Archive - geneXplain

Genome Enhancer

Release 3.1 is now out!


Welcome to the new era of Precision Medicine

Meet Genome Enhancer – a fully automated pipeline for patient omics data analysis, which identifies prospective drug targets and corresponding treatments by reconstructing the molecular mechanism of the studied pathology. Proven applications of Genome Enhancer include cancer, neurodegenerative diseases, infectious diseases, diabetes and metabolic diseases, hypertension.

Starting from release 2.0 Genome Enhancer is also available as Genome Enhancer Expert solution – a powerful synergism between the automatic pipeline for multi-omics data processing of Genome Enhancer and the comprehensive bioinformatics toolbox of the geneXplain® platform. More details about Genome Enhancer Expert solution are available here.

You can login to Genome Enhancer directly with your geneXplain® platform (get one by registering here).


Genome Enhancer uses Upstream Analysis, an integrated promoter and pathway analysis, to identify potential drug targets of the studied pathology.

In the first step of this analysis the transcription factors that regulate differentially expressed or mutated genes are identified with the use of the TRANSFAC® database of transcription factors binding sites.

The second step searches for common master-regulators of the identified transcription factors by building a personalized signal transduction network of the studied pathology using the TRANSPATH® database of mammalian signal transduction and metabolic pathways. The identified

master regulators are prospective drug target candidates. They are used for further selection of chemical compounds that can bring therapeutic benefit for the studied clinical case. In this step the HumanPSD database is employed to identify drugs that have been tested in clinical trials. The cheminformatic tool PASS predicts small molecules that can affect the identified targets.

Finally, Genome Enhancer generates a comprehensive analysis report about the personalized drug targets identified for a certain patient, or a group of patients, and the drugs that may be effective in this case. You can view a number of Genome Enhancer demo reports at the corresponding section of this page.


A detailed description of Genome Enhancer analysis schema can be found on this page.

Check out the Genome Enhancer video channel ran by the CSO of geneXplain GmbH Dr. Alexander Kel. See how various omics data can be analyzed in Genome Enhancer or even send your own data to Dr. Kel and he will show its analysis in one of the next videos!

Key features

  • Identifies activated targets in the examined patient data and suggests known and re-purposing drugs
  • Based on well accepted databases TRANSFAC®, TRANSPATH®, HumanPSD™
  • Applies unique in-house developed algorithms
  • Suites for use by researches and by medical doctors
  • Does not require special skills to operate
  • Analyses all types of omics data starting either with raw or pre-processed data
  • Generates a comprehensive report on the identified drug targets and prospective therapies
  • Generates MTB (Molecular Tumor Board) report on patient’s genomics data for a number of pathologies (if not more than 2 conditions were selected during the analysis launch)


Available solutions


Starting from release 2.0 Genome Enhancer is also available as Genome Enhancer Expert solution – a powerful synergism between the automatic pipeline for multi-omics data processing of Genome Enhancer and the comprehensive bioinformatics toolbox of the geneXplain® platform.

Genome Enhancer Expert will open you the full functionality of the geneXplain® platform with TRANSFAC®, TRANSPATH® and HumanPSD™ databases connected. In the platform view you will be able to perform further processing of your analysis results, received from Genome Enhancer, create, modify and use already pre-defined workflows for your multi-omics data analysis and work with data coming from other model organisms. For more info on geneXplain® platform functionality please refer to the platform product page.

Only three steps to launch the analysis


Running Genome Enhancer takes only three steps:

1. Upload your data to the server and specify the import options (data type)

2. Split your data by the conditions you want to compare

3. Launch the analysis by specifying the conditions to be compared and the disease and tissue types (optional)

The analysis report will be ready shortly. Depending on your input data, it will include lists of differentially expressed or mutated genes; transcription factors, regulating those genes; reconstructed signaling network of the studied pathological process; potential drug targets and corresponding known drugs and re-purposing drugs, which may be effective in the studied case, as well as further cheminformatically predicted drug-like compounds. The report also contains description of analysis methods used and the references.

Acceptable input data formats

Genome Enhancer works with genomics, transcriptomics, epigenomics, proteomics and metabolomics input data types of the following formats:

Transcriptomics (RNA-seq, microarrays)

*.txt, *.csv, *.xls (table with gene identifiers)

*.CEL (affymetrix)

*.txt (special agilent format)

*.txt (special illumina format)


Epigenomics (ChIP-seq)


*.bam (hg38 only)

*.bed (hg38 only)

*.txt (table with illumina methylation probe ids, cg*)



*.txt, *.csv, *.xls (table data with SNP identifiers, rs*), *.tsv



*.txt, *.csv, *.xls (table with protein identifiers)


*.txt, *.csv, *.xls (table with the list of metabolites from chebi database, e.g. CHEBI:57316)

Files of one data format can be uploaded in a .zip archive

What we offer

Multi-omics analysis

Use genomics, transcriptomics, metabolomics, proteomics, and epigenomics data in one analysis run and receive an integrated report


Easy interface

Due to complete automation of the analysis, the system can be used by medical doctors and biologists without any bioinformatics skills


Personalized medicine

Running the analysis on omics data of a certain patient, you will identify personalized prospective drug targets and corresponding treatments


Scientific base

Integration of promoter and enhancer analysis with pathway reconstruction gives unrivaled disease molecular mechanism modeling accuracy

Drug target identification

Genome Enhancer reconstructs a complex network of signal transduction pathways that are activated in the pathology and identifies their key regulators



Flexible pricing

Select the license which fits you best – both short-term and long-term licenses are supported by our flexible tariffs. For more details visit the geneXplain store

Report examples

You can view various analysis report examples generated by Genome Enhancer on the basis of different omics input data types and various origins of the studied pathologies:

You are also very welcome to view or download the Genome Enhancer flyer.



Below you will find a compilation of our videos referring to different aspects of Genome Enhancer.
In English Language
This video introduces you to the fully automated pipeline for the easy bioinformatics analysis of multi-omics data. (2:02 min)
This video shows how Genome Enhancer results can be interpreted for further use in clinic on the example of sensitivity prediction towards VEGFA-targeted therapy for three colorectal cancer patients. (5:51 min)
In Chinese Language
The video provides you with a preview to the user interface of Genome Enhancer, and shows how to perform an analysis using Genome Enhancer. (4:42 min)
This video uses transcriptomics data as an example to highlight the detailed content of the data analysis report obtained as result of an analysis by Genome Enhancer. (3:09 min)
Here, Genome Enhancer is applied to analyze multi-omics data of colorectal cancer cell lines to find master regulators and to predict drug compounds affecting them. Thereafter, the effect of the predicted drug compounds on tumors is verified in an animal experiment.


  1. Lloyd, Katie, et al. “Using systems medicine to identify a therapeutic agent with potential for repurposing in Inflammatory Bowel Disease.” Disease models & mechanisms (2020).
  2. Kel, Alexander, et al. “Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer.” BMC bioinformatics 20.4 (2019): 119.
  3. Kolpakov, Fedor, et al. “BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data.” Nucleic acids research 47.W1 (2019): W225-W233.
  4. Boyarskikh, Ulyana, et al. “Computational master-regulator search reveals mTOR and PI3K pathways responsible for low sensitivity of NCI-H292 and A427 lung cancer cell lines to cytotoxic action of p53 activator Nutlin-3.” BMC medical genomics 11.1 (2018): 12.
  5. Boyarskikh, U. A., et al. “Master-regulators driving resistance of non-small cell lung cancer cells to p53 reactivator Nutlin-3.” Virtual Biology 4 (2017): 1-31.



The results of Genome Enhancer analysis, contained in any of the reports produced by this pipeline, are intended for research use only and should not be used for medical or professional advice. GeneXplain GmbH makes no guarantee of the comprehensiveness, reliability or accuracy of the information contained in the reports generated by Genome Enhancer.

Decisions regarding care and treatment of patients should be fully made by attending doctors. The predicted chemical compounds listed in the reports are given only for doctor’s consideration and they cannot be treated as prescribed medication. It is the physician’s responsibility to independently decide whether any, none or all of the predicted compounds can be used solely or in combination for patient treatment purposes, taking into account all applicable information regarding FDA prescribing recommendations for any therapeutic and the patient’s condition, including, but not limited to, the patient’s and family’s medical history, physical examinations, information from various diagnostic tests, and patient preferences in accordance with the current standard of care. Whether or not a particular patient will benefit from a selected therapy is based on many factors and can vary significantly.

The compounds predicted to be active against the identified drug targets in the reports are not guaranteed to be active against any particular patient’s condition. GeneXplain GmbH does not give any assurances or guarantees regarding the treatment information and conclusions given in the reports. There is no guarantee that any third party will provide a refund for any of the treatment decisions made based on these results. None of the listed compounds was checked by Genome Enhancer for adverse side-effects or even toxic effects.

The analysis reports contain information about chemical drug compounds, clinical trials and disease biomarkers retrieved from the HumanPSD™ database of gene-disease assignments maintained and exclusively distributed worldwide by geneXplain GmbH. The information contained in this database is collected from scientific literature and public clinical trials resources. It is updated to the best of geneXplain’s knowledge however we do not guarantee completeness and reliability of this information leaving the final checkup and consideration of the predicted therapies to the medical doctor. In all cases, the end user (including researchers and medical doctors) accepts full responsibility for all risks associated with using of information, contained in the reports generated by Genome Enhancer.

The scientific analysis underlying the Genome Enhancer reports employs a complex analysis pipeline which uses geneXplain’s proprietary Upstream Analysis approach, integrated with TRANSFAC® and TRANSPATH® databases maintained and exclusively distributed worldwide by geneXplain GmbH. The pipeline and the databases are updated to the best of geneXplain’s knowledge and belief, however, geneXplain GmbH shall not give a warranty as to the characteristics or to the content and any of the results produced by Genome Enhancer. Moreover, any warranty concerning the completeness, up-to-dateness, correctness and usability of Genome Enhancer information and results produced by it, shall be excluded.

The results produced by Genome Enhancer, including the analysis reports, severely depend on the quality of input data used for the analysis. It is the responsibility of Genome Enhancer users to check the input data quality and parameters used for running the Genome Enhancer pipeline.

Note that the text given in the reports is not unique and can be fully or partially repeated in other Genome Enhancer analysis reports, including reports of other users. This should be considered when publishing any results or excerpts from the reports. This restriction refers only to the general description of analysis methods used for generating the reports. All data and graphics referring to the concrete set of input data, including lists of mutated genes, differentially expressed genes/proteins/metabolites, functional classifications, identified transcription factors and master regulators, constructed molecular networks, lists of chemical compounds and reconstructed model of molecular mechanisms of the studied pathology are unique in respect to the used input data set and Genome Enhancer pipeline parameters used for the current run.

Upstream Analysis

genexplain platform logo

What is Upstream Analysis?

GeneXplain’s proprietary approach to analyze gene expression data is called Upstream Analysis. The term indicates that it is a causal analysis, providing a clue about the reason why a certain set of genes has been up- (or down-) regulated in the system under study. In contrast, conventional analyses usually reveal the effects of the differentially expressed genes, e.g. by mapping them onto ontological categories.


How does it work?

GeneXplain’s Upstream Analysis is an integrated promoter – pathway analysis. It starts from any list of differentially expressed genes (DEGs), which you may have extracted from your raw data with the aid of the geneXplain platform, and comprises two main steps:
  • At first, the promoters of the differentially regulated genes are retrieved and analyzed for potential transcription factor (TF) binding sites and their combinations. From that, a set of TFs is identified that potentially have regulated the found DEGs.
  • In a second step, the pathways are reconstructed that are known to activate the previously hypothesized TFs. Molecules where these pathways converge are considered as potential master regulators of the process under study

Step 1: Promoter analysis

First, potential transcription factor binding sites (TFBSs) are identified in all promoters of the DEGs of your experiment (Yes set) as well as in a negative control set (No set). This is the usually done with a library of position-specific scoring or positional weight matrices (PSSMs or PWMs).
We recommend to apply the most comprehensive matrix library available, the TRANSFAC® database, and using the MATCHTM algorithm for the sequence analysis.
Next, out of all these potential transcription factor binding sites (TFBSs), those that are characteristic for the DEG set under study are identified. This is done by rigorously determining their enrichment in the Yes- compared to the No set.
Learn more about promoter analysis with TRANSFAC® in the geneXplain platform.

Step 2: Pathway analysis

Step 1 resulted in a set of transcription factors (TFs), that are likely repsonsible for the differential regulation of the observed set of DEGs. From available pathway data, we have extracted information about all relevant signaling cascades that regulate the activity of TFs; optimally, the TRANSPATH® database is used for this and the further analysis.
As has been proven in a large number of use cases, these pathways usually converge in a couple of key nodes, which qualify as candidate master regulators of the process under study.

Activities of transcription factors (TFs, blue circles) are regulated by upstream signaling cascades (components shown as green circles). These converge in certain nodes, representing molecules that are potential master regulators of the process under study.


PharmaExpert Logo
PharmaExpert analyzes the relationships between biological activities, drug-drug interactions and multiple targeting of chemical compounds and selects compounds that have a pre-defined biological activity. It helps answer a question like “How to select the most promising compounds among those known to interact with the selected protein?”

Click image to enlarge the picture.

PharmaExpert interface


PharmaExpert interface. PharmaExpert analyzes the relationships between biological activities, drug-drug interactions and multiple targeting of chemical compounds and selects compounds that have a pre-defined biological activity.

PharmaExpert interface. (Click image for an enlarged view.)

Key features of PharmaExpert

PharmaExpert is an expert system taking into account the known relationships between pharmacotherapeutic effects and mechanisms of action of biologically active substances.

Fields of application: Analysis of the cause-effect relationships between the biological activities, estimation of possible positive and negative pharmacokinetic and pharmacodynamic drug-drug interactions, selection of compounds with the needed activity spectra predicted by PASS, identification of compounds with multiple mechanisms of particular pharmacological action.

PharmaExpert is designed to visualize and to analyze the prediction results of PASS and GUSAR software. It provides the following functions:

– reading the SD files containing information about the structures of organic compounds and prediction results of their spectra of biological activity provided by PASS, as well as the prediction results of (Q)SAR models of GUSAR;

– visualization of relationships between the predicted biological activities based on the known data about the causal relationships between them, and “target-pathway-effect” relationships;

– selecting compounds with desired biological activities in one or more SD files;

– analysis of possible positive and negative drug-drug interactions for individual pairs of compounds or for all compounds contained in the SD file;

– saving identified relationships between the activities and the results of the selection of compounds with desired biological activities in SD or TXT file.


PharmaExpert 2022 contains a knowledgebase with over 15 thousand of known interactions between biological activities, as well as the relationships between proteins, signalling/regulatory pathways (KEGG or Reactome), Gene Ontology biological processes and therapeutic and adverse effects:

PharmaExpert knowledgebase

All biological activities are divided onto seven types: (1) mechanisms of action; (2) pharmacological effects; (3) toxic and side effects; (4) interaction with antitargets; (5) interaction with drug metabolizing enzymes (inhibition, induction, interaction as a substrate); (6) changing gene expression of individual genes (increase, decrease); (7) interaction with transporter protein (inhibition, stimulation, interaction as a substrate).

Automatic search is provided for compounds acting on any of the mechanisms of action (or simultaneously on several mechanisms of action, up to ten) associated with the therapeutic effect or signalling/regulatory pathway (KEGG or Reactome) and biological process of Gene Ontology.

Analysis of possible drug-drug interactions is performed simultaneously for all seven types of biological activity.


The acronym PASS stands for Prediction of Activity Spectra for Substances. Using structural formula of a drug-like substance as an input, one obtains its estimated biological activity profile as an output. The predicted biological activity list includes the names of the probable activities with two probabilities: Pa – likelihood of belonging to the class of “Actives” and Pi – likelihood of belonging to the class of “Inactives”. 


By default, all activities with Pa>Pi are considered as probable; however, depending on the particular tasks, the user may choose any other cutoff for selecting the probable “Actives”.

PASS has been well accepted by the research community, and is now actively used in the field of medicinal chemistry, by both academic organizations and pharma companies. There are over 1,200 publications described PASS approach and its applications. Overview on some papers is provided here

Activity prediction for a chemical substance by PASS

The slide show demonstrating the look-and-feel of PASS can be found here as well as on our Facebook site. A particularly useful tool to analyze and utilize PASS results further is PharmaExpert.

Key features of PASS 2022

PASS training set
The general PASS training set was corrected and extended; thus, PASS 2022 SAR Base includes 1,614,066 (1,368,353 in PASS 2020) drugs, drug-candidates, pharmaceutical agents and chemical probes, as well as compounds for which specific toxicity information is known.
Biological activities list
The entire activity list includes 10,112 terms describing biological activities (9,942 in PASS 2020). About two hundred novel biological activities were added including: Antiviral (Coronavirus), Antiviral (SARS coronavirus), 3C-Like protease (SARS coronavirus) inhibitor, Papain-like protease (SARS coronavirus) inhibitor.
Pairwise structure-activity
In PASS 2022 the total number of pairwise structure-activity records is 5,174,855 (4,288,195 in PASS 2020), with an average of 512 compounds per activity and 3.2 activities per compound.
Predictable activity types
The number of predictable activity types is 8,565, and 1,957 activity types are in the recommended activity list. The average invariant accuracy of prediction (IAP) exceeded 0.93 for all 8,565 predictable activities, and is over 0.97 for the recommended activities. Depending on the particular purpose, the user may include into the predictable activity list any of the 8,565 activity types using the “Selection” procedure.

In PASS 2022, the MNA descriptors (for prediction of activity spectra or for adding substances to SAR Base) are generated if structure corresponds to the following criteria:

  • each of the atoms in a molecule must be presented by atom symbol from the periodic table. Symbols of unspecified atom A, Q, *, or R group labels are not allowed;
  • each of the bonds in a molecule must be covalent bond presented by single, double or triple bond types only.

All other limitations on the structural formulae implemented in the previous PASS versions (only one uncharged component, minimum three carbon atoms in the structure, MW<1,250) are not applied anymore.

If the structure does not correspond to these criteria or the input data contains any other errors, a message about the first critical error will be received.

For a multicomponent structure, only the largest component (with the largest number of heavy atoms) is taken into account.

Based on the prediction results, you can evaluate the contribution of each of the atoms of the structure to the estimated biological activity. Select the desired biological activity in the predicted activity spectra by clicking on it; then, each of the atoms of the structure will be colored according to the following scheme:

Light Green   Pa = 1, Pi = 0 (atom promotes activity)

Light Red       Pa = 0, Pi = 1 (atom promotes inactivity)

Light Blue      Pa = 0, Pi = 0 (atom does not generate any signal)

Grey                Pa = 0.33, Pi = 0.33 (atom equivocal for weak signal)

Acyclovir, selected activity – “Antineoplastic enhancer”.



Find history in our archive.
January 20, 2023

Check out this white paper that is comparing TRANSFAC and JASPAR in a study of SNPs associated with the risk of severe COVID-19 symptoms.

January 19, 2023

Coffee Break With TRANSFAC

The “Coffee break with TRANSFAC” initiative is a series of recurrent sessions devoted to various aspects of applied bioinformatics. Leave us your question and geneXplain’s CEO Dr. Alexander Kel will address it during one of the next sessions.

January 17, 2023

Bioinforamtics Q&A session - coffee break with TRANSFAC

Free online seminar “Coffee break with TRANSFAC” – ask your question here and join the Q&A session with our CEO Dr. Alexander Kel on January 17th at 11 AM CET

November 21, 2022

New release of the geneXplain products – check all new features here!

September 26, 2022

Our new COPreDict project devoted to COPD progression tracking and prospective treatment selection has started. Find all details here >>