geneXplain-platform

geneXplain platform

The comprehensive bioinformatics platform, also available as Java API

The geneXplain platform is an online toolbox and workflow management system for a broad range of bioinformatic and systems biology applications. The individual modules, or Bricks, are unified under a standardized interface, with a consistent look-and-feel and can flexibly be put together to comprehensive workflows. The workflow management is intuitively handled through a simple drag-and-drop system. With this system, you can edit the predefined workflows or compose your own workflows from scratch.

Own Bricks can easily be added as scripts or plug-ins and can be used in combination with pre-existing analyses.
GeneXplain GmbH provides a number of state-of-the-art bricks; some of them can be obtained free of charge, while others require licensing for small fee in order to guarantee active maintenance and dynamic adaptation to the rapidly developing know-how in this field.

geneXplain start page

The start page provides an easy access to a number of application areas. Click image for full picture.

Key features

Integrated databases and analysis tools

The platform provides an integrated view on several databases and analysis tools, public domain as well as commercial ones. They can be combined in a highly flexible way to design customized analyses.

Ready-made workflows for an easy start

A rapidly growing number of proven workflows facilitates a quick and easy access to the platform and its complex analysis functions. Input forms are simple and user-friendly. Workflows can be easily customized to specific needs. Experienced users can create their own workflows.

Fully integrated upstream analysis

The platform provides a fully integrated upstream analysis, which combines state-of-the-art analysis of regulatory genome regions with sophisticated pathway analyses.

Knowledge-based data analysis

The platform uses a number of renowned high-quality databases for the data analysis. TRANSFAC® and TRANSPATH® are expert-curated databases. GeneWays is generated by an NLP-based text-mining approach, providing a helpful complement for manually curated data. Well-known public-domain databases like Reactome and HumanCyc are integrated and applied as well.

JavaScript and R scripts

User-specific scripts in JavaScript and in R 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.

NGS data analysis

NGS data analysis is supported by the platform. ChIP-seq data sets containing in vivo transcription factor binding sites or methylation results can be analyzed with the help of ready-made workflows. Galaxy tools are integrated, supporting RNA-seq data analysis, and many functions more.

Simulation engine inside

The platform contains a simulation engine that executes differential equation systems and visualizes the results. Parameter optimization, parameter fitting (based on expression data), and hierarchical modeling are supported.

Group project work including chat function

Share your data and results with other members of the project. Discuss what you are doing while working together on a dataset.

Major features

New workflows

  • A new set of workflows is summarized under the start page button Metabolism
  • Flux balance analysis to identify enzymes, reactions and metabolites including clustering and mapping of flux data
  • Enriched upstream analysis (TRANSFAC® and TRANSPATH®)
  • Focused upstream analysis (TRANSFAC® and TRANSPATH®)
  • Upstream analysis with feedback loop (TRANSFAC® and TRANSPATH®) indicates master regulators with expression values (fold changes) from the input set
  • Identify enriched motifs in tissue specific tracks (TRANSFAC®)
  • Cross-species identification of enriched motifs in promoters
  • Cross-species mapping to ontologies

New and updated databases

  • Human Metabolic Reaction (HMR): HMR2 contains 3.765 genes associated with over 8.000 reactions and over 3.000 unique metabolites.
  • Human metabolism global reconstruction (Recon 2): The most comprehensive representation of human metabolism that is applicable to computational modeling
  • Integration of the latest TRANSFAC® and TRANSPATH® versions (releases 2019.3)
  • HOmo sapiens COmprehensive MOdel COllection (HOCOMOCO): This database provides transcription factor (TF) binding models for 601 human and 396 mouse TFs, partially based on TRANSFAC.
  • Reactome database has been updated to version 63.
  • Integration of new Ensembl versions (Release 96), Ensembl Homo sapiens (hg38), Ensembl Mus musculus (mm10), Ensembl Rattus norvegicus (rn6)

New methods

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Subread – aligns DNA- and RNA-seq reads

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Subjunc – exon–exon junctions

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featureCounts – counting of reads

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exactSNP – SNPs for individual samples

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Limma voom – Differential expression analysis

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Guided limma – Linear model analysis

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HISAT2 – Fast and sensitive alignment tool

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HTseq Count – reads mapping for each feature

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HTseq QA – Quality report for reads

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EdgeR – Empirical Analysis of Digital Gene Expression Data in R

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Plotting – Pie and bar charts

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Tree map – Reduce functional classification terms

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Get miRNA targets

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miRNA feed forward loops

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Analyze miRNA target enrichment

Current platform release

geneXplain® platform 6.0 release

Download full new features list

Databases update:

  • TRANSFAC®, TRANSPATH® and HumanPSD™ databases update to release 2020.2
  • Ensembl version update to release 99
  • Gene Ontology update to version 2020-03-25

New features:

  • New Import – import now supports the Drag and Drop function.
  • EdgeR for two tables – method now supports two separate read counts tables for samples and control samples.
  • MPT report – search within curated databases (GKDB1 and CIViC2) for predictive biomarkers according to their clinical evidence for somatic variants (mutations, amplifications, deletions, rearrangements) of a patient and outputs automatic pdf report.
  • Calculate CMA regulation – calculates regulatory scores for transcription factors from a CMA (composite modules analysis) and Master regulator (MR) analysis result and creates a visualization of top MRs with CMA modules, underlying genes and potential feed forward loop regulation.
  • Create profile from CMA model – generates a matrix collection of transcription factors found in a user’s CMA model.
  • Find regulatory regions with mutations – calculates mutation scores using information about mutation locations.
  • PSD pharmaceutical compounds analysis – generates a table with drugs known to be acting against the corresponding targets of an input gene list.

New workflows:

These new workflows allow a fully automatic analysis from raw RNAseq reads with pre-processing, alignment summaries, quality control, plots and estimation of differentially expressed genes (DEGs) for unlimited FASTQ files stored simply in a data folder:

Enhancements:

  • Transparent genome version control – easy selection of a genome version in RNAseq pre-processing pipelines, which will be applied to state-of-the-art alignments methods.

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Demo Workflows

Here, we list workflows that were used to prove the capabilities of the geneXplain platform, for instance in the cited publications. The links given direct you straight into the platform without requiring any registration.
Using these demo workflows, you will be able to reproduce the published results and to learn more about the platform’s look and feel. To work with your own data, however, registration is required. Additional licensing is necessary for certain third-party products such as TRANSFAC® or TRANSPATH®.
Workflows 1-3 are from our recent publication Koschmann et al., Microarrays 4, 270-286.
1. Workflow “Identify enriched motifs in promoters“,
applied to datasets of naphthalene-treated mouse liver and lung tissue.
Find further instructions and explanations here.
Origin of datasets:
GEO GSE18858 and GSE17933
Thomas, R.S., et al. (2011) Application of transcriptional benchmark dose values in quantitative cancer and noncancer risk assessment. Toxicol. Sci. 120, 194-205. PMID: 21097997
Thomas, R.S., et al. (2009) Use of short-term transcriptional profiles to assess the long-term cancer-related safety of environmental and industrial chemicals.Toxicol. Sci. 112, 311-321. PMID: 19776212
2. Workflow “Find master regulators in the TRANSPATH® network“,
applied to datasets of naphthalene-treated mouse liver and lung tissue.
Find further instructions and explanations here.
The data sources are as for Workflow 1.
3. Workflow “Enriched upstream analysis with TRANSFAC® and TRANSPATH® network“,
applied to datasets of naphthalene-treated mouse liver and lung tissue.
Find further instructions and explanations here.
The data sources are as for Workflow 1.

Workflow management

Sequential launching of particular analysis modules can be saved as a graphically represented workflow. Modules are shown as purple rectangles, and outputs of each step serve as inputs into the next analysis step. A workflow that is specific for a given data set can be easily constructed by drag and drop of the required analysis modules. In addition, Java scripts and R scripts can be added directly within the platform, for more specific requirements of the analysis.
The picture shows a small workflow for the gene set enrichment analysis (GSEA) of using four different ontologies: Gene Ontology (GO) Biological Process, GO Cellular Compartment, GO Molecular Function, and Reactome's functional assignments (see on the right side). The larger light blue boxes are analysis functions (program modules, "Bricks"). Green boxes stand for input files, especially user-defined inputs. Yellow boxes represent automatic delivery or output files. A workflow can be intuitively designs by simple drag-and-drop of the constituents and graphically connecting them.

Workflow management in the geneXplain platform. (Click image to see the complete picture).

See demo workflows for a collection of executable workflows: no registration required!

Videos

Intro video of the geneXplain platform.

Information downloads

Publications

Mekonnen, Y. A., Gültas, M., Effa, K., Hanotte, O. and Schmitt, A. O. (2019) Identification of Candidate Signature Genes and Key Regulators Associated With Trypanotolerance in the Sheko Breed. Front. Genet. 10:1095. doi: 10.3389/fgene.2019.01095. Link

Blazquez R., Wlochowitz D., Wolff A., Seitz S., Wachter A., Perera-Bel J., Bleckmann A., Beißbarth T., Salinas G., Riemenschneider M.J., Proescholdt M., Evert M., Utpatel K., Siam L., Schatlo B., Balkenhol M., Stadelmann C., Schildhaus H.U., Korf U., Reinz E., Wiemann S., Vollmer E., Schulz M., Ritter U., Hanisch UK., Pukrop T. (2018) PI3K: A master regulator of brain metastasis-promoting macrophages/microglia. Glia. 66(11):2438-2455. doi: 10.1002/glia.23485. Epub 2018 Oct 25. Link

Orekhov, A. N., Oishi, Y., Nikiforov, N. G., Zhelankin, A. V., Dubrovsky, L, Sobenin, I. A., Kel, A., Stelmashenko, D., Makeev, V. J., Foxx, K., Jin, X., Kruth, H. S. and Bukrinsky, M. (2018) Modified Ldl Particles Activate Inflammatory Pathways In Monocyte-Derived Macrophages: Transcriptome Analysis. Curr. Pharm. Des., 11. doi: 10.2174/1381612824666180911120039. Link

 

Kalozoumi, G., Kel-Margoulis, O., Vafiadaki, E., Greenberg, D., Bernard, H., Soreq, H., Depaulis, A., Sanoudou, D. (2018) Glial responses during epileptogenesis in Mus musculus point to potential therapeutic targets. PLoS One. 13(8):e0201742. doi: 10.1371/journal.pone.0201742. Link

 

Smetanina, M.A., Kel, A.E., Sevost’ianova, K.S., Maiborodin, I.V., Shevela, A.I., Zolotukhin,  I.A., Stegmaier, P., Filipenko, M.L. (2018) DNA methylation and gene expression profiling reveal MFAP5 as a regulatory driver of extracellular matrix remodeling in varicose vein disease. Epigenomics. 10(8):1103-1119. doi: 10.2217/epi-2018-0001. Link

 

Boyarskikh, U., Pintus, S., Mandrik, N., Stelmashenko, D., Kiselev, I., Evshin, I., Sharipov, R., Stegmaier, P., Kolpakov, F., Filipenko, M., Kel, A. (2018) 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 Med Genomics. 11(Suppl 1):12. doi: 10.1186/s12920-018-0330-5. Link

 

Triska, M., Solovyev, V., Baranova, A., Kel, A., Tatarinova, T.V. (2017) Nucleotide patterns aiding in prediction of eukaryotic promoters. PLoS One. 12(11):e0187243. doi: 10.1371/journal.pone.0187243. Link

 

Niehof, M., Hildebrandt, T., Danov, O., Arndt, K., Koschmann, J., Dahlmann, F., Hansen, T. and Sewald, K. (2017) RNA isolation from precision-cut lung slices (PCLS) from different species. BMC Res. Notes 10, 121. doi: 10.1186/s13104-017-2447-6. Link

 

Mandić, A. D., Bennek, E., Verdier, J., Zhang, K., Roubrocks, S., Davis, R. J., Denecke, B., Gassler, N., Streetz, K., Kel, A., Hornef, M., Cubero, F. J., Trautwein, C. and Sellge, G. (2017) c-Jun N-terminal kinase 2 promotes enterocyte survival and goblet cell differentiation in the inflamed intestine. Mucosal Immunol. 10(5):1211-1223. doi: 10.1038/mi.2016.125. Link

 

Pietrzyńska, M., Zembrzuska, J., Tomczak, R., Mikołajczyk, J., Rusińska-Roszak, D., Voelkel, A., Buchwald, T., Jampílek, J., Lukáč, M., Devínsky, F. (2016) Experimental and in silico investigations of organic phosphates and phosphonates sorption on polymer-ceramic monolithic materials and hydroxyapatite. Eur. J. Pharm. Sci. 93, 295-303. doi: 10.1016/j.ejps.2016.08.033. Link

 

Kural, K. C., Tandon, N., Skoblov, M., Kel-Margoulis, O. V. and Baranova, A. V. (2016) Pathways of aging: comparative analysis of gene signatures in replicative senescence and stress induced premature senescence. BMC Genomics 17(Suppl 14), 1030. doi: 10.1186/s12864-016-3352-4. Link

 

Kel, A. E., Stegmaier, P., Valeev, T., Koschmann, J., Poroikov, V., Kel-Margoulis, O. V. and Wingender, E. (2016) Multi-omics “upstream analysis” of regulatory genomic regions helps identifying targets against methotrexate resistance of colon cancer. EuPA Open Proteomics 13, 1-13. doi: 10.1016/j.euprot.2016.09.002. Link

 

Ciribilli, Y., Singh, P., Inga, A., Borlak, J. (2016) c-Myc targeted regulators of cell metabolism in a transgenic mouse model of papillary lung adenocarcinoma. Oncotarget 7, 65514-65539. doi: 10.18632/oncotarget.11804. Link

 

Wlochowitz, D., Haubrock, M., Arackal, J., Bleckmann, A., Wolff, A., Beißbarth, T., Wingender, E., Gültas, M. (2016) Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines. Front. Genet. 7, 42. doi: 10.3389/fgene.2016.00042. Link

 

Lee, E.H., Oh, J.H., Selvaraj, S., Park, S.M., Choi, M.S., Spanel, R., Yoon, S. and Borlak, J. (2016) Immunogenomics reveal molecular circuits of diclofenac induced liver injury in mice. Oncotarget 7, 14983-15017. doi:10.18632/oncotarget.7698. Link

 

Borlak, J., Singh, P. and Gazzana, G. (2015) Proteome mapping of epidermal growth factor induced hepatocellular carcinomas identifies novel cell metabolism targets and mitogen activated protein kinase signalling events. BMC Genomics 16, 124. doi:10.1186/s12864-015-1312-z. Link

 

Koschmann, J., Bhar, A., Stegmaier,P., Kel, A. E. and Wingender, E. (2015) “Upstream Analysis”: An integrated promoter-pathway analysis approach to causal interpretation of microarray data. Microarrays 4, 270-286. doi:10.3390/microarrays4020270. Link

 

Shi, Y., Nikulenkov, F., Zawacka-Pankau, J., Li, H., Gabdoulline, R., Xu, J., Eriksson, S., Hedström, E., Issaeva, N., Kel, A., Arnér, E.S., Selivanova, G. (2014) ROS-dependent activation of JNK converts p53 into an efficient inhibitor of oncogenes leading to robust apoptosis. Cell Death Differ. 21, 612-623. doi:10.1038/cdd.2013.186 Link

 

Schlereth, K., Heyl, C., Krampitz, A.M., Mernberger, M., Finkernagel, F., Scharfe, M., Jarek, M., Leich, E., Rosenwald, A., Stiewe, T. (2013) Characterization of the p53 Cistrome – DNA Binding Cooperativity Dissects p53’s Tumor Suppressor Functions. PLoS Genet. 9, e1003726. PubMed

 

Nikulenkov, F., Spinnler, C., Li, H., Tonelli, C., Shi, Y., Turunen, M., Kivioja, T., Ignatiev, I., Kel, A., Taipale, J., Selivanova, G. (2012) Insights into p53 transcriptional function via genome-wide chromatin occupancy and gene expression analysis. Cell Death Differ. 19, 1992-2002. PubMed

 

Zawacka-Pankau, J., Grinkevich, V.V., Hunten, S., Nikulenkov, F., Gluch, A., Li, H., Enge, M., Kel, A., Selivanova, G. (2011) Inhibition of glycolytic enzymes mediated by pharmacologically activated p53: targeting Warburg effect to fight cancer. J. Biol. Chem. 286, 41600-41615. PubMed

 

Kel, A., Kolpakov, F., Poroikov, V., Selivanova, G. (2011) GeneXplain — Identification of Causal Biomarkers and Drug Targets in Personalized Cancer Pathways. J. Biomol. Tech. 22(suppl.), S16. PubMed

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