with several gene tables. The steps of this workflow for each individual gene table are the same as described in the section above. The same steps are performed iteratively for each of the gene tables in the input folder.

The output is a folder which contains subfolders with the results for each individual input table. The subfolders are automatically given the same names as the input tables.

16.1.3. Join tables

The analysis method Join tables () can be found in the Tree Area under the Analyses tab in the folder Methods, subfolder Data manipulation. The complete path to this method is:

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Data%20manipulation/Join%20tables

Applying the Join table method, you can join two tables together in one new table containing selected columns. Joining is performed according to ID matching from left and right tables. The result table will contain IDs present in at least one table.

The input form of this method, when opened in the work space, is shown below:

In the following, we will consider the input fields one by one:

Left Table – left (first table) for join.

Right Table – right (second table) for join.

Aggregator for numbers (expert) – Function to be used for numerical columns when several rows are merged into a single one, if the merge columns option is selected.

Output table - Name of the table where results will be saved. If a table with that name already exists it will be replaced.

If you like to join more than two tables, please see method Join several tables ().

16.1.4. Intersect tables

The analysis method Intersect tables () can be found in the Tree Area, under the Analyses tab in the folder Methods, subfolder Data manipulation. The complete path to this method is:

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Data%20manipulation/Intersect%20tables

This function allows for the identification of common rows between two tables. The intersection is performed according to IDs matching from "left" table to "right". The result is a single table that contains rows common for both input tables.

In the following, we will consider the input fields one by one:

Left Table – left (first table) for intersection.

Right Table – right (second table) for intersection.

Aggregator for numbers (expert) – Function to be used for numerical columns when several rows are merged into a single one if merge columns option is selected.

Output table - Name of the table where the results will be saved. If a table with that name already exists it will be replaced.

16.1.5. Venn diagram

With this feature you can create VENN diagrams from the input tables as well as to get tables of common and unique genes according to the sections of the VENN diagrams.  VENN diagrams are images that show all possible logical relations between the input tables. As input, two or three gene tables can be provided for which you wish to know common and unique genes. These input tables can be in any format (gene or protein IDs). The VENN diagram function can be found under the tab Analyses, in the folder Methods/Data manipulation/Venn diagrams ().

The initial form of this analysis looks as it is shown below:

When the expert options are opened, the form looks like:

To perform this analysis you can input two or three tables.

Left Table (T1), Right Table(T2) and Center table (T3). You can drag and drop the input tables which you wish to add to the VENN diagram.

Left table name, Right table name, and Center table name. These are expert options. You can specify the names of the input tables as you want to see them in the output diagram, if you want them to be different from the names of the input tables. By default the original names of the input tables will be shown in the resulting diagram.

Left-top circle color, Right-top circle color, and Center-bottom circle color. These are expert options. In these fields you can specify the colors you wish to see in the diagram. The default colors are displayed in the input form. To change them just click on the colored boxes.

Simple picture. When this box is checked-, all three circles in the resulting diagram will have the same size, no matter whether the input tables are of the same size or not. By default this option is checked. When this box is unchecked, the size of the circles will be proportional to the size of the input tables in the resulting diagram.

Output path. Specify the output path. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output path, and a new window will be opened, where you can select the location of the results folder and define its name.

Important: please define the output path within one of your Project folders.

Press [Run].

The analysis will start as shown below:

Wait till the analysis is completed.

The output is a folder comprising several tables and one diagram as shown below:

Results

Diagram opens automatically in the work space when the analysis is completed:

This picture can be exported in png, jpg, or bmp formats with the help of the Export () button on the top control panel.

For the same input files, if you specify the input table names under the expert options as L1, L2, and L3 respectively, and un-check the Simple picture box, the resulting diagram looks different, as shown below:

Please note that the size of the circles is proportional to the number of genes in the input tables.

Along with the diagram, there are several tables in the output folder.

T1, T2 and T3 are the three original input tables.

Each individual intersection is shown as a separate gene table; correspondingly, the following tables are displayed:

Rows present in all three tables,
Rows present in T1 and T2, but not in T3,
Rows present in T2 and T3, but not in T1,
Rows present in T1, but not in T2 and T3,
Rows present in T2 and T3, but not in T1,
Rows present in T2, but not in T1 and T3,
Rows present in T3, but not in T1 and T2

The tables containing rows present in all three or in two tables, also contain columns from three or two input tables, respectively. As an example, the table Rows present in T2 and T3, but not in T1, contains the columns from T2 and from T3 tables, as shown below. If the column names are the same in two input tables, “_1” is automatically added to the name for the second column.

Each of the resulting tables can be used for all operations with tables and serve as input for a number of workflows, e.g. for functional classifications, promoter analysis, pathway analysis and more.

16.2. Operations with tracks

The folder Data manipulation contains several methods allowing useful operations with tracks, among other methods. This folder can be found on the Analyses tab. Track is a set of DNA fragments or intervals with obligatory information about their chromosomal location and absolute positions of the beginning and of the end. Optionally, any additional information about the fragments can be included. Tracks are very often available in BED format. Within the geneXplain platform, tracks in the tree area are shown as (). As for the basic operations with tracks, kindly refer to the previous descriptions (2.3.3).

16.2.1. Annotate track with genes

The method Annotate track with genes () helps to add information about nearby located genes to each fragment. The input form is shown below:

Input track. Specify the input track. You can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field select element and a new window will be opened, where you can select the input track.

The input track used in this example can be found under location:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Encode%20TFBS%20CEBPB%20in%20H1-hESC%20cells/Data/CEBP%20in%20H1-hESC%20cells%20YES

This track contains 500 in vivo binding fragments for C/EBP transcription factor (Encode project).

Species. Choose human, mouse or rat from the drop-down menu.

5’ region size and 3’ region size. By default this method considers the following regions around Ensembl genes: 1000 bp in 5’ direction from TSS and 100 bp in 3’ direction from the last exon. The positions of each fragment on the input track are compared with the positions of the extended gene regions. Genes overlapping with an input fragment are considered for annotation of this fragment.

Output track. Specify the path and name to store the output track.

Having filled in the input form, launch the analysis with the [Run] button. Wait till the analysis is completed.

Results. The resulting track is automatically opened in genome browser in the work space.

In the tree area, at the location specified in the input form, you can find the resulting track, highlighted in blue on the screenshot below:

When opened as a table, it looks like this:

All columns of the input track are present, and one column is added, called Property:Genes. This newly added column is a result of an annotation of the input track with genes, and for each fragment it contains gene symbols of overlapping genes. As you can see, some of the fragments are not overlapping with any genes, and some of the fragments may be overlapping with two or even more genes. It depends on the particular fragments, their length and location as well as on the length of the gene-bound extension regions specified in the input form.

Next to each gene symbol there are gene regions specified, for example ERI1 (2 exons + intron). This means, a particular fragment overlaps two exons and one intron of the ERI1 gene.

Tip If you would like to annotate overlapping genes for all fragments in the input track, you might be interested to increase the gene-bound extension regions in the input form, and run the analysis again.

16.2.2. Intersect tracks

Track intersection provides two types of operations whose results are either the intersection itself or the difference of two tracks. In the first case, the output track consists of intervals two tracks have in common (which overlap). In the second, the output contains those intervals uniquely found in the input track.

This analysis can be used, for instance, to filter predicted binding sites for conserved regions.

The parameters can be described as follows.

Input track: The input track contains the intervals which will be available in or omitted from the output track if they overlap with intervals of the filter track.

Filter track: The filter track contains the intervals against which input intervals are tested for overlap.

Operation type: Here one can select the desired input intervals, intersection or difference.

Output track: The output track will contain the input intervals contained in the intersection of difference set.

Overlap coverage: The overlap coverage is the relative proportion of an input interval that needs to overlap with a filter interval.

Maximal uncovered flank positions: This parameter limits the number uncovered positions (sometimes called "overhanging ends"). Note that this limit is applied to each side of an input interval, not to the total number of uncovered end positions.

16.2.3. Gene set to track

The method Gene set to track () aims at creating a track corresponding to any table with Ensembl gene IDs. As with the fragments of the output track, this method takes the gene regions around TSS (transcription start sites). It is a useful method to create a track of gene promoters or upstream regions for any input gene table. The input form is shown below:

Input table. Specify the input table with Ensembl gene IDs. If your table has different IDs, you need to convert it first. Details on how to convert table identifiers are given in the Section 11.3.3. You can drag & drop the table from your project within the tree area. Alternatively, you may click on the pink field select element and a new window will be opened, where you can select the table. Here, the following table is taken as input.

You can see Ensembl gene IDs in the column ID. Such a table may contain any number of additional columns. Here, three additional columns are present, Gene description, Gene symbol and Affymetrix ID.

Species. After input of the table, the species (human, mouse or rat) is adjusted automatically. Verify the species shown in the species field.

From and To. By default this method considers the following regions around the TSS of the input genes: 1000 bp in 5’ direction and 100 bp in 3’ direction.

Output track. Specify the path and name to store the output track.

Having filled in the input form, launch the analysis with the [Run] button. Wait till the analysis is completed. The resulting track is automatically opened in genome browser in your work space.

The output track when opened as a table, is shown below:

This table contains exactly the same number of the fragments (rows) as the number of Ensembl genes in the input table. There are columns for chromosome, positions From and To, Length, Strand, and Type. The type of the fragments after this conversion is automatically assigned as misc_feature. Other columns present in the input table are all added on the right side of this table, e.g. here Affymetrix ID column.

16.2.4. Track to gene set

The method Track to gene set () aims at identifying genes located close to the fragments in the input track. Genes overlapping with at least one input fragment are considered resulting target genes. The input form is shown below:

Input track. Specify input track. You can drag & drop it from your project within the tree area. Alternatively, you may click on the pink field select element and a new window will be opened, where you can select the input track. With the [track Plus] button () you can add another track to have several tracks analyzed together.

Species. Choose human, mouse or rat species from the drop-down menu.

5’ region size and 3’ region size. By default this method considers the following regions around Ensembl genes: 1000 bp in 5’ direction from the TSS and 100 bp in 3’ direction from the last exon. The positions of each fragment on the input track are compared with the positions of the extended gene regions. Genes overlapping with at least one input fragment are considered resulting target genes.

Types of resulting columns. This analysis specifies the overlap between the extended gene regions and the fragments; such a specification can be given in several different waysanda desirable way of representation can be specified in this field. Let’s consider the options available in the drop-down list:

+ or -. This option shows the presence or absence of overlap between any of the input tracks and the specified gene regions.

Count. Returns the number of fragments overlapping with each gene.

Count in exons, Count in introns, Count in 5’, Count in 3’. When any of these options is chosen, the number of fragments overlapping with the corresponding gene regions is shown for each gene in the resulting table.

Structure. This option returns the names of gene regions overlapping with the fragment(s).

Positions. With this option, you can see the position of the 5’ end of the fragment relative to the TSS of the overlapping gene.

Schematic. The gene structure is shown schematically with exons and introns, and the overlapping fragments are displayed.

The resulting tables with all available types of representation are shown below.

Output name. Specify the path and name to store the output table with Ensembl genes.

Having filled in the input form, launch the analysis with the [Run] button. Wait till the analysis is completed. The resulting table is opened automatically in the work space. Let’s consider different variants of the resulting gene tables depending on the selected option in the input field Types of resulting column.

+ or -.

This option is especially useful when two or more tracks are added as input. In the resulting table (below) you can see the columns corresponding to each of the input tracks, here two columns.

Each row corresponds to one gene overlapping with at least one fragment in at least one of the input tracks. For example, in the table above, the gene SCYL3 is overlapping with at least one fragment in the track GSM558469_E2F1_hg19 filtered chr 1, and is not overlapping with any fragment in the track CEBP in H1-hESC cells YES.

Tip If you would like to find overlapping genes for all fragments in the input track(s), you might be interested in increasing the gene-bound extension regions on the input form, and run the analysis again.

To learn more details, e.g. how many fragments are overlapping with gene regions and with exactly which parts of particular genes, you might be interested to choose other types of the output, as shown below.

Count

For each gene, a gene symbol is given, and in the column Count you can see a number of the fragments overlapping with each gene. Here, the sorting is done by this column.

Structure

The column Structure contains the names of gene regions overlapping with the fragment(s). The table can be sorted by this column to get all genes where the fragments overlap the gene regions in focus.

Positions

The column Positions presents positions at the 5’ end of the fragment overlapping with this gene. Positions are shown relative to the TSS of the gene in each row.

Schematic

The column Schematic presents a gene schema with depicted as blue boxes. Introns, 5’ regions and 3’ regions are represented by blue lines, and the fragments on the input track by red short vertical lines. The length of the introns is calculated in logarithmic scale relative to the length of the exons, to allow for a reasonable schematic representation.

All Ensembl gene IDs are hyperlinked, and upon click on them the corresponding Ensembl gene page is opened in a new tab of the browser.

16.2.5. Process track with sites

In general, a track is a set of intervals where positions are specified that we can map on a chromosome. These track files can be visualized in a genome browser and can be used as input for various site analysis functions.

The geneXplain platform provides you with an option to modify these track files. “Process track with Sites” is a function which enables the user to enlarge/shrink sites on the track, merge overlapping sites or remove too short sites. For example an already saved track in the repository can be processed by adding sequences from Ensembl or some other database.

The initial form of this analysis looks as shown below:

Source track: Track you want to process
Sequences: Sequences to use
Enlarge sites at start: Use positive numbers to enlarge and negative to shrink
Enlarge sites at end: Use positive numbers to enlarge and negative to shrink
Merge overlapping: Checking this box merges overlapping sites into a single site. Site annotations will be lost!
Remove small sites: If checked, sites smaller then Minimal site size will be removed, otherwise they will be expanded to Minimal site size
Minimal site size: Sites shorter than the specified size will be removed from output
Output track: You should specify the path for the processed track here.

An example source track file saved in the repository to which you want to add sequences may look like this:

The track file shown provides you with the positions of promoter areas selected for analysis, as shown in columns From and To. The column Strand shows the strand of the chromosome where these promoters are located, where 1 means strand not applicable, 2 means forward strand, 3 means reverse strand, 4 means both strands. This file can be dragged and dropped on a particular chromosome opened in the genome browser to visualize its positions (see Section 16.2.3).

This Source track file can be selected as an input to “Process track with Sites”. The sequences we want to map are selected from the Ensembl database as shown below:

Using default conditions for the other parameters you can now press [Run].

The output track looks like shown below:

For comparison of the results you can click on individual chromosome sequences from both the original track and the Processed track from the Tree Area as shown below:

The detailed view of the processed track is as shown below:

100bp are added to both the sides and thus from original 19bp track, you now have a track with 219bp. This processed track can be used further for other site analysis functions.

16.2.6. Create random track

This method creates a track of randomly sampled sequence regions, also denoted as intervals, segments or subsequences. Upstream regions of genes serve as source for the random segments.

Sampling can take into account an input track in two ways. First, the lengths of output regions are sampled from lengths observed in the input track, so that the output track has a similar length distribution. This functionality can be overridden by specifying a common sequence length, in which case all sampled sequences will have the same length. Second, gene upstream regions that overlap with segments in the input track can be omitted from the sampling. Omission of overlapping upstream regions is active by default and can be switched off (see parameter description).

Specification of an input track is optional. Random seed and sequence length arguments with values less than or equal to 0 are ignored. However, if no input track is provided, the sequence length argument is required.

The input mask of the tool is shown above. The parameters are described in the following.

Input track: This is argument is optional. The input track can be supplied to obtain a random track with a similar length distribution and/or void of segments overlapping with input intervals.

Sequence source: The sequence source specifies which sequences are associated with intervals. Note that you can apply a custom source, e.g. a specifically uploaded genome. Clicking on the “Custom” option will open a new field to choose the custom sequence source.

Species: Upstream regions of genes will be compiled from the annotation for the specified species.

Standard chromosomes: If marked (default), sampling will only take into account standard chromosomes. As non-standard chromosomes, this analysis considers for instance haplotype segments.

Sequence number: This is the number of sequence regions to sample.

Sequence length: If greater than 0, this value specifies one length for all sampled sequence regions. Otherwise, an input track must be provided and random interval length will be sampled from length observed in the input track.

Allow overlap: If marked, sampled intervals are allowed to overlap with input intervals.

Output track: The path of the track with random track to be created.

Random number seed: If greater than 0, this number will be supplied as seed for the random number generator in order to be able to reproduce the sampling result.

16.2.7. Create transcript region track

This method allows for creating tracks specific for particular transcript regions, e.g. promoters, 5’ UTRs, 3’ UTRs, exons, introns.

The analysis method ‘Create transcript region track’ can be found on the Start page, under the button ‘Popular functions’.

Here it is shown how to create a track with 5’ UTR sequences starting from input transcripts. The input should be a table with Ensemble transcripts.

In the following, the input fields are shown one by one:

Input transcripts – Enter a table of Ensemble transcripts in this field. You can either drag and drop the file from the tree area or select it from the drop-down menu. In case your gene table does not have transcript information, use the ‘convert table’ function(section XXX) to convert any gene or protein table into Ensembl transcripts.

Species – Select the species of the input transcripts.

Transcript region – You can select the part of a transcript region which you wish to include in the output track. The region can be selected from 3’ UTR, 5’ UTR, promoter, intron, and exon.

As soon as you have chosen a transcript region from the drop-down menu, the following input fields are adjusted. If Promoter is selected as the transcript region, the input form becomes the following:

Promoter start – You should specify the first base of the promoter relative to the TSS.

Promoter end – Here you should specify the last base of the promoter relative to the TSS.

If 3’ UTR or 5’ UTR are selected as the transcript region, the input form looks as follows.

First/last exon as UTR - This check box can be used to select the first or last exon as UTR if it is not defined in the input transcript. By default this box is unchecked.

Fixed UTR length – The column can be used to create UTRs of fixed length. By default the method uses 300bp as the track length. Please note that actual UTRs can be very long.

Ignore CDS information – This box is checked to ignore CDS information and create fixed length UTRs. By default this box is checked.

Output path - Specify the path to store the result and indicate the name of the output track or sequences.

If Exon or Intron are selected as the transcript region, the input form is adjusted as follows:

Exon/Intron number - This field becomes active when you select Intron or Exon as the transcript region. You should specify 1, 2 ... for first, second, ... exon/intron, or -1, -2, ... for last, second last, ... exon/intron.

Output path - Specify the path to store the result and indicate the name of the output track or sequences.

Below it is shown how to create a track with 5’ UTR sequences starting from the input transcripts. The input should be a table with Ensemble transcripts.

The analysis will start as shown below:

After the run is completed, the output track is opened automatically in the work space as shown below:

You can select the sequence (chromosome) number from the drop-down arrow menu and view the corresponding track file.

The track file when viewed as a table looks like as shown below:

The resulting tracks file can be used as input for various other workflows, for example to search for TF binding sites, enriched motifs and composite modules, and others.

16.2.8. Create tissue-specific promoter track

The analysis method ‘Create tissue-specific promoter track’ can be found on the Start page, under the button ‘Popular functions’.

This method uses a set of Ensembl genes as input and extracts promoter regions by mapping it against the TSS locations defined in CAGE data in the Fantom5 (Nature 507:462–470) database (see also 19.10).

The input form is as shown below:

Input genes: Enter the set of genes or a gene table to extract transcription start sites (TSSs).

CAGE TSS database: Specify the path of the Fantom database.

Cell/Tissue condition: Once you specify the database, select the cells/tissues for which you want to create the promoter track from the drop-down menu.

From/To: Specify the promoter length relative to the TSS; by default the promoter length is from -1000 to +100 bp

TSS selection: The TSS should be selected if there are multiple TSS. By default the most active site is considered as TSS.

Substitute default: By default this box is unchecked. If checked it will substitute the gene promoter by default, if the promoter is missing in the selected condition.

Output path: Define the output file name and path in the tree area where you wish to save the Fantom5 promoter track.

For example:

The method is run using a set of upregulated genes from brain tumor as input, specifying cerebellumadult as cell tissue condition, and keeping all other conditions as default.

The input dataset can be found here:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes%20filtered%20(LogFC%3E2)

Upon completion the output file is opened in the work space and looks as shown below:

These set of promoters can be used as an input for other site analysis workflows.

16.2.9. Track statistics

The analysis method Track Statistics can be found on the Start page, under the button ‘Popular functions’ as shown below:

This method gathers various statistical information about any input track or Fastq file. This information is helpful to calculate the number of reads in a particular input file which is a pre-requisite for many workflows. The input form is shown below:

The input form parameters are as follows:

Source –Specify the type of input track that you wish to process using this method. The source can vary from Track, FastQ, Solid and CSFastQ.

FastQ file – This is a sequence file with reads in FastQ format.

CSfastq file – This is a file containing reads in color space.

If the source is Track, you have to specify the Input track. Based on the specified source, you should input the track to process using this method.

Alignment – Specify whether to align sites on the left or on the right.

In case the source is a Fastq or CSFasta file, you need to specify:

Quality encoding – This specifies how phred quality values are encoded in the FASTQ file. In most of the cases the system detects this value automatically. You may change it manually if the auto-detection worked incorrectly.

Alignment – Specify whether to align sites on the left or on the right.

Processors – This is a list of methods to gather diverse statistics:

Basic statistics – Gathers basic statistics like reads count and average read length.

Quality per base – Distribution of phred quality score along the bases.

Quality per sequence – Distribution of phred quality score among the sequences.

Nucleotide content per base – Distribution of individual nucleotides along the bases.

GC content per base – Distribution of GC along the bases.

GC content per sequence – Draws a distribution of GC content among reads.

N content per base – Distribution of 'N' along the bases.

Sequence length distribution – Calculates a distribution of read lengths and outputs them as a table and a chart.

Duplicate sequences – Calculates the rate of sequences duplication: how many sequences occur 2, 3 etc. times relative to unique sequences. This statistic is based on the first 200000 reads.

Overrepresented sequences – Looks for sequences which appear in more than 0.1% cases.

Overrepresented K-mers – Search for K-mers which are represented 3x times per sequence or 5x times per position.

Overrepresented prefixes – Search for read prefixes (starting from the read start) up toa length od 15 bp which are overrepresented in the set.

Output path – Specify the output file name and path in the tree where you want to save the output file.

After pressing ‘Run’ the method runs as shown below:

After completion of the method, the output folder is created and an HTML report opens in the workspace.

The link to an example HTML output report for an input FastQ file is here:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/RNA-Seq%20analysis%20of%20human%20esophageal%20squamous%20cell%20carcinoma%20(ESCC)%2C%20GSE32424%2C%20FASTQ%20files/Data/Fastq%20files/SRR349741.fastq%20stats/Report

The report looks as shown below:

It gives a summary of all the parameters at first glance; details can be retrieved by clicking on the respective hyperlinks.

The tool calculates each parameter and lets you know if the particular value looks ‘OK’ in the input sequence, else it gives a WARNING or ERROR

The link to an output HTML file using a track file as input is here:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/E2F1%20binding%20regions%20in%20HeLa%20cells%2C%20ChIP-Seq/Data/GSM558469_E2F1_hg19%20filtered%20chr%201%20stats/Report

16.2.10. Mutation effect analysis

This tool allows to find proteins affected by mutations. The mutation effect analysis determines the effect of a certain genomic mutation on a protein, such as synonymous, gain/loss of stop codon, frameshift or others. It accepts a list of Single Nucleotide Variations (mutations), and determines the type for each mutation.

The analysis “Mutation effect” can be found in the NGS folder of analysis methods (analyses/Methods/NGS/Mutation effect) or on the start page button ‘Popular functions’ under the section ‘Operations with tracks’.

Step 1. Open the analysis form from the Start page. It will open in the main Work Space and looks as shown below:

Step 2. The Input track is a track file with a list of single nucleotide variations (mutations) and should be in vcf format.

One input example is here on the platform:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Chronic%20Myeloid%20Leukemia%20Patient%20Genotyping/Data/SNP_indels.vcf

Open the track file as a table, and for each variation point you can see several columns with genomic position, chromosome, alternative nucleotide etc., as shown below.

Step 2. Verify the Sequences source and use the drop-down menu for different Ensembl genome annotations of human, mouse and rat, as shown below.

Alternatively, you can choose 'Custom' from the same menu if you would like to specify another genome, e.g. a particular patient genome imported into the platform before. As soon as the option 'Custom' is chosen, an additional field, Sequence collection, automatically appears on the input form (screenshot below), and you can specify the sequences location manually.

Step 3. Specify the path and name of the Output track.

After completion the output track file (SNP_indels.vcf with mutation effect) is opened by default in the work space.

This resulting track can be found in the Examples folder under the URL: http://platform.genexplain.com/bioumlweb/#de=data/Examples/Chronic%20Myeloid%20Leukemia%20Patient%20Genotyping/Data/SNP_indels.vcf%20with%20mutation%20effect

The output track is created from the input track by adding the single column 'MutationEffect' with the determined mutation type. Note that a single mutation can affect multiple proteins with distinct consequences. In this case the MutationEffect column contains a list of mutation types separated by comma. This analysis uses the Ensembl database for protein genomic annotations.

The upper example highlighted by the red box has ID=1 in the track. The columns From and To define the positions of the affected position within the genome on chromosome 1 (Sequence (chromosome) name). The column Length shows the length of the position, here 1. The Property:AltAllele exhibits the nucleotide in the mutated sequence and Property:RefAllele gives the nucleotide of the reference genome at the indicated position. The Property:MutationEffect shows NONSYNOMYMOUS and means a single nucleotide change which will cause an amino acid change.

Possible Mutation effect types are:

1. SYNONYNYMOUS_SNV - a single nucleotide change that does not cause an amino acid change

2. NONSYNONYMOUS_SNV - a single nucleotide change that causes an amino acid change

3. STOP_GAIN - a nonsynonymous SNV, frameshift insertion/deletion, nonframeshift insertion/deletion or block substitution that leads to the creation of a stop codon at the variant site. For frameshift mutations, the creation of a stop codon downstream of the variant will not be counted as "stopgain"!

4. STOP_LOSS - a nonsynonymous SNV, frameshift insertion/deletion, nonframeshift insertion/deletion or block substitution that leads to the immediate elimination of a stop codon at the variant site

5. NONFRAMESHIFT_INSERTION - an insertion of 3 or multiples of 3 nucleotides that does not cause frameshift changes in the protein coding sequence

6. NONFRAMESHIFT_DELETION - a deletion of 3 or mutliples of 3 nucleotides that does not cause frameshift changes in the protein coding sequence

7. NONFRAMESHIFT_BLOCK_SUBSTITUTION - a block substitution of one or more nucleotides that does not cause frameshift changes in the protein coding sequence

8. FRAMESHIFT_INSERTION - an insertion of one or more nucleotides that causes frameshift changes in the protein coding sequence

9. FRAMESHIFT_DELETION - a deletion of one or more nucleotides that causes frameshift changes in the protein coding sequence

10. FRAMESHIFT_BLOCK_SUBSTITUTION - a block substitution of one or more nucleotides that causes frameshift changes in the protein coding sequence

11. NOTHING - coding sequence is not changed

Tip. If you would like to not show the single nucleotide changes that do not cause an amino acid change, use Property_MutationEffect != 'NOTHING' for filtering.

The output track can be opened in the genome browser as shown in the picture below.

16.2.11. Remove overlapping sites

This method removes overlapping sites from any track and constructs a subset of the input track with no sites overlap in the output track. It can be found under the tab Analyses, in the folder Methods/Data manipulation/Remove overlapping sites. Here the default input form is shown:

In the following, we will consider the input fields one by one.

Input track. You can drag & drop the input track from your project within the tree area. Alternatively, you may click on the pink field “select element” and a new window will open, where you can select the track.

Sequence source. Specify the reference genome or select Custom to specify the sequences location manually.

Independent strands. Select whether you want to handle sites from different strands independently; by default it is unchecked, and strands are not handled independently.

Overlapping site selection mode. Choose one mode from the drop-down menu for the selection of sites.

The One longest, One shortest, One with best value and One random modes will select a single site from the set of overlapping sites. Other modes can select more than one site from the set of overlapping sites, but the resulting sites will not overlap with each other. The Largest set mode constructs a non-overlapping set of sites with the maximum number of sites. The Longest set mode constructs a non-overlapping set with the largest total length of sites. The Set of best sites mode iteratively selects the best site and removes sites that overlap with the best site, then selects the best from the remaining sites and removes those which overlap with the best site, and so on until no sites remain. The Most 5’ and Most 3’ select the site located at the 5’ or 3’ end.

Output track. Define where the track with the result should be located in your project tree. You can do so by clicking on the pink box (select element) in the field, and a new window will open, where you can select the location of the resulting track and define its name.

Press the [Run] button and wait until the method is completed.

For this example, all further steps are demonstrated with the following input track:

data/Examples/Sample data/Data/Overlapping sites/example overlaps

The track looks as shown below:

We perform several runs with different site selection modes. All different output tracks are shown below:

16.3. Statistical methods

These methods have been described in detail in other sections, where they contribute essentially to certain workflows. Please, refer to these sections as specified below.

Principal Component Analysis (PCA)

See Section 10.1.4

LIMMA (Linear Models for MicroArrays)

See Section 4.2.1

EBarrays

See Section 4.2.2

16.3.1. Compare analysis results

This tool compares P-values in two analysis results. Analyses of interest are, for instance, binding sites or GO term enrichment results. The comparison can help to reveal items that show different enrichment across certain conditions.

This analysis method can be found on the Start page under the button “Popular functions”.

The input form looks as shown below:

In the following, the input fields are shown one by one:

First analysis result - Enter the first input table which you wish to compare having a P-value column.

Note: Currently the method does not compare FDR or log (P-value) columns.

P-value column - From the drop-down menu select the P-value column which will be used for comparison.

Second analysis result - Enter the second input table which you wish to compare having a P-value column.

P-value column - Select the P-value column from the drop-down menu.

Output folder - Specify the path to store the result and indicate the name of the output folder.

Here, two enrichment results are taken for comparison from the Examples folder; the analysis will start as shown below:

The output consists of two files: the Analysis comparison plot and the Analysis comparison Table.

The Analysis comparison table as shown below lists all P-values, absolute differences, difference P-values and estimated FDR.

The output columns are explained below:

First P-value (-log): This column contains the –log P-values calculated from the P-values of the first input table.

Second P-value (-log): This column contains the –log p-values calculated from the P-values of the second input table

Difference: Column 2 – Column 1

Difference P-value: This column contains the calculated P-values for the results in column 3 (Difference)

Difference FDR: This column contains the calculated FDR values for the results in column 3 (Difference)

The analysis comparison plot as shown below is a scatter plot of P-values on the log-scale together with the diagonal and the difference cutoffs at FDR < 0.05.

Gene or protein lists

All functions collected in this area have already been described in other sections. Kindly refer to those parts of this User Guide, as specified in the following:

Load gene or protein list

See Chapter 3

Discover functional enrichment

See Section 10.3

Analyze networks

See Section 5.1

Analyze regulatory regions

See Section 10.4

Find drug targets

See Chapter 11

Complete list of workflows

When you open this Area, the following complete listing of workflows available in the geneXplain platform will show up:

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For detailed explanation of their function and how to operate them, please refer to the previous chapters.

Working with the different databases

Working with one of the databases listed in the Tree Area involves several, frequently all four areas of your screen. In this chapter, we will briefly demonstrate how to operate the individual databases.

19.1. Biomodels

Biomodels is a source of mathematical models suitable for simulating biological processes. They have been compiled by the BioModels.Net project (http://www.biomodels.net/). These models are stored in the geneXplain platform as database Biomodels, subdirectory Diagrams, along with their graphical representations, parameters, simulation default values, etc. The present version is release 25, comprising 426 curated and 522 non-curated diagrams

You can access the Biomodels contents by browsing or searching. When you browse the list of models in the Tree Area (Databases > Biomodels > Diagrams), please note that only 50 of either 421 curated or 433 non-curated diagrams are displayed at once. Selecting one of the models is by double-clicking on the respective name.

Searching for a model starts from the Search tab in the Info Box. You may enter, for instance, glycolysis and start the search with the button . You will get back 75 entries, the contents displayed under the tab Search result in the Operations Field, the information given being essentially self-explaining: A short description of the model including references is given. You may open any model by a click on the corresponding Accession number in the last column.

Having opened one of the diagrams by either method, the network schema will appear in the Work Space as well as in the Operations Field, tab Overview; see Section 23.3 for editing these diagrams. The tab Layout provides a number of options for changing the layout style of the diagram (see 21.2); none of these options will change the diagram contents. Clicking on an individual node will show information about this component and its role in this model within the Info Box (tab Info).

Many Biomodels have been optimized for dynamic simulation. Open, for instance, the subdirectory Diagrams and double-click Goldbeter1991_MinMitOscil_ExplInact. The diagram will open in the Work Space, the tab Info in the Info Box will show some details about the model, database links for the individual components. In the Operations Field, under the tab “My description”, a detailed description of the model is given; it shows the original reference, its abstract and the PubMed link. Further information about how the model was constructed is added.

Under the tabs Simulation, you will find the default settings for the simulation, which can be changed before launching the simulation (). The simulation results will be graphically displayed in a new window, which can be saved as image using the Export button () in the Control Panel (E, see Chapter 2). For this, a number of formats are available.

19.2. Biopath

Biopath is a collection of molecular pathways, manually annotated from original scientific publications and comprising biological models and diagrams. A more detailed description can be found in the Info Box, tab Info, after clicking on the term Biopath in the Tree Area, Databases.

User accessible entities are stored in the subdirectory Diagrams (presently 571).

This way, you may browse the database contents, receiving information about the individual entities in the Info Box, after a single click with the left mouse button on the corresponding entity. The Diagrams can be opened by double-click with the left mouse button, or by opening a context menu with the right mouse button and selecting “Open diagram”. These diagram contain their components listed underneath as nodes () and edges (), for which additional information can be retrieved in the same way as for the whole diagram. Many of them may be assigned to a hierarchy of compartments ().

If you select the Biopath database for a search, you may enter a gene name into the field of the Search tab in the Info Box (try, for instance, AKT1). In the Operations Field, under the tab Search result, you will find the following hit:

It shows the molecule encoded by the gene AKT1 being involved in the insulin pathway. The hyperlinked Accession number here opens a pathway diagram from the Biopath database, which includes insulin and AKT1 as well as the path between them. The pathway is shown in full in the Work Space; an overview is depicted under the “Overview” tab of the Operations Field:

The part of the diagram that is displayed in the Work Space is framed by the dotted blue line in the Overview, which can be shifted with the mouse, the Work Space adapting accordingly. You may also shift your mouse pointer over the Work Space: It turns into a hand, indicating that you can now shift the diagram section in the display by pressing the left mouse button and moving the mouse accordingly. The dotted blue rectangle in the “Overview” tab of the Operations Field will move accordingly.

Information about an individual node can be obtained in the Info Box after double-click on the corresponding symbol in the Work Space.

In the diagram, green arrows represent conversions, magenta edges catalytic effects. To facilitate overview in complex diagrams, individual edges are highlighted in light blue on mouse-over.

19.3. Ensembl

The Ensembl database provides annotation of genes from the Ensembl genome databases (http://www.ensembl.org/index.html).

Presently, there are human, mouse and rat Ensembl data included in the geneXplain platform, separately listed on the Databases tab. The following versions are provided:

Human:

Genome build hg19, version 72.37

Genome build hg19, version 65.37

Genome build hg18, version 61.37f

Mouse:

Genome build mm10, version 72.38

Genome build mm9, version 65.37

Rat:

Genome build rn5, version 71.5

Genome build rn4, version 65.34

In the subdirectories “Sequences/Chromosomes …” the individual chromosomes of the corresponding genome are stored. A double-click on an individual chromosome symbol opens the corresponding object with its annotation in the genomes browser of the geneXplain platform. Here, you can move the mouse pointer along the sequence to select a certain position. With the buttons and (zoom-out and zoom-in, resp.), you can go up to a level where you have the whole chromosome displayed or down to a level where you see the individual nucleotides. The most extreme views can also be directly selected with the buttons (overview) and (detail; see below). A moderate view is provided as “default” ().

At an intermediate resolution of, e.g., human chromosome 1, you see individual genes highlighted in the Work Space:

The alignment of genes and other genomic data against a reference genome can be viewed as data tracks in genome browser. The GeneTrack shows the localization of primary transcripts including intron/exon structure and direction of transcription. Scrolling backwards and forward through the genome can be done using the arrowheads next to the track name, or with the buttons “Page backward” () and “Page forward”
(), respectively.

Information about individual genes is displayed in the Info Box after clicking on the respective gene symbol in the Work Space; on the Sites tab of the Operations Field, a detailed list of all functional sites

The KaryotypeTrack shows on which arm and in which karyotypic band(s) of the chromosome the present view is located.

RepeatTrack and VariationTrack comprise large numbers of sites scattered all over the chromosome. In a resolution like the one given above, only summarizing figures can be given. When zooming in, at a certain level of resolution, localization of individual repeats and their names will appear, and similarly sequence variations will show up in the Work Space, and they are listed in the Operations Field, under the Sites tab. Note that information about individual sites can be invoked in the Info Box only after a resolution has been adjusted that allows the display of their names in the Work Space, since the names are the clickable items.

The color scheme for the display in the Work Space can be changed in the Operations Field; here, on the Tracks tab, you can adjust the settings of the genome browser:

Individual tracks can be removed from the display in the Work Space (e.g. by de-selecting them on this tab). The same way, they can be brought back. Another possibility is to open the subdirectory Tracks in the Tree Area, where all available tracks are listed with the symbol . Just drag-and-drop the track of interest to the Work Space will render the corresponding data amenable to the browser.

The Ensembl database sections are searchable in the same way as described before: Just click on the name of the respective database in the Tree Area, so that it receives a light-blue background and its path appears on the Search tab of the Info Box. Enter your search term, launch the search by pressing , and find your Search results on the respective tab on the Operations Field. Among the multiple hits, the one with the perfect match will be highlighted in bold.

19.4. Gene Ontology (GO)

Contents from Gene Ontology are imported into the geneXplain platform and are updated regularly. The present version is 06.2013.

GO is searchable in the same way as described before: Just click on the name GO in the Tree Area, so that it receives a light-blue background and its path appears on the Search tab of the Info Box. Enter your search term, launch the search by pressing , and find your Search results on the respective tab on the Operations Field. When clicking on linked descriptions, additional information will appear in the Info Box, Info tab.

19.5. GeneWays

GeneWays is a database about genes and their functional interactions. The underlying data (version 7.0) have been retrieved from the original scientific literature by a sophisticated text mining system applied to more than 360,000 full text papers and of more than eight million publication abstracts [Iossifov I., Rodriguez-Esteban R., Mayzus I., Millen K.J., and Rzhetsky A. Looking at cerebellar malformations through text-mined interactomes of mice and humans. PLoS Comput Biol. 2009, 5:e1000559. PubMed PMID: 19893633].

The directory GeneWays/Data has two subdirectories: Genes and Reactions. When you click on any gene entry, information about the gene will be retrieved from the Entrez database. When you click on any reaction entry, you will find reaction title, links to Entrez for both incoming and outgoing molecules and the link to the PubMed entries of the corresponding publication in the Info Box.

A single click on the term GeneWays in the Tree Area / Databases suffices to indicate this data source in the Info Box, tab Search. You can insert your search term (e.g., a gene symbol) into the field underneath. Clicking on the icon launches the search. The search routine scans for exact matches, but use of wildcards is possible. Thus, searching for elk* returns results for elk1, elk2p1, elk3, and elk4.

The results will be shown in the Operations Field, under the tab Search result. For instance, when searching in GeneWays for JAG1, the following result table will be displayed:

The search term is highlighted in bold.

You may recognize that the numbers in the last column (Accession) are hyperlinked. When you click on them, information about the gene will be retrieved from the Entrez database at NCBI, displayed in a new window or tab, depending on your browser settings. Rows referring to reactions show a different type of accession number, they are linked to a PubMed entry of the corresponding publication.

19.6. Reactome

Reactome is a database that provides information on biological objects such as proteins, protein complexes, reactions etc. It is an open-source, open access, manually curated and peer-reviewed pathway database. Pathway annotations are authored by expert biologists in collaboration with the Reactome editorial staff, and cross-referenced to many bioinformatics databases. The contents of Reactome are copyright © 2003-2010 Cold Spring Harbor Laboratory (CSHL), Ontario Institute for Cancer Research (OICR) and the European Bioinformatics Institute (EBI).

So far 5222 diagrams have been imported from version 45 of this database into the geneXplain platform. They can be used as graphical and editable schemata. In the Tree Area, all components are listed in each diagram subdirectory.

19.7. TRANSPATH®

TRANSPATH® is BIOBASE’s database about signal transduction and metabolic pathways in human and the most important model organisms, mouse and rat. Its particular structure, which models signaling components and reactions in multiple hierarchical ways, makes TRANSPATH® a unique resource for both encyclopedic and modeling purposes.

Under the geneXplain platform, the TRANSPATH directory contains the following folders:

Data

Diagrams

Dictionaries

Layouts

Under “Data”, browsable lists of objects, like genes and molecules, and of processes, like reactions and pathways, are stored. As usual, detailed database contents are displayed in the Info Box upon selecting individual entities from the lists by mouse click.

As for other databases, the Search tab in the Info Box provides an easy search function to retrieve individual entities from the resource. The search results are displayed in the Operations Field, amenable to further activities.

19.8. GTRD

GTRD (Gene Transcription Regulation Database) is a database of weight matrices to recognize transcription factor binding sites. The GTRD library of weight matrices consists of new matrices derived from ChIP-seq data by applying our multiple alignment method IPSmatrix. The IPSmatrix represents a modification of the previously published alignment method [Ananko E.A. et al. Recognition of interferon-inducible sites, promoters, and enhancers. BMC Bioinformatics 2007, 8:56. PMID: 17309789]. Each individual matrix is derived from an analysis of the corresponding set of TF-binding regions obtained from a set of raw ChIP-seq data. It is assumed that:

some TF-binding regions do not contain TF-binding sites;

strand orientation and exact location of TF-binding sites in TF-binding regions are unknown.

The IPSmatrix relies on the Gibbs sampling principle and our conception of the Individual Probability Score (IPS) where IPS represents the extension of the common matrix score. In addition to the cores of TF-binding sites, the IPSs take advantage of nucleotide contents of both flanks of site cores.

When several ChIP-seq datasets are available for a single TF, several matrices were calculated from which the optimal matrix was selected by comparing the corresponding Receiver Operator Characteristic (ROC) curves.

In the GTRD directory of the geneXplain platform, the folder “matrices” provides the list of matrices generated in the way described. You may also wish to include at this point a matrix collection provided by the UniProbe database.

Under “profiles”, the matrices are stored along with thresholds that are to be applied when using the matrices for TFBS recognition. All these “profiles” are grouped according to three different thresholds (strong, moderate and weak).

The directory “tracks” provides ChIP-seq (or ChIP-chip) data, e.g. from GEO (Gene Expression Omnibus database at NCBI) in a format that renders them suitable for visualization in the Genome Browser.

Under “views”, available matrices (PWMs), underlying ChIP-seq experiments or the TFs themselves can be browsed. They are arranged according to the most recent classification of human TFs (http://www.edgar-wingender.de/huTF_classification.html) and extended to mouse and rat homologs.

In cases where several ChIP-seq experiments are available to deduce matrices, all respective logo plots are given when navigating down to the level of individual TFs.

19.9. TRANSFAC®

TRANSFAC® is the most comprehensive database on eukaryotic transcription factors (TFs), their genomic DNA-binding sites and DNA-binding profiles. It is a commercial database, maintained and distributed by BIOBASE GmbH as well as, in most countries, also offered for licensing by geneXplain. A valid license is thus required to access the contents of this database.

In the TRANSFAC folder labeled according to the respective release, there is the data subdirectory with the areas “classifications”, “factor”, “gene”, “matrix”, “profiles” and “site”:

Under “classifications”, the class definitions of DNA-binding domains of eukaryotic TFs are listed with their accession numbers (such as C0001 for “zinc finger”). Clicking on any item invokes further information in the Info Box. The same holds for taxa (of biological species), and for the TF classification as it was basically established in 1999.

By clicking on the folder “factor”, you can browse all TF accession numbers, with detailed information about any selected TF appearing in the Info Box. The same applies to the folder “matrix”, which lists all PWMs available in the actual database release, and the Info Box displaying the matrix and the corresponding logo plot.

Under “profiles”, matrix collections are given for TFs that are known to play a role in a certain biological context, as defined by the profile name.

The TRANSFAC folder “Dictionaries” contains mostly files with links to other databases for internal use of the program.

19.10. Fantom5 cell-type and tissue-specific transcription start sites

The Fantom5 databases (Fantom5-Cell and Fantom5-Tissue) provide cell- and tissue-type specific transcription start site (TSS) annotations derived from CAGE measurements of the functional annotation of the mammalian genome 5 (FANTOM5) project (FANTOM Consortium, Nature 507:462-470, 2014). The databases cover 171 cell types and 121 tissue types (plus one type of default TSSs from Ensembl), respectively.

TSSs were inferred by a two-step process. The data for inference were the number of Cage TSSs (CTSSs) that had been mapped experimentally to genomic locations. First, a set of CTSS clusters was obtained by a sliding-window method similar to the one described by Strbenac et al. (BMC Genomics 5:S9, 2013). As illustrated in the figure below, this method used estimates of the rate of CTSS “hits” per nucleotide to calculate a statistical significance for the putative TSS (red line) on the basis of the Poisson distribution. For this, local background CTSS rates were calculated from flanking regions (blue areas) of a putative TSS (red line), excluding its direct vicinity (red area).

In the second step, we further refined the initial CTSS clusters using a Hidden Markov Model (HMM). The HMM modelled CTSS hits in TSS regions as well as in the genomic background by negative binomial distributions whose parameters, as well as their transitions, were estimated from the first set of CTSS clusters.

The HMM-based CTSS clusters from corresponding cell or tissue samples (the Fantom5 project collected tissue or cell samples from several donors) were eventually grouped into sets of overlapping clusters and annotated with high CTSS mark as consensus TSS.

The derived databases are now available in the geneXplain platform denoted as Fantom5-Cell and Fantom5-Tissue. Condition-specific TSSs can be extracted for gene sets using the tool named “Create tissue-specific promoter track”.

19.11. Other data sets

In addition to databases, there are several datasets available within the platform that might be interesting to make use of in particular analyses. The datasets are available under the tab Data in the folder Public, within the project Data sets.

19.11.1. DrugExpress – genome-wide transcriptional signatures of drug response

The folder DrugExpress contains sets of genes that significantly change their expression in response to treatment by different drugs. It originates from the Connectivity Map (also known as cmap) project developed at the Broad Institute, USA, http://www.broadinstitute.org/cmap/.

In this collection, we identified 321 compounds that could be mapped to Drugbank (http://www.drugbank.ca/), and also have known target genes in Drugbank. The following steps were performed:

Data normalization with affy package in R with parameters: method="quantiles", bgcorrect.method="rma", pmcorrect.method="pmonly", summary.method="liwong".

Gene IDs were converted into Ensembl IDs using Convert table analysis.

Up- and down-regulated genes were identified applying the following criteria: (LogFoldChange >= 0.7) and (p_value <= 0.05) for up-regulated genes, and (LogFoldChange <= -0.7) and (p_value <= 0.05) for down-regulated genes. LogFoldChange is a logarithm of fold change with base 2 of gene expression in treated versus untreated cells.

The DrugExpress folder contains 321 gene tables for up-regulated and 321 gene tables for down-regulated genes for each chemical compound or drug, altogether 642 gene tables. The names of the files correspond to the names of the chemical compounds. Along with the individual files, there are 17 subfolders, in which the files are grouped according to the classification of the respective drugs (for example, “adrenergic antagonist”).

Search with DrugExpress Database

To search within the DrugExpress database you have to switch from Default mode to DrugExpress, mode using the drop-down menu at the top right corner (see picture below). The input mask of the search page appears automatically in the main Workspace.

Browsing the summary drug table can be done by clicking the link “Browse summary table”. The table with 321 entries, corresponding to the represented drugs, will be automatically opened as shown below. The columns of the table are sorted alphabetically. The links to the following external databases are provided:

Drug Bank (http://www.drugbank.ca/drugs),
PubChem Compound (https://www.ncbi.nlm.nih.gov/pccompound),
ChEBI (http://www.ebi.ac.uk/chebi/init.do).

The next columns provide links to the gene lists of all up- and down-regulated genes in response to drug treatment. The Known targets for every drug is shown in the next column. A Structure of the drug is visible in the last column of the table.

Let’s close the summary table to return to the search form of the DrugExpress database. To search by the drug name, you can type in your search term and press .

Let’s consider this example. Searching for “acid” returns 15 search results in the database with all columns described above.

To search within the DrugExpress database by the gene name please type in your search term and press button.

Example. Searching for Caspase10 (casp10) returns 5 entries. The resulting table contains the following columns, the Accession numbers of the linked drug, Drug Name, ENSEMBL Gene Id, Gene Symbol, Affymetrix Id, P_value and the Fold Change of gene expression in the drug treatment experiment.

To find out which other genes are regulated in response to the same drug, follow the link from the drug accession number. This opens the whole table with genes that significantly change their expression in response to the treatment.

Functional analysis applying DrugExpress, the input form

The classification of drugs can be also applied to any gene signatures, for example to the genes differentially expressed under certain disease conditions. Mapping of disease-specific gene signatures to the gene signatures of drug responses and the similarities identified may result in intriguing suggestions which drugs can be potentially used for disease treatment.

The DrugExpress dataset can be used by two methods, Functional classification and Enrichment analysis, as a user-specific ontology, the so-called Repository folder. Any input gene or protein table will be classified using the DrugExpress data. 17 subfolders and 642 gene tables will be used as categories for the classification of the input table. We will exemplify this in the following.

Step 1. Open Functional classification analysis (), under the tab Analyses, folder Methods, subfolder Functional classification.

Step 2. Select your input gene table with Ensembl IDs and the corresponding species in the input fields, Source data set and Species, respectively.

Please note that if the table you plan to classify has other IDs, you first need to convert it into a table with Ensembl IDs. This can be done using the Convert table () function located at analyses/Methods/Data manipulation/Convert table.

For this example, the following input table is used:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Upregulated%20Ensembl%20genes

Step 3. In the Classification field, select the option Repository folder from the drop-down menu, as shown below.

Step 4. As soon as the option Repository folder is selected, two additional fields will automatically appear in the input form, Path to classification root and Reference collection. The input form with the added fields is shown below.

Step 5. Mark the DrugExpress folder in the field Path to classification root.

Step 6. Leave the fields Minimal hits to group and P-value threshold as per default, and specify location and name for the output table in the field Result name.

Step 7. Press the [Run] button and wait till the analysis is completed.

Results of the functional classification using DrugExpress

As a result, a table is generated with all columns as usual for Functional classification results, shown below. You can find the resulting table at:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Upregulated%20Ensembl%20genes%20DrugExpress

Each row corresponds to one ontological category, which in this case is one of the subfolders or tables from the DrugExpress folder. The names of the tables and subfolders are shown in the ID column. For each row several parameters are calculated, including the expected number of hits, actual number of hits, p-values, as well as hit names.

All IDs are internally hyperlinked, and with a click on each name the corresponding gene table will be opened.

For this example, we classified genes up-regulated in the Ewing brain tumor versus neuroblastoma, using DrugExpress gene signatures. Each resulting line identifies a statistically significant similarity of genes up-regulated in Ewing brain tumor versus neuroblastoma, with the gene signature in response to a given drug or chemical substance. For example, 52 genes from the input list are classified into the group metronidazole Dn, which means that these 52 genes are known to be down-regulated by metronidazole; the p-value of this classification is 6.2862E-12. In the next lines, 9 genes from the input list are classified into the group resveratrol Up, which means these 9 genes are known to be up-regulated by resveratrol; the p-value of this classification is 4.7654E-4 This might be an interesting hint, because resveratrol has a potential anticancer activity.

Tip. Any user-created ontology or collection of tables can be used for the classification of input gene lists, in a similar way as it has been shown for DrugExpress. To do this, you need to create a corresponding folder in your project, which can contain a hierarchy of subfolders as well. Importantly, each table in such a folder should have Ensembl IDs. Each subfolder and individual table will be used as a separate classification category, and will result in a separate line in the resulting table.

Description of analysis methods

20.1. Sequence analysis methods

20.1.1. SNP matching

NGS DNA sequencing is a powerful analytical method to discover novel SNPs and detect known SNPs. The geneXplain platform provides a unique analysis method termed SNP matching. Using this method and an SNP table (derived after sequencing) as input, the corresponding SNP loci are mapped to Ensembl genes so that you can get an annotated SNP table as output.

By default the SNP matching tool looks as shown below:

Example:

An example data file to be used as an input table for SNP matching may look like this:

In this table, ID is the SNP identifier, CHR is the chromosome, P is some p-value, and POS_B36 is the genomic position of a given SNP in NCBI-build36 human genome.

You can save this table in the repository and input the saved table in the SNP matching tool. The tool yields three output files as shown below:

Output table 1 (ALC_SNPs_annotated)

Output table 2 (ALC_SNPs_genes)

Output table 3 (ALC_SNPs_track)

The output tables can be further used for any other analysis of the geneXplain platform.

20.1.2. Site search on gene set

This feature provides you with an option to search for putative transcription factor binding sites (TFBS) in a set of genes. As input for the analysis you are supposed to indicate two gene sets, Yes (e.g. differentially expressed in an experiment, test set) and No (set of background genes, control set) as well as positional range relative to the TSS and a collection of predefined weight matrices with a particular threshold (profile).

The initial form of this analysis looks as it is shown below:

To perform this analysis you have to input two datasets:

Yes set: This is the set of genes that you want to analyze, for example these can be genes the expression of which has changed in an experiment (test set). Tables with Ensembl gene IDs can be used as input in this column. In case you have a file with different identifiers, you can first use the Convert table function in the Data manipulation folder.

No set: This is the set of background genes (control set). This again should be a gene table with Ensembl gene IDs as input.

These two datasets might be taken from the output tables of previous analyses; see, e.g., Detect differentially expressed genes (Section 10.2).

Species: A pull down menu allows you to select the biological species according to the species of your Yes and No gene sets. Currently, the analysis can be done for human, mouse and rat genes.

From: You can indicate the gene region where the search for putative TFBS should be done. Here, you enter the 5’ border of the region, relative to the transcription start site (TSS) as it is annotated in the current version of Ensembl.

To: Here, you enter the 3’ border of the region relative to the TSS.

Profile: This is a predefined set of positional weight matrices with a particular threshold. By default, the TRANSFAC® profile vertebrate_non_redundant_minSUM is applied.
You can use other available TRANSFAC® profiles. Alternatively, you can apply profiles in the GTRD database. Currently, there are three profiles included in the GTRD database; they depend on the threshold for matrices: strong threshold (with an Individual Probability Score (IPS) higher than 6), moderate threshold (with IPS higher than 5), and weak threshold (with IPS higher than 4).

You also have the option to import matrices and profiles into the geneXplain platform and use them for your further analysis.

To perform site search following steps are recommended:

Step 1. Input Yes set from the tree. You can drag-and-drop as usual. Here, the set of genes from the Example folder is used as input Yes set, highlighted blue on the screenshot below:

Step 2. Input pre-saved No set. You can drag-and-drop as usual. Here, the set of human housekeeping genes from the Example folder is used as input No set, highlighted blue on the screenshot below:

Step 3. Select the species and the promoter length. By default the promoter length is set as -1000 to +100 relative to the TSS. In this example, the range
-500 to 100 is selected.

Step 4. Select the TRANSFAC® or GTRD profile from the pre-saved profiles in the tool. In this example the default TRANSFAC® profile vertebrate_non_redundant_minSUM is used:

Step 5. Identify the output path. Define where the folder with the results should be located in your project tree. You can do so by clicking on the pink field “select element” in the field Output path, and a new window will be opened, where you can select the location of the results folder and define its name.

Important: please define the output path within one of your Project folders.

Press [Run].

The analysis will start as shown below:

Wait until the analysis is complete as shown by the progress bar. The output of Site search on gene set contains one table and six tracks:

summary table ( ), yes promoters (), no promoters (), yes sites (), no sites (), yes sites optimized () and no sites optimized ().

The summary table is automatically opened in a new tab of the geneXplain platform when the analysis is completed. An example of the summary table is shown below:

Each row summarizes the information for one PWM. For each selected matrix, the columns Yes density per 1000bp and No density per 1000bp show the number of matches normalized per 1000 bp length for the sequences in the input Yes set and input No set, respectively. The Column Yes-No ratio is the ratio of the first two columns. Only matrices with a Yes-No ratio higher than 1 are included in the summary table. The higher the Yes-No ratio, the higher is the enrichment of matches for the respective matrix in the Yes set. The matrix cutoff values as they are calculated by the program at the optimization step are shown in the column Model cutoff, and the last column shows the p-value of the corresponding event.

Tracks Yes promoters and No promoters

These two files resulting from the “Site search on gene set” analysis represent promoters of the corresponding set, as a track (). The track files might be used for site visualization or for further analyses, e.g. Construct Composite Modules, Construct IPS CisModule.

The track file Yes promoters when opened in the work space is shown below. The track file No promoters has a similar structure.

This table lists the positions of the promoter areas selected for the analysis on particular chromosomes, as shown in the columns From and To. The column Strand shows the strand on which each particular promoter is located. This track can be dragged and dropped on a particular chromosome opened in the genome browser to visualize the localizations of the promoters as discussed in Section 19.3.

Yes (No) sites optimized track. The file Yes sites visualizes those putative sites that are over-represented in the promoters of the Yes set versus the No set as they are located in the promoters of the Yes set. Putative TFBSs are shown as a track . Scores of the putative sites are optimized by the algorithm.

This track is a list of all putative TFBS found in one analysis. Each row presents details for each individual match for every PWM. The columns Sequence (chromosome) name, From, To, Length and Strand show, correspondingly, genomic location of the match including chromosome number, start and end positions, strand and length of the match. The column Type contains information about the type of the elements, in this case all matches are considered as “TF binding site”. Further columns keep information about PWM producing each match (column Property: matrix) as well as score for the whole matrix (column Property: score). The column Property: siteModel contains the identifier for the corresponding site model, which is the matrix together with a cutoff applied (and in the example shown is identical to the matrix identifier).

Yes (No) sites tracks are very similar in structure. The major difference is that these tracks include putative binding sites before the cutoff optimization, and thus they contain more sites.

These track files can be used as an input for other functions, for example sites can be visualized on chromosomes. For visualization details please refer to the tip in the Section 7.2.2.

Visualization of TFBS for individual genes and for individual matrices

Having the summary table opened in the work space, you can select different rows and apply a couple of different functions with the help of the buttons on the top menu bar. These five buttons are marked by red oval in the figure below.

To get a visualization of TFBS for individual genes, the button should be applied on the selected matrices. After click on this button, two new files will be saved in the tree area, a table and a track , as shown below. The table is automatically opened in the Work Space.

Let’s consider this table in more detail. Each row corresponds to one individual gene.

The column ID presents the Ensembl ID for each gene, and the gene symbol is shown in the column Symbol. The column Sites view shows a schematic representation for each gene, where blue bars correspond to gene starts and coding regions, and TFBSs for different matrices are shown by arrows of different colors. The column Total count shows the number of TFBSs for all matrices together in the promoter of each particular gene. The next columns are named as matrices in the summary table and represent the number of TFBSs for each matrix in each particular gene.

On the picture above the table is sorted by the column Total count, and on the top we can see those genes that contain the highest total number of sites. This table can be sorted by different columns corresponding to individual matrices, and then on the top you will see those genes that contain the highest number of sites for the matrix in focus.

The TFBS color schema can be customized. For this, open the tab „Site colors“ in the Operation Field in the bottom right area of the tool (figure below). You can see the default colors for different matrices, and can adjust them by clicking on each color box.

This table can be exported in tab-separated format (txt) or comma-separated format (csv).

The second file, a track , has the same structure as described above for other track files. For visualization details please refer to the tip in Section 7.2.2.

20.1.3. Creation of customized profiles

"Profile" is a term used for a collection of positional weight matrices (PWMs) with a particular threshold (or cutoff), also referred to as “site models”. The geneXplain platform provides an option to create profiles from a table of site models or from any gene table that contains some transcription factor genes. The newly created customized profiles can be further used for analyses of regulatory regions.

There are two possibilities to create a user-specific profile.

Create profile from gene table

This option supports you in creating a new profile from any gene table. The resulting profile contains site models linked to the genes encoding the corresponding transcription factors in the input gene set. This option can be accessed via the URL:

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Site%20analysis/Create%20profile%20from%20gene%20table

Create profile from table

Here you can create a profile from a table of matrices. This option can be accessed via the URL:

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Site%20analysis/Create%20profile%20from%20site%20model%20table

20.1.3.1. Create profile from gene table

This function creates a new profile from any gene table. The resulting profile contains site models linked to the transcription factors in the input gene set.

The input form when opened in the work space is shown below:

In the following, we will consider each of the input fields.

Gene set: Input any gene table here for which you intend to design a profile. The algorithm automatically identifies transcription factor genes in the input table and the matrices linked to these factors in the TRANSFAC® database. These matrices will be put into the newly created profile.

Further steps are demonstrated with the table of genes available in one of the platform examples present in the Tree Area. The example file can be accessed using the URL:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Genes%2C%20fold%20change%20and%20p-value%2C%20non-filtered

Species: Select the biological species corresponding to the input gene set from the drop-down menu.

Reference profile: Specify one of the TRANSFAC® profiles available. Cutoffs for the corresponding matrices will be copied from the selected reference TRANSFAC® profile into the newly created profile.

To select the reference profile click on the box of this field and a new window will be opened. Scroll towards the desired profile and click on it so that the name of the selected profile is displayed in the field “Name”. When the selection is done, press [Ok].

Please note that this analysis method works only for the TRANSFAC® profiles.

Output path: Specify the path to store the result and indicate the name for the new profile.

Having filled all the input fields, launch the analysis with the [Run] button. The process will start as shown below:

After completion of the analysis the output profile is opened automatically as shown below:

Let us now have a look into the newly created profile.

Each row presents the information for one site model. In the column Name the name of the site model is given which here is the same as for the matrix. In the column Matrix name the name for the positional weight matrix is present. For each site model, a cutoff adapted from the “Reference profile” is shown in the column Cutoff.
According to the TRANSFAC® standard, the core part is specified for each matrix. The core is represented by the 5 consecutive most conserved nucleotides. The columns Core cutoff, Core start and Core length provide details about the core of each matrix. In the last column the matrix logo is shown.

In the Tree Area, the newly created profile has the symbol , the same symbol as for all other profiles, and is ready to use for the analysis of regulatory regions.

Suggestion when to use “Create profile from gene table”

If you plan to do a promoter analysis for differentially expressed genes from a particular experiment, you might be interested in creating a profile from a table of genes expressed under the same conditions. For example, you might take one of the tables resulting from the workflows “Detect differentially expressed genes…” as input gene table for profile creation.

The example table is a list of non-filtered genes from the workflow “Detect differentially expressed genes…”:
http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Experiment%20normalized%20(RMA)%20(Differentially%20expressed%20genes%20Affy)/Genes%2C%20fold%20change%20and%20p-value%2C%20non-filtered

A profile created from this gene table can be found here:
http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Genes%2C%20fold%20change%20and%20p-value%2C%20non-filtered%20profile

Such a profile contains matrices for the transcription factors expressed under the same experimental conditions, and thus it might be reasonable to apply it for the promoter analysis of genes up-regulated or down-regulated in this experiment.

20.1.3.2. Create profile from site model table

This function creates a profile from the table of site models. For example, a new profile can be created from the summary table resulting from the workflow “Analyze promoters (TRANSFAC®)” or “Upstream analysis (TRANSFAC® and TRANSPATH®)”.

The analysis input form can be found in the Tree Area, under the Analyses tab, in the folder Methods/Site analysis. The opened input form is shown below.

Let us consider the individual input fields:

Input table: Input a table with matrices for which you intend to design a profile. The input table should contain TRANSFAC® matrix IDs as row names. Such a table can be the result of a site search analysis.

In the following, further steps are demonstrated with a table of site models from one of the examples. The example file can be accessed using the URL:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Analyze%20Promoters/Upregulated%20(filtered)_Transfac/Site%20search%20-1000%20100/summary

Reference profile: In this field, you indicate the profile where the cutoffs for the new profile should be taken from. It is filled automatically if the input table is a result of a site search analysis or derived from it, and it corresponds to the profile used for site search. You also have an option to manually select the reference profile from the available TRANSFAC® profiles.

Cutoffs column: In this field you specify which column of the input table should be considered for the cutoff values. It is filled automatically if the input table is the result of a site search optimization analysis. It is advisable to use "(none)" to leave the cutoffs as in the reference profile.

Output profile: Specify the path to store the result and indicate the name for the new profile.

To launch the analysis, fill the input fields and press [Run]. The process will start as shown below:

After completion of the analysis the output profile is opened automatically as shown below:

An example profile created from this gene table can be found here:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Brain%20Tumor%20GSE1825%2C%20Affymetrix%20HG-U133A%20microarray/Data/Ewing%20Family%20Tumor%20versus%20Neuroblastoma/Analyze%20Promoters/summary%20profile

Each row summarizes the information for one site model. In the column Name the name of the site model is given, which in the majority of cases is the same as for the matrix. In the column Matrix name the name for the positional weight matrix is present. For each site model, cutoff is shown in the column Cutoff. According to the TRANSFAC® standard, the core part is specified for each matrix. The core is represented by the 5 consecutive most conserved nucleotides. The columns Core cutoff, Core start and Core length give details about the core of each matrix. In the last column the matrix logo of each matrix is shown.

The new created profile has the symbol , the same symbol as for all other profiles, and is ready to use for the analysis of regulatory regions.

Suggestion when to use “Create profile from table”

Creating a new profile from a table of site models (matrices) might be very useful e.g. if you plan to focus the analysis on those matrices shown to be over-represented upon the first run of promoter analysis and plan further to construct composite promoter models.

For this, you first run a site search with optimization, e.g. the workflow “Analyze promoters (TRANSFAC®)” or “Upstream analysis (TRANSFAC® and TRANSPATH®)”. Then, you can use the table summary as an input to construct your specific profile.

It will contain only those site models (matrices) for which hits are over-represented in your genes of interest versus the background set, which is a specific subset of the TRANSFAC® matrix library. Having such a profile specific for your genes of interest, you can run it on the same set of genes without optimization, and then use the results as input to construct composite promoter models.

20.1.3.3. Create profile from matrix library

This tool can set score cutoffs for an entire matrix library and store a corresponding profile, which in turn can be applied binding site analyses.

The figure below shows the default input mask. There are two general ways of setting score cutoffs, either by P-value (the default) or as one cutoff value that will be applied to all matrices.

The input parameters are as follows.

Input matrix library: Selection of the matrix library. All matrices of the library will be present in the profile (“Output profile”).

Core-cutoff: The cutoff for the matrix core, which is the five consecutive matrix position which are most selective for certain nucleotides. When the matrix cutoff is defined by a P-value, the core cutoff will be 0 for all matrices.

Template for cutoffs: The P-value threshold according to which the score cutoff for each matrix will be set. Selecting the “Custom…” option presents a new field to set one common cutoff for all matrices of the library.

Nucleotide distribution template: The distribution of nucleotides on which P-value calculations are based. Selecting the “Custom…” option enables custom setting of individual base frequencies.

Output profile: The path for the output profile.

An profile can be created as follows.

Step 1. Input the matrix library. As usual, you can drag-and-drop. Here we use the TRANSFAC® 2013.1 library:

Step 2. Edit other input parameters as shown in the figure above. The output profile path needs to be chosen in a writable directory, e.g. one of your projects.

Clicking the [Run] button will invoke the analysis. A part of the resulting profile is shown below:

20.1.4. Search for enriched TFBSs

The platform comprises tools that are dedicated to discovering types of binding sites enriched in a set of sequences. They are named “Search for enriched TFBSs (genes)” and “Search for enriched TFBSs (tracks)”. Both apply a common core algorithm to gene promoters or tracks, respectively. The “Search for enriched TFBSs”-tools are accessible in the “Analysis” tab under “analyses/Methods/Site Analysis”.

Dedication to discovering enriched types of binding sites means that these methods won’t bother with storing predicted binding sites and they also do not require predictions as input. Their sole output is a relatively small table summarizing the enrichment detected for PWM models in a sequence set of interest. As a result, the “Search for enriched TFBSs”-tools are often considerably faster than solutions that have to read and write binding site information. In addition, their outputs consume only the disk space needed to present the results of analyzing binding site enrichment. Hence, a “Search for enriched TFBSs” is for you if you wish to gain insight into enriched binding sites quickly and in an uncomplicated way.

20.1.4.1. Search for enriched TFBSs (genes)

Let us begin with the analysis for gene promoters. The figure below depicts the input mask of the analysis tool. The parameters are similar to those used by “Site search on gene set” and are described in the following.

Yes set: This is the set of genes that you want to analyze, for example these can be genes with altered expression. The program accepts genes specified by Ensembl gene identifiers. Note that the “Convert table” functionality in the “Data manipulation” folder can map other identifiers to the required Ensembl genes.

No set: This is the set of background genes (control set), which also need to be specified by Ensemble gene IDs.

These two datasets might be taken from the output tables of previous analyses; see, e.g., “Detect differentially expressed genes” (Section 10.2).

Species: This option specifies the biological species of Yes and No gene sets. Currently, the method is applicable to human, mouse or rat genes.

From and To: These values define the length and location of promoter regions (upstream and downstream) relative to the transcription start site (TSS). Respective sequence regions are extracted for each Yes and No gene according to Ensembl annotation.

Input motif profile: The profile lists the PWMs (motifs) to be used for binding site prediction together with a score cutoff. By default, this field is set to the last profiled set in your workspace. Note that cutoffs in the profile are ignored, because the “Search for enriched TFBSs” determines a starting threshold specified in the Initial cutoff field.

Output path: In this field you select a path in the workspace to store the output table.

Initial cutoff: This cutoff determines the starting point of the analysis with respect to the score threshold to predict binding sites. The choice is expressed as frequency of predicted sites per base. By default, the analysis begins with a frequency of 5 sites in 100 bases (0.05). From the initial cutoff the algorithm iterates over higher cutoffs and eventually reports the one that resulted in optimal enrichment of binding sites in the Yes set. This is done separately for each PWM.

The steps of an analysis can be described as follows:

Step 1. Input Yes set from the tree. As usual, you can drag-and-drop. Here, the set of genes from the Example folder is used as input Yes set, highlighted blue on the screenshot below:

Step 2. Input No set (drag-and-drop). Our example uses the set of “Non-changed Ensembl genes”:

Step 3. Select the TRANSFAC® or GTRD profile from the available profiles. In this example, we select the TRANSFAC® 2013.1 profile named “vertebrates”:

Step 4. Edit the output path (highlighted green in the figure above). After setting the Yes gene set, a default output path is suggested. The Example folder may however not be writable for your account requiring selection of an alternative such as one of your own projects. A different selection can be made easily by clicking on the field.

We keep the defaults for promoter range (starting from 1000th base upstream to the 100th base downstream of the TSS) and initial cutoff.

Clicking the [Run] button will invoke the analysis. The summary table is automatically opened in a new tab when the analysis is completed. Here is a part of the output for our example:

Each row of the output table represents the result for one PWM from the input profile. Fold enrichment (FE) values quantify the enrichment of binding sites in the Yes sequence set as a whole (Adj. site FE) and of Yes sequences with at least one binding site (Adj. seq FE). The FE is an odds ratio that compares the ratio of Yes sites to No sites with the ratio of Yes sequences to No sequences. Unlike in other tools the FE values are statistically corrected (adjusted) quantities that report a value below the actually observed ratio according to the 99% confidence interval. This correction takes into account the underlying site numbers and sequence numbers and penalizes enrichment values that are based on only few binding site occurrences, e.g. at a high score threshold. The output table is sorted by the adjusted Site FE by default.

The statistical significance of enrichment is further assessed by one-tailed binomial test and Fisher tests P-values, for which False Discovery Rates (FDRs) are reported. The Site FDR is based on binomial test P-values calculated for the number of binding site in Yes and No set. The Seq FDR column contains FDRs for Fisher test P-values for the number of sequences with at one site in Yes and No sets.

Additional columns are available via the “Columns” tab of the lower-right panel. Each column is also accompanied by a concise description and can be included into the table presentation upon demand.

20.1.4.2. Search for enriched TFBSs (tracks)

The “tracks” variant of the “Search for enriched TFBSs” can be applied to sequence tracks signified by the symbol (). For instance, a track may contain genomic intervals identified by a ChIP-seq experiment.

The analysis uses the following parameters:

Yes set: This is the track that you want to analyze, for example these can be ChIP-seq intervals bound a transcription factor.

No set: This is the set of background intervals (control set).

Sequence source: Both Yes and No track need to refer to a common source, such as a genome, as specified by this parameter. Note that you can apply a custom source, e.g. a specifically uploaded genome. Clicking on the “Custom” option will open a new field to choose the custom sequence source.

Input motif profile: The profile lists the PWMs (motifs) to be used for binding site prediction together with a score cutoff. By default, this field is set to the last profiled set in your workspace. Note that cutoffs in the profile are ignored, because the “Search for enriched TFBSs” determines a starting threshold specified in the Initial cutoff field.

Output path: In this field you select a path in the workspace to store the output table.

Initial cutoff: This cutoff determines the starting point of the analysis with respect to the score threshold to predict binding sites. The choice is expressed as frequency of predicted sites per base. By default, the analysis begins with a frequency of 5 sites in 100 bases (0.05). From the initial cutoff the algorithm iterates over higher cutoffs and eventually reports the one that resulted in optimal enrichment of binding sites in the Yes set. This is done separately for each PWM.

The platform is provides an out-of-the-box example for this tool under “data/Examples/ Encode TFBS CEBPB in H1-hESC cells”. The ChIP-seq experiment targeted CEBPB binding sites in H1-hESC cells. The steps of an analysis can be described as follows:

Step 1. Input the Yes set from the tree. As usual, you can drag-and-drop. The YES set contains the 500 most significant peaks:

Step 2. Input the No set (drag-and-drop). The NO set contains 1000 random intervals from promoter regions with the same length distribution as the 500 YES intervals:

Step 3. Select the TRANSFAC® or GTRD profile from the available profiles. In this example, we select the TRANSFAC® 2013.1 profile named “vertebrates”:

Step 4. Edit the output path. After setting the Yes gene set, a default output path is suggested. The Example folder may however not be writable for your account requiring selection of an alternative such as one of your own projects. A different selection can be made easily by clicking on the field.

Clicking the [Run] button will invoke the analysis. The summary table is automatically opened in a new tab when the analysis is completed. A part of the output for our example is shown below. Please refer to “Search for enriched TFBSs (genes)” for further description.

20.1.5. Construct composite modules

Composite modules are combinations of several TFBSs that are found together in a set of regulatory sequences. We search for such combinations of TF binding sites that are overrepresented in the regulatory sequences under study compared to a background set of sequences. The search for composite modules is performed using an in-house implementation of a genetic algorithm. As input for the genetic algorithm we take the output of a site search analysis.

There are two individual analysis functions available with the same symbol ; they are different with respect to the type of sequences where the search for composite modules is done, and correspondingly with respect to the format of the input data.

Construct composite modules analysis works on the promoter sequences specified relative to TSS in the set of genes. As input, it takes the results of the Site search on gene set analysis function.

Construct composite modules on tracks works with any DNA sequences specified by their absolute genomic positions, and is very often applied for the analysis of ChIP-seq fragments. As input, it takes the results of the Site search on track analysis function.

Both analysis functions can be found in the geneXplain platform online under the path

http://platform.genexplain.com/bioumlweb/#de=analyses/Methods/Site%20analysis

20.1.5.1. Construct composite modules

This analysis function enables the identification of combinations of several TFBSs in the promoters of the genes under study (Yes-set). The resulting composite module differentiates the Yes-set from a background set (No-set).

Before starting this analysis, you need to perform Site search on gene set with your selected Yes-set, No-set and a specified profile of matrices. If you are interested in finding site models for particular TFs, and see them eventually in the resulting composite modules, you need to be sure that such matrices are present in the selected profile. You can use one of the available TRANSFAC® profiles, or alternatively you can construct a customized profile; for details please see Section 20.1.3.3.

The input form for this analysis is shown below:

Step 1. Specify Site search result. This is the input field for the analysis, where you specify the results of the site search. The input field is marked by the symbol , which means that the input data set should have the same symbol.

In the example below, we will use an input data set, which you can find in the geneXplain platform online under the following path:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Construct composite modules/Upregulated Ensembl genes LogFoldChange >1.2 sites -500..100, profile 0.001

As you can see, this is a folder with the results of the Site search on gene set. If you single-click on this folder in the tree area it will be highlighted in blue, and in the Info box you can find a description of all the details about Yes-set, No-set, profile, promoter regions applied to get these results (cf. screenshot below).

You can drag & drop the name of the folder Upregulated Ensembl genes LogFoldChange >1.2 sites -500..100, profile 0.001 into the input field, or you can select it in the tree in the pop-up window if you click on the pink box of the input field.

Step 2. Specify Model parameters. You can specify the number of elements in the hierarchical structure of the desired composite module. Details and explanations on how to do this are explained below in Section 9.5.3 Hierarchical structure of the composite modules.

Step 3. Specify Output path. Specify a location for the results in your project in the tree area. The resulting folder will be marked by the same icon as the analysis,.

Results are described below in Section 20.1.5.4.

20.1.5.2. Construct composite modules on tracks

This analysis is designed for identifiying combinations of several TFBSs in DNA sequences specified by their genomic positions (tracks). An example of a track that is very often used is a set of the ChIP-seq data. The resulting composite module differentiates between a Yes-track and a background (No-track).

Before starting this analysis, you need to perform Site search on track with your selected Yes-set, No-set and specified profile of matrices. If you are interested in seeing the site models for particular TFs in the resulting composite modules, you have to make sure that such matrices are present in the selected profile. You can use one of the available TRANSFAC® profiles, or alternatively you can construct a customized profile; for details please see Section 20.1.3.

The input form for this analysis is shown below:

Step 1. Experiment track is the input field for the Yes-track, or track under study.

Step 2. Control track is the input field for the No-track, or background track.

Step 3. Model parameters. You can specify the number of elements in the hierarchical structure of the desired composite module. You may find details and explanations on how to do this below in Section 9.5.3 “Hierarchical structure of the composite modules”.

Step 4. Output path. Specify a location for the results in your project in the tree area. The resulting folder will be marked by the same icon as the analysis: .

Results are described below in Section 20.1.5.4.

Note. This analysis is the central part the workflow ChIP-Seq - Identify composite modules on peaks (TRANSFAC®), and it might be more convenient to use the workflow instead of the individual analysis.

20.1.5.3. Hierarchical structure of the composite modules

Composite modules may have a complex hierarchical structure consisting of two levels: site models and modules. The highest hierarchical level contains several modules and corresponds to the promoter model.

The first level, site model, corresponds to the individual site model, often based on one PWM. Names of the site models are often the same as the matrix names (in case the site models are based on a library of matrices). The site models are taken from the profile that was used in the site search. In the resulting schemas the site models are shown by blue boxes, for instance:

Within these boxes, there are two values below the site model. The first value is the threshold value for the score of the respective site model, which is determined by the genetic algorithm during the optimization process (here it is equal to 0.81); in some cases this value is equal to 0.0, which means that the original threshold value given in the profile was found by the algorithm to be the optimal one. The second value, in this example N=2, is the maximum number of best found individual matches (sites) for this site model which are taken into account for calculating the score of the module.

The next level, module, may contain several site models, shown within the light brown boxes:

The module is characterized by its width, the average length of DNA window containing matches for the mentioned site models. In the example, the module width is 237 bp. In the resulting schemas modules are shown in green boxes, and they are numbered, e.g. Module 1, Module 2, ….

In the input form you can define the complexity of the promoter model to be constructed by specifying the number of units of each level: number of modules, number of site models, and also the minimum and maximum numbers of individual sites to be considered. In order to illustrate how to specify these parameters, let’s consider three examples of resulting modules depending on the input parameters.

Example 1.

In the picture below (left part) you can see the composite module resulting from the performed analysis. On the right side of the picture the input form with specified parameters is shown.

We can see that Min modules (minimum number of modules) and Max modules (maximum number of modules) is 1, and correspondingly there is just one module in the resulting picture, Module 1, highlighted by the red circle on both the resulting schema and the input parameters.

The blue circle highlights the parameters Min models (minimum number of site models within one module) and Max models (maximum number of site models within one module) in the input form. As we can see, the number of site models is set to vary from 4 to 12. Correspondingly, in the resulting schema (left part above) there are 11 site models (blue boxes) identified by the algorithm.

The third parameter, Min sites to account (minimum number of individual sites for each site model to be considered) and Max sites to account (maximum number of individual sites for each site model to be considered), is highlighted by a green circle. As we can see, this parameter is set to vary from 1 to 3, and correspondingly in the resulting schema, for the different matrices we can see N=1 or N=2 or N=3.

Example 2.

In this example, the number of modules (red circles) is specified from 2 to 3, and correspondingly the resulting promoter model contains three modules, Module 1, Module 2, and Module 3 (picture below, left part, red circles).

The number of site models is specified in the input form as from 2 to 2, which means that we are going to search for pairs of individual site models. In agreement with the input parameters, in the resulting schema we can see each module containing two site models highlighted by blue circles.

Example 3.

In this example, the number of modules (red circles) is specified from 2 to 5, and correspondingly the resulting promoter model consists of four modules selected by the algorithm, Module 1, Module 2, Module 3 and Module 4 (picture below, left part, red circles).

The number of site models is specified in the input form as from 2 to 3, which means that we would like to find either pairs or triplets of individual site models. In agreement with the input parameters, three out of four modules contain three site models (highlighted by blue circles within Module 1), and one module, Module 3, contains two site models.

Based on these three examples you can specify input parameters for the number of modules, site models, and individual sites, depending on what resulting promoter model you would like to get.

Site models in focus

There are situations when researchers would like to focus on particular TFs and would like to find out with what other TFs they may form composite modules. Site models that must be present in the resulting modules, are referred to as site models in focus. In the expert options menu, under Score calculation parameters, there is a field Site models in focus. As soon as the site search results are submitted to the input field, all site models from the profile used before for site search are now available for selection via the drop-down menu, as shown on the screenshot below.

In this example, we can see that V$AP1_Q2_01 is selected. Using the Ctrl button, several matrices from the list can be selected.

As soon as the site model is selected, two new fields will appear just below the field Site models in focus (screenshot below).

All modules contain site model in focus. When this box is checked, all the resulting modules must contain specified site models. When it is unchecked, at least one of the resulting modules must have a specified site model, and other may have them too, but not necessarily.

Focused sequences percent. In the drop-down menu, you can specify the minimum percentage of the Yes sequences (genes, promoters) that must contain modules with the specified site models in focus.

20.1.5.4. Visualization and interpretation of the results

Let us consider the results of the Construct composite module analysis obtained for the following input data set, which you can find in the geneXplain platform online under the following path:

data/Examples/Brain Tumor GSE1825, Affymetrix HG-U133A microarray/Data/Ewing Family Tumor versus Neuroblastoma/Construct composite modules/Upregulated Ensembl genes LogFoldChange >1.2 sites -500..100, profile 0.001

Input parameters used were the following:

As result, a new folder is generated containing two tables, two tracks, and one histogram, as shown below.

Model visualization in Yes set

This table represents the primary results of the analysis, and shows the visualization of the identified composite modules in the promoters of the Yes set.

Each row in this table corresponds to one gene of the Yes set, and for each gene the Ensembl ID and the gene symbol are shown in the two first columns. The column Model displays a symbolic map of the gene promoter taken for the analysis, in this case -500/+100 relative to the TSS. Arrows of different colors correspond to individual TFBSs, and a gradient in grey corresponds to the statistical density of the identified composite modules. The most intensive grey color corresponds to the center of a composite module. Each individual TFBS on this map is clickable, and upon a click information is displayed in the Info box (bottom left corner in the tool). As an example, one blue arrow is selected on the promoter of the top gene in the screenshot above, and for this selected TFBS the following details are shown in Info box:

The last column in the table, Score, shows a score calculated for each promoter depending on the number of modules, site models, sites, their scores and other statistical parameters. The higher the score for a promoter, the better the differentiation of this promoter from the promoters of the No set. The column Score is used for default sorting of the table, with the highest scores on top.

Having opened the table Model visualization on Yes set, you can see the schematic representation of the hierarchical structure of the identified composite module as well as a comprehensive set of its statistical characteristics at the bottom part of the tool, under My description tab, as shown on the screenshot below.

Yes track

The Yes track provides essential information about the regulation of individual promoters and is therefore important to be included in the visualization of individual promoters by the genome browser.

The schematic visualization can be comfortably extended to a more detailed visualization for each individual promoter.

To do this, open the table Model visualization on Yes set, select one row in the table with a mouse click, and open the tab Genome browser in the Operation Field (bottom right part of the tool),.

For a selected promoter, you can see a more detailed map, including the names of the matrices and the numbers of individual modules, M1 through M4. Each element of this interactive map has a corresponding check box. Unchecked elements will not be displayed on the map. De-selection is applied simultaneously to both the detailed view of one promoter, and the table with the schematic representation of all promoters.

To adjust colors for individual matrices, you can open the next tab to the right, called Site colors, and change the colors to your liking by clicking on the individual colored box, as shown below.

Model visualization on No set and No track

The table Model visualization on No set shows a visualization of the identified composite modules in the promoters of the No set.

The structure of this table is the same as that of the Model visualization on Yes set table, described above.

The function of the No track is to provide a possibility for a detailed visualization of no promoters in a way similar to that of the Yes track.

Histogram

The distribution of scores for individual promoters is shown as a histogram, where the promoter score value is shown on X axis and the percentage of promoters (% sequences) having this score is shown on the Y axis.

This histogram can be further interpreted applying the statistical characteristics described above.

The center, a vertical grey line, corresponds to the average score value and is equal to 3.44 in this example. Promoters from the No set with a score above 3.44 are shown in the histogram as blue bars to the right of the center, and they are referred to as false positives. In this example, the false positive rate is 16.82 %.

Promoters from the Yes set with a score below 3.44 are shown in the histogram as red bars to the left of the center, and they are referred to as false negatives. In this example, the false negative rate is 23.42 %.

A visual analysis of the histogram suggests that the Yes promoters with a score above 4.5 are very well separated from the No promoters, which means that for this part of the promoters the composite model constructed is most suitable. In this example there are 38 promoters with the score value >4.5; they can be saved as a separate gene set, and for them the model obtained works best.

20.1.5.5. Score calculation of the composite models

The figure below demonstrates the calculation of the score value for the composite modules in the promoter sequences. The TSS is shown as a thin arrow on the right side of the figure. Four thick arrows exemplify four sites found in this promoter. The color of the arrows exemplifies the site model which these sites belong to (three site models – red, green and blue).

A promoter model consists of K modules. The score of each module Mk (Score(Mk), k = 1, …, K) is calculated according to this formula:

Here,

Site Score (t,i) is the site score for the sites found in the promoter, which is calculated by the Match algorithm.

mt – the number of sites of the site model t found in the promoter.

Tk – the number of site models in the module Mk, and

The final promoter score is calculated as the sum of the module scores Mk.

Standard deviation () of the normal distribution is subject of optimization by the genetic algorithm and represents the width of the module in the output of the composite module analysis.

20.2. About the GSEA analysis and the interpretation of the results

Schematic description of the GSEA algorithm

The Gene Set Enrichment Analysis (GSEA) is a method that determines whether an independently defined set of genes shows a statistically significant enrichment either in up-regulated or down-regulated genes http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html

Independently defined sets of genes might be groups of genes linked to Gene Ontology terms, or to TRANSPATH® pathways, or to Reactome pathways, etc.

As input for the GSEA, we can use either normalized tables after normalization of the microarray raw data, or a table with genes that contains a column with pre-calculated expression values. The algorithm of the GSEA can be schematically presented in three steps:

All genes are sorted by Fold Change, from highly up-regulated to highly down-regulated. In such a sorted table, each gene is given a rank, which is just the number of the line where this gene is located in the sorted table. The most highly up-regulated gene has the rank “1”, and so on.

The algorithm takes a set of genes linked to one biological term (e.g. to a particular GO category) and maps each gene in this set to the sorted (ranked) table. Genes in some categories might be distributed randomly over the ranked table; some are among the up-regulated, some among the down-regulated, and some among the non-changed genes. For some of the GO categories, a majority of the genes are among the up-regulated. These GO categories are the most interesting for a biological interpretation.

Several statistical parameters, ES (Enrichment Score), NES (Normalized Enrichment Score), Rank at max, nominal p-value, FDR, for each GO category are calculated. ES and NES reflect how significantly genes in each particular GO category are enriched (over-represented) among up-regulated genes.

Structure of the resulting tables with enriched ontological terms

The resulting tables with enriched ontological categories contain twelve columns. Here, to have a better resolution of the screenshots, one table is shown in two parts. This is the result of a GSEA using the PROTEOME™ biological process.

Each row presents details about one enriched ontological term. The column ID comprises the identifier of the ontological category, here identifiers of Gene Ontology biological process terms. These identifiers are hyperlinked to the page http://www.ebi.ac.uk/QuickGO/ where you can get further information about this ontological term. In case of enrichment by PROTEOME™ diseases the disease identifiers are hyperlinked to http://ctdbase.org/

The columns Title and Group size contain further details about the ontological terms, the title and the number of genes linked to this term in the corresponding database, here in PROTEOME™.

The column Expected hits shows the number of genes expected to fall into this category by random chance, based on the size of the input set and the size of the category.

Five columns, Nominal P-value, ES (Enrichment Score), Rank at max, NES (Normalized Enrichment Score), and FDR, show corresponding statistics of the results. For more details about each of these statistics, please refer to http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html, under the section Interpreting GSEA results/GSEA statistics.

The column Number of hits shows how many genes from the input set fall into the enrichment group; these genes are explicitly listed in the column Hit names. As the lists can get quite long, only a few genes are shown by default in each row. To get the full list, press (more). The column Plot and View diagrams are described in detail in a separate sub-section below.

Further possible actions with the enrichment results.

The tables with the GSEA results can be exported in txt, csv or html formats. To export, you can apply the Export button in the top control menu, highlighted by the dark-blue oval.

One or several rows of the table can be selected with shift-click , shown in blue color in the screenshot below. Having selected one or several rows in this way, you can save hits of these rows in a separate gene table, with the button Save hits in the top control menu, highlighted by the red oval. Such genes tables can be analyzed further, e.g. to find master regulatory molecules in the networks, and to identify transcription factors that might commonly regulate these genes.

You can save the selected rows in a separate table, with the button Save selected rows as… in the top control menu, highlighted by the green oval.

Enrichment plots

As described above, there is a column Plot with the buttons View in each row in the GSEA resulting tables.

Let’s open a visualization plot for the category defense response.

X-axis

On this line, all (human in this example) genes present in the input table are ranked from 1 to approximately 23,000 according to their LogFoldChange. The numbers we see on the X-axis, 2,500; 5,000 etc., are the ranks of the genes.

At the left end of the line X, up-regulated genes are located, and down-regulated genes at the right end . The smaller the rank of a gene is, the higher is its up-regulation.

Y-axis, right

This axis shows the values of the LogFoldChange. Positive values correspond to up-regulated genes, negative values to down-regulated genes.

Blue plot

This is a distribution of LogFoldChange values among all (human) genes.

Y-axis, left

This axis shows the values of the ES parameter (also known in statistics as Kolmogorov-Smirnov score). Positive values correspond to up-regulated genes, negative values to down-regulated genes.

Red plot

This is a distribution of ES values for a particular GO category over all (human) genes. ES is calculated by summing up on the previous values, so the most interesting parts for us are those where the plot is coming up.

Interpretation of the plots

For interpretation, let’s look at the red plot. The most interesting parts are those where the red line is ascending. In this example plot, let’s follow the red line from its left end to its maximum.

The starting point for the red line is 0 on the Kolmogorov-Smirnov score axis.
Next, the algorithm takes each gene within the category "defense response", and looks what the LogFoldChange for this gene is. If gene is up-regulated, the red line grows.Then the algorithm takes the next gene from "GDP binding" and again looks what the LogFoldChange for the 2d gene is. If it is also up-regulated, the red line grows further. This is called incremental growth: the closer the next gene is to the previous one on the X-axis, the more pronounced is the growth of the red line.

Let’s add an auxiliary line (here, the green dotted line) from the maximum point of the red plot down to the X-axis. The intersection of the green dotted line with X-axis corresponds to the rank of the gene at maximum enrichment. This value is exactly what in statistics is called Rank at max, and it is shown in a dedicated column in the tables with the GSEA results. In the plot shown above, the green line crosses the X-axis at approximately 4,500. To know this value exactly, look at the table, and in the column Rank at max for this category, you can find the number 4328.

To describe this plot, we can say:

“Genes belonging to the GO category defense response are enriched among the top 4328 up-regulated genes”.

Operations field

In the Operations Field (D in the figure of Section 2.1) a number of essential functions to operate the geneXplain platform are provided on a number of tabs. How many and which tabs are shown depends very much on the context.

Please note that not all tabs are always visible due to space constraints. In these cases, double arrowheads left and right of the tabs indicate that there are additional ones, reachable by clicking on these double arrowheads.

The function of the individual tabs will be explained in more detail in those sections where their effect is part of a certain operation. In general, the icon initiates the corresponding activity within the Operations Field, whereas applies to the results generated in the Operations Field of the Work Space.

21.1. Changing the table structure in the Operations Field

Having opened a table in the Work Space, e.g. by double clicking on its name in the Tree Area, it is possible to edit its structure in the Operations Field under the tab Columns.

For instance, if you have opened a table with data about Enrichment GO Molecular Mechanism (resulting from having run a GSEA), this field may look like this:

Recognizably, you can change the column headers, the data type in the column, or its (usually hidden) descriptions. You may add an Expression, which may be a mathematical formula, formulated in Java script; you find detailed explanations for this when you press the Edit key () next to this field. In the last column, you can specify which columns are visible or shall be hidden (unmarking a column here does NOT delete it, it hides it from the currently displayed table).

If you hide a column by unmarking it, you have to refresh the Work Space by pressing the button in the control panel right on top of the Operations Field. Here, you can also add new () columns. Before removing a column with the button , you have to mark it by clicking somewhere in the background of the line specifying this column; the selected item will be highlighted in blue. But be careful: Deleting it from the table will irrevocably erase the column including all its contents!

21.2. Changing the layout

Under the Layout tab, you can change the layout of the diagram. First, you can select one of the following layout schemes:

Hierarchical layout (default)

Orthogonal layout

Force directed layout

Cross cost grid layout

Grid layout

When you have selected another layout type, you have to press the “Prepare layout” button (), showing the new layout at the right of the same window in the Operation Field. Pressing “Apply layout” () transfer the new layout to the Work Space.

Here are examples how the different layouts look like; the example is the Caspase 12 pathway and has been taken from the database Integrated models (Int_casp12_module12):

Some of the re-layouting options may take considerable time. If you want to interrupt the process, press the “Stop layout” button ().

The layout that has been applied to the Work Space can be further edited manually. A single click on a node (component or reaction node) highlights it; it can be shifted now by mouse movement, or can be deleted (right mouse button click opens a context menu with the option “Remove”). The results of this manual editing can be saved to the Operation Field () so that they will be retained for the following work.

Please, be careful: changes in your own diagrams are automatically saved! You can even close the diagram in the Work Space, but still can undo your last changes with the Undo button (). After logout, all your changes will be automatically fixed.

Further editing of the layout schemes can be done by parameter settings in the Operation Field, Layout tab. A detailed description of these layout schemes, the underlying algorithms and parameterization can be found in the Help texts.

21.3. Expression Mapping

This function enables highlighting of up-regulated and down-regulated genes in the network diagrams.

The Expression mapping tab can be found in the Operations Field after having a network diagram opened in the work area. Initially, the expression mapping form is empty as shown in the figure below.

First, drag and drop the table with expression data from the Tree Area over the diagram. You might be interested to use the table with identified differentially expressed genes and calculated fold change values for expression mapping.

Important note. The format of the table with expression data that can be used depends on the format of the diagram. If the diagram was constructed based on the TRANSPATH® database, the format of the table to drag and drop should be “Proteins: Transpath peptides”, and the table should have the symbol . If the diagram was constructed based on the GeneWays database, the format of the table to drag and drop should be “Genes: Entrez”, and the table should have the symbol .

Please check the format of the table with expression data before dragging and dropping it over the diagram, and if necessary, convert it to the required format with the function “Convert table” that can be found at the analyses/Methods/Data manipulation/Convert table.

After the table with expression data is dragged and dropped, the up- and down-regulated genes are automatically highlighted, and the expression mapping tab looks as shown below:

Let’s consider the main options/fields of the expression mapping form.

The default type of mapping is “outside fill”, and the corresponding check-box is checked, highlighted by the red oval below.

If the selected table contains a column LogFoldChange, this column is automatically chosen in the field “Columns”, highlighted by the green oval above. Other numerical columns in the selected table are available under the drop-down menu, and can be chosen instead of the default column to be applied for mapping expression data.

The fields Minimum value and Maximum value display the corresponding values of the selected column with expression data, (see highlighted red ovals in the figure below). These values are used to calculate the intensity of the colors for expression.

You can select colors to indicate up- and down-regulation by mouse click over the colored boxes, and the following toolbox will be displayed.

The expression values can be inferred by the color gradation. A more intensive color corresponds to a bigger fold change value whereas the lighter shade corresponds to a smaller fold change value. In this example all the up-regulated genes are shown with a color gradient from white to red whereas down-regulated genes are shown in with a color gradient from white to blue.

Check-in the box Use inside fill and check-out the box Use outside fill result in the following picture.

21.4. Graph Search

The “Graph search” option allows to extend diagrams already saved in the tree, e.g. to add interactions around the molecules in focus. Whenever a network diagram is opened in the Work Space, the “Graph search” tab can be found in the Operations Field as it is shown below.

The gray fields in the graph search form cannot be edited. The user can change the search direction (upstream of the molecule in focus, or downstream, or both directions) and number of steps (depth) in the selected direction.

Graph search can be done in several iterations.

First iteration of a graph search.

To perform a graph search the following steps are recommended:

Open a diagram in the Work Space and select one element (gene or protein or reaction) for which you want to perform search by mouse-clicking over that element. On the picture below, the molecule “PAK1-isoform1” has been selected, marked by the red oval.

Use the button to add the selected molecule to the elements pane. The added element is marked by the blue oval on the picture below.

Simultaneously, the database to search for additional interactions is automatically identified as the database based on which the network diagram is constructed. For the identified database the Search engine field is specified, marked by the blue oval on the picture below.

Next, choose the direction and depth of search.

Use to start the search. Once the search is finished you get the search results in the elements pane as shown in the screenshot below.

When the Add box is checked, the corresponding molecules will be added to the diagram.
The column with the check box Use means that selected molecules can be in turn used for searching, e.g. in the next search iteration. Direction of the search (up, down or both) and depth will be applied to the molecules with checked boxes in the Use column. By default, all boxes are checked. If you like to add to the diagram only a few molecules, you need to uncheck the others.

In the next step, found molecules can be added to the current diagram by using the icon. By default all found molecules are checked. The user can uncheck some of the molecules in the column Add before adding to the diagram. Only the molecules checked in the column Add are added to the diagram. Here, all molecules are added, as shown on the picture below.

Now, the layout for the extended diagram can be specified, details of the layouts are described in Section “Changing the layout”.

If you would like to remove the search results and select another molecule, the button can be applied.

Second and next iterations of the graph search.

Having several molecules in the element pane at the previous iteration of a graph search, the user can start searching again in the specified direction from these molecules.

The user can uncheck some of the molecules in the column “Use” before starting the search again. Only the molecules checked-in in the column “Use” are used for the second search.

After the search is completed, all found molecules can be again added to the diagram, which may result in a picture, like shown below:

Editing and creating workflows

22.1. Edit a pre-existing workflow

Tip for the workflow editing

You can easily create a similar workflow with parameter values adjusted to your needs. For example, you might be interested to change the number of steps used for the regulator search. By default, 10 steps are applied.

To make a change, you need first to open the workflow under the “Edit workflow” mode, and save its copy in your project area. The [Edit workflow] button is located near the button [Run workflow]. Upon clicking on "Edit workflow", the workflow diagram will be opened in the Work Space, and you can select the analysis box you would like to modify. On the screenshot below "Regulator search" analysis was selected, and in the Operations Field, on the tab "Workflow", all the parameters are visible. Under this mode, you can modify default parameters and then save the workflow.

In this way you will get a customized workflow, with the parameters specified according to your needs.

22.2. Create a new workflow

To create your own workflow, please go to the Start page and click “Create your own workflow” under the list of pre-defined workflow groups. You will be asked to specify the name of the new workflow.

In this example, an entry New workflow (default name) will be created.

You will see now a new tab being opened in the Work Space, where you can design a new workflow diagram. The workflow diagram represents different analysis functions being connected by input and output files. The resulting directed graph visualizes the sequence of analysis steps in the workflow. The diagram also may contain parameters, which are to be defined by the user.

Below you can see an example of one step of a new workflow. At the top of the Work Space there is a toolbox (1) to create all the elements of the workflow. The light blue rectangle in the center (2) is the analysis function (Filter table) to be used in this step. The green box at the left (3) stands for an input table, the right yellow box (4) for the output table of this analysis function. The yellow box above (5) represents a parameter of the analysis function, which in this case (Filter table function) defines a filtering condition.

Upon clicking on any component of the workflow you can see the information about this particular element in the Operations Field. For instance, in the figure below, we can see the content of the parameter “Filtering condition”, which was set as “Score > 2” (so the input table will be filtered by this condition in the table column “Score”).

22.2.1. Six steps to create a simple workflow

Let us go now through the steps of creating this workflow.

Step 1. The elements of the workflow can be placed into the Work Space by using the toolbar or by drag and drop from the Tree Area. Let us place the Filter table function by drag and drop from the subdirectory Methods/Data manipulation on the “Analysis” tab of the Tree Area:

Step 2. For creating the input table you should click on the green element in the tool bar, locate the cursor in the Work Space where you would like to put this element and click. A new window “Create new node” will pop up, where you can define the parameters of the element as will be explained in the following.

Name field: the title of the element; Type: please, select “Data element”, for any objects like tables. In the field Default value you can type a full folder path where the table is located. You can also use some global variables, like “$project$” that already contain the full path (by clicking on the “…” button you can access all the global variables defined for this workflow. Rank (sort order): this number gives the position of this input element in the list of all elements upon starting the workflow. Role: Input, since we are using this element for inputting a table into the workflow.

We have created now an input table element:

Step 3. Connecting elements on the diagram is done by clicking on the arrow symbol in the toolbar. A new window “Create new edge” will pop up. By clicking on the “Table” element in the Work Space you select it as Input node for this edge. Similarly, you click on the left table symbol in the “Filter table” element to select it as an Output node of this new edge.

After pressing “OK” a new connection is created:

Step 4. The same way you can create now an output table element on the diagram by selecting the yellow element in the tool bar (since it is going to be an intermediate table for further use in the next steps of the workflow) and connecting it with the output icon of the “Filter table” element.

In the field “Expression” you can now use a new global variable “$Table$” which will contain, during the run of the workflow, the name of the table which you have entered. So in this case we are creating a new name for the future output table “$Table$ filtered” by adding to the name of the input table an ending “filtered”.

As a result, we have now created one step of the workflow.

Step 5. To filter data, we have to define a filtering condition. To do this we have to create a new element “Filtering condition” (yellow element in the tool bar), which will be now of simple “String” type and which contains a filtering condition “Score > 2” in the field Expression.

A new element “Filtering condition” is created. This element should now be connected to the analysis function “Filter table” in order to define the filtering condition that is going to be applied in this step of the workflow. To do that, please click first on the “Filter table” element and open the parameters of this element in the Operations Field. After that, click on the field “Filtering condition” (1) in the parameter list and select it (a blue background color indicates that the field is selected). Click on the “Bind property to variable” button (2) in the toolbar of the Operations Field. And after that, move the cursor to the Work Space and click on the “Filtering condition” element on the diagram (3).

So, a filtering condition parameter is now connected to the corresponding field in the “Filter table” function.

Step 6. The workflow is now ready to be executed. To start the workflow please click on the “Run workflow” button () in the toolbar of the Operations Field.

In the pop-up menu “Workflow parameters” you should specify the input table. Please navigate to the folder with your tables and select a table which has a column Score and press [Ok]. The workflow will be executed and a new table with a new name and the appendix “filtered” will be created in the same folder as the input table.

22.2.2. Complex workflows

More complex workflows are created by adding further workflow steps and by connecting them through a common data element. As in the example below, the output element “Table filtered” of the first step is used as an input element of the second step of the workflow, “Regulator search”. You can also see that we have added a new input parameter “Species”, which appears now among the workflow parameters upon starting the workflow. With this, you can select a taxonomic species (presently human, mouse or rat) for the table you are going to run through the workflow.

Note: During execution of the workflow a research diagram is saved (you can specify the name of this diagram before starting of the workflow). The research diagram (see figure below for an example) contains the history of the workflow execution with the names of all input and output files. It also contains all the links to these tables, so that you can easily open them by clicking on the respective element in the diagram.

22.2.3. Cycles and scripts

One more element available in the workflow is cycle. It can be created using the “cycle” button in the tool bar. It is necessary to specify the Name of the cycle (the “cycle variable”), and to choose the appropriate Type and Cycle type.

The option “All elements in collection” (Cycle type) together with the Type “Data element” and some folder name in the field Expression means that all data elements from that folder will be taken one by one as cycle variable values. E.g., when selecting Cycle type “Table columns”, Expression should specify the name of this table. Or when choosing Cycle type “Range (from ...to)” for Type “Integer number” and Expression as “2 ... 6”, the cycle will be executed by assigning the values 2,3,4,5 and 6 to the cycle variable.

The Script workflow element represents a code written in JavaScript, which can be executed during the workflow run. To add a script the user should press the tool bar button “analysis-script”, click the proper place in the workflow diagram area and type the JavaScript code in the Script source field.

Another way to add a script to the workflow is to drag and drop some script data element right from the project tree.

All variables defined in a workflow (green and orange boxes) are available inside the script as JavaScript variables. If the name doesn’t contain spaces, it can be used as is, the name should be put into $[“…”] otherwise. For example, the variable “TableColumn” can be accessed from a script by name TableColumn, but “Table column N1” should be called as $[“Table column N1”].

Example 1. Print column names

In the example below the data element parameter InputTable (green box) is first added to the workflow. Then a cycle with the following settings is added:

,

Note that expression is set to $InputTable$ for a cycle.

Then a script element with the following code will be created inside a cycle:

print(TableColumn);

When the [Run] button is pressed, the workflow will ask for a table path, and then will print all column names into the workflow output log.

Example 2. Run GO classification for all tables in a folder

This workflow contains 3 input elements: InputFolder, where one or more Tables should be placed; ResultFolder that will be created by the method “Create folder”, and the data element Species, required for table conversion and functional classification.

The cycle has following settings:

Here the cycle variable named Table will adopt the names of the tables in the InputFolder. Then it goes to the method “Convert table”, and identifiers are converted to Ensembl genes, according to the analysis settings below:

The conversion result is taken as input set in the Functional classification analysis:

The output of the functional classification is a data element named GOres with Expression $ResultsFolder$/$Table/name$ GO.

When the [Run] button is pressed, the workflow will start. If, for instance, the folder test with some tables is defined as input, and the result folder name is test result GO, we get after completion of the workflow:

Editing and creating diagrams

23.1. Diagram types

Diagram types are hierarchically organized as shown in the following scheme:

There are 5 types of diagrams in the geneXplain platform:

Pathway diagram

Pathway simulation

Composite diagram

Pathway simulation (SBGN)

SBML simulation (SBGN)

You find them listed for selection when pressing the button to create a new diagram. This button appears on top of the Tree Area when you go into the Data folder of your project directory (usually under your user name).

The Pathway type diagram is used for formalized description of biological pathway structure (metabolic pathway, gene network, etc).

The Pathway simulation type diagram is an extension of pathway type, where variables are associated with graph nodes and differential equations with graph edges. This allows automatic generation of the mathematical model of the system and simulation of its dynamics.

The Composite diagram may contain several pathway simulation diagrams as well as some types of links between them to join separate simulation modules into one composite model.

The Pathway simulation SBGN diagram type is the same as pathway simulation, and visualized according to the rules of System Biology Graphic Notation.

SBML-SBGN is a specific type to wrap an SBML diagram in the SBGN view.

Please note that diagrams of Semantic type can’t be constructed under general user account.

Entity types and proteins are represented as follows:

The following relations can be defined:

When you choose the option “Pathway simulation”, additional functions are available which you can see from the extended series of icons on top of the Work Space:

The seven new icons on the right (red frame) are specifically introduced for simulation functions, the one on the left replaces the icon with the capital A in the “Pathway diagram” option.

23.2. Creation of a new diagram

A new diagram can be made in the Work Space by two methods:

You can edit pre-composed diagrams and create a new diagram by saving it in your own project area (see below, 23.3).

You can start a new diagram in the work space by using graph search. The details of this method are further described below.

To start with the creation of a new diagram, please select the correct folder in your project area and press the icon in the Control Panel. The tool will ask you to select the type of diagram you want to create in the work space. In the following, the creation of a new “Pathway diagram” will be described.

The description for diagram types has been mentioned above. Upon selecting the type of a diagram and clicking [Ok] a new node New Diagram will appear in the Tree and the corresponding tab is opened in the Work Space to start the diagram creation.

A pathway diagram opened in the work space has a tool bar as shown below:

Using these icons, you can manually add a node that represents a cell (), a cellular compartment (), a gene (), an RNA (), a protein () or a substance (or small metabolite, ). It may also something as abstract as a concept (). These nodes may be related to each other with any kind of link (), or , more specifically and if you wish to model your network as a bipartite diagram, with a reaction ().

In case you wish to link two already existing nodes by a new edge, click on the icon , which will open a new mask where you can define the input and the output node. Just move the mouse cursor into the respective field and click on the node that should serve as input or output node, respectively. The corresponding name or accession number will appear in the field. The same works for linking a note (to be defined before with ) to a node by introducing an edge of the type .

To add an element to the diagram, several ways are available:

i) Search for the element using the search tab in the databases folder (as described in the Section 2.1.3.2) and then add this element to the diagram using the icon .

ii) Drag and drop the element from the Tree Area to the Work Space as shown below.

Drag and drop from tree area to work space

Please note that double clicking on the gene gives the information about that gene in the Info box.

iii) You can then use the Graph search option as described in Section 21.4 to further search for other elements upstream or downstream of those that are already included in the diagram. You can add elements to the new diagram as per your requirement as shown below.

You can specify a layout for the extended diagram, details of the layouts are described under “Changing the layout”.

iv) To create an entirely new node on the same diagram, click on the icon on the pathway diagram menu bar and click on the diagram where you want to add a node. You will get a message as shown below:

You can add the node/Name and press [Ok]; the node will be added to the diagram.

You can also link this node to another element by clicking on the icon in the pathway diagram menu bar. The tool asks the user to select input and output nodes.

You can select input and output nodes easily by clicking first on the element you want to select as input node, and second on the element you want to be the output node. The diagram after adding the node and linking it to a protein looks like shown below.

23.3. Editing a pre-composed diagram

Diagrams saved in the tree area can be edited in several ways. For this, please first copy one of the diagrams, e.g. located in the Examples folder, in your own project area. For this, you open the diagram in work space by double-clicking on its name. On the picture below, the diagram which is opened in the work space, highlighted blue.

Then choose the option “Save as” (button in the Control Panel). A new mask opens (“Save document as”), where you will find your project and a folder labeled “data”; you should save this copy under any new name.

Changing location, color and title of the nodes on a diagram

When you open a diagram saved in your project, you can change color and edit the title of the nodes, shift one node relative to others, remove or add nodes. Adding new nodes to a diagram is described in the previous section, Section 23.2.

To edit a node, first, select it by a mouse click. Below, the molecule bard1 is selected.

Apply the Edit button at the top right corner of the Info box to open the form for editing the selected node. The Edit button is highlighted by a red oval on the picture below.

The Edit form is opened as shown below.

In the Title field you can edit the name of the selected node. In the ShapeColor field the current color of the selected node is shown. By clicking into this field you can open the Select color form and change the color.

After editing is done, press Save on the Edit form.

To remove a node from the diagram, first select this node and then right-click (see figure below).

To shift a node relative to other nodes, select it and drag to a desired position.

Saving changes and undo & redo functions.

After editing of a diagram is complete, you can either save changes or undo them. Saving a diagram under the same path and name is done by the button on the top menu. If you would like to save a diagram to a different folder or project, or under different name, use the Save button .

In case you would like to return to the previous variant, use the Undo button .

Important: undo and redo functions work for one previous step, you need to click the same button again to undo or redo the next previous step. Please note, undo and redo functions work only before saving.

To return to the previously saved version of a diagram, use the Revert button .

Further editing of diagrams includes mapping available expression data on the molecules and adding interactions around the selected molecules. For details on these two functions please refer to the sections 21.3 “Expression mapping”, and 21.4, “Graph search”.

Editing and creating JS scripts

User-specific scripts in JavaScript (JS) can be added directly into the platform, and immediately executed. They can be combined with pre-existing analyses and can be part of the workflows; existing codes in workflows can be edited.

24.1. Creating new JS scripts

For writing a new JS script, go to any of your data folder (blue marked) and click on the “New JS script” button () in the toolbar. After pressing a new tab “New script.js” opens in the Workspace. You can write your script and save it for later execution or incorporation into a workflow.

24.2. Executing JS scripts

To execute a JS code directly, click the “Script” tab in the Operation Field. Simply write or copy and paste your script code in the box. To run the script, press the button [Execute].

After pressing [Execute] the new tab “Script log” opens in the Workspace. Here you can find information about the success and the output of the script.

To familiarize yourself with the handling of JavaScript, you may use the following example script:

http://platform.genexplain.com/bioumlweb/#de=data/Examples/Scripts/Data/mergeTables.js

24.3. Editing JS scripts in workflows

To edit a JavaScript code in an existing workflow, you have to open the workflow in Edit mode and copy it in your data folder. An example is given by the following workflow:

http://platform.genexplain.com/bioumlweb/#de=analyses/Workflows/GTRD/Analyze%20promoters%20%28GTRD%29

To change the JavaScript code, the grey ScriptBox must be clicked in the workflow overview (1), whereupon the Script source appears in the “Workflow” tab of the Operation field (2). Press the button [ ... ] to open the JavaScript editor (3). In this window, the code can now be changed directly. The confirmation of the editing is done using the [Ok] button.

2

1

24.4. Inserting JS scripts in workflows

To insert a script in an existing or self-created workflow, press the “Analysis –script” button () in the toolbar. After clicking on button [...] of the Script source, the JavaScript editor opens. The script can be inserted.

List of icons and their meaning

25.1. General (Control Panel)

Icon

Function

Log-out

Home

Account info

Project Properties

Save

Save as, applied to the element (table or diagram) opened in the Work Space

Revert to the previously stored version, to discard the changes introduced and revert to the default diagram

Import a file

Export the element (table or diagram) opened in the Work Space

Undo previous action

Redo previous action

Zoom in

Zoom out

Information about geneXplain platform

Help

Remove selected rows from a table

25.2. Tree Area Panel

25.2.1. General

Note that the appearance of these icons on top of the directories in the Tree Area is context-sensitive.

Expand / collapse directory

Invoke a script

Compose a new workflow

Compose a new diagram

Create a new directory

Start an parameter optimization process

Import a file into the selected (sub-)directory

Export the element selected in the (sub-)directory of the Tree Area

Remove

Open the selected file (highlighted by a light-blue background in the Tree Area)

25.2.2. Database types

Icon

Function

Protected database with read access enabled

Public database with read access

Database with read & write access

Database with read access, write access enabled

Database with read access, write access disabled

25.2.3. Data types

Icon

Function

Table (general)

Table of probes and properties of their signals

Table of probes with Affymetrix IDs

Table of probes with Agilent IDs

Table of probes with Illumina IDs

Table of genes

Table of genes with Ensembl IDs

Table of genes with Entrez IDs

Table of genes with GeneBank IDs

Table of genes with Gene Symbol as IDs

Table of genes with RefSeq IDs

Table of genes with UniGene IDs

Table of genes with Illumina IDs

Table of genes with Proteome IDs

Table of genes with Transpath IDs

Table of proteins (general)

Table of proteins (Reactome database)

Table of proteins (TRANSPATH database)

Table of proteins (Ensembl)

Table of proteins (GeneBank)

Table of proteins (IPI)

Table of proteins (RefSeq)

Table of proteins (TRANSFAC)

Table of proteins (UniProt)

Table of SNPs

Table of transcripts

Transcripts Ensembl

DNA sequences in EMBL, FASTA, GenBank format

Workflow

Diagram, with nodes and (optionally) edges as components

Compartment, with nodes and edges hierarchically assigned

Node in a diagram

Protein

Gene

Edge (relation) in a diagram

Plot

Reaction

Set of genomic intervals (Track)

25.2.4. Types of analyses

Icon

Function

Data manipulation

Annotate a table (add additional columns)

Annotate track with genes

Apply State to Diagram

Composite modules to Proteins

Convert table identifiers using BioHub(s)

Convert table to track

Create a folder

Create Random track

Filter one track by another

Filter table

Filter track by condition

Gene set to track

Intersect / join tables

Intersect Tracks

Join several Tables

Join Tracks

Matrices to molecules

Process track with Sites

SNP matching

Split table by columns

Track to gene sets

Venn diagram

Data normalization

Normalize expression data

Normalize data and split between experiment and control

Normalization Quality Plots

Functional classification

Gene Set Enrichment Analysis (GSEA)

Functional classification

Import

Import a file

Molecular networks

Add expression values to a network / diagram

Cluster by shortest path/Join Diagrams

Search for a common effector

Extend network to all reachable nodes

Search for a common regulator

Save hits

Save network

Visualize results as diagram

Optimization

Parameter optimization

Sequence manipulation

Bowtie

Chip-Seq peak Profile

Model-based analysis of ChiP-Seq data (MACS)

ChipHorde/DiChipHorde

ChipMunk/DiChipMunk

Mutation Effect

Site analysis

Create Profile cutoffs

Construct IPS cisModule

Construct Composite Modules

Create IPS model

Create Match model

Create profile from gene table

Create profile from the table of site models (matrices)

Create profile from matrix library

Create weight matrix model

IPS motif discovery

MEALR(tracks)

Motif quality analysis

Search for enriched TFBSs (genes)

Seach for enriched TFBS (tracks)

Site search on gene set

Site search on track

Site search result optimization

Statistical analysis

K-means cluster analysis

Chinese restaurant cluster analysis (CRC)

Correlation analysis

EBarrays

Fold change calculation

Hypergeometric analysis

LIMMA

Meta-analysis

PCA

Polynomial regression analysis

Identification of up- and down-regulated genes

25.3. Info Box

View information in a separate window

Edit description on the Info tab (only for own files; editing possible in pink fields)

Launch search for the entered term in the selected database

25.4. Operations Field

Execute (e.g., prepare layout as defined)

Apply layout as generated in the Operations Field to the diagram in the Work Space

Stop process (e.g., stop preparing new layout)

Accept layout edited in the Work Space to the Operations Field

Add new (e.g., new expression mapping)

Remove

Edit description

Save

Launch graph search

Discard changes / clear elements

Stop Task

27.5. 

25.5. Work Space

25.5.1. Diagram editing

Insert a node representing a cell

Insert a node representing a cell compartment

Insert a node representing a concept

Insert a node representing a gene

Insert a node representing an RNA

Insert a node representing a protein

Insert a node representing a substance

Define a reaction

Add a note

Add an edge representing a relation

Add an edge to a note