geneXplain platform

Platform release 4.13 is available now
The comprehensive bioinformatics platform, with geneXplain‘s proprietary Upstream Analysis as particular feature
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.

gene-xplain 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.

New in release 4.0

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 2016.2)
  • 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 57.
  • GO (Gene Ontology Database) has been updated to version 06.2015.
  • Integration of new Ensembl versions (release 84), Ensembl Homo sapiens (hg38), Ensembl Mus musculus (mm10), Ensembl Rattus norvegicus (rn6)

New methods

Remove overlapping sites
Preprocess raw reads
Site search report
Convert site search summary
Find longest connected chains
Match genes and metabolites
Building flux balance data table
Flux balance constraint
Score based FBC table builder
Variance filter

New release


  • New start page icons in any of the platform’s research categories
  • Pre-release of TRANSFAC®, TRANSPATH® and HumanPSD databases (version 2018.2)
  • Update to Ensembl 91 database
  • Update to Reactome 63 database


  • New analysis methods

Construct composite modules on track (correlation) – method predicts composite module using the result of the “Site search on gene set” analysis.

Cluster track – method clusters sites in a track, what is useful for merging of closely spaced sites into one big cluster.

Compute profile thresholds – method computes profile thresholds minimizing either false negatives(minFN) or false positive(minFP) on the random DNA sequence.

Create miRNA promoters – method extracts miRNA promoters from mirprom database for a given list of miRNAs

Get transcripts track – method extracts track from a database by a transcript ID

Recalculate composite module score on new track – method takes best composite model from the given CMA result and calculates its scores on all sites of a given track.

Continue CMA – method continues prediction of composite module using results of the previous prediction as a start point. Prediction parameters are customizable.

Table Imputation – method replaces missing data in the given input table with row means.

  • New HTML report for site search analysis

You can now create a summary of your site search analysis including visualization of input promoters together with identified enriched transcription factor binding sites (TFBSs) in HTML format, which can be exported to your local computer.

  • New toolbar buttons
  • Integration with updated TRANSFAC®, TRANSPATH® and HumanPSDTM databases in release 2018.1


Enhancement of the method LRPath.

Installation of TRANSFAC 2017.3 (information download)

– Annotation of transcription factor binding sites based on sequence conservation

ChIP-Seq experiment browse pages

– Reorganization of the in vivo transcription factor bound fragment section on a Locus Report

HOCOMOCO v10 matrix library integration

Enhanced human SNP content

Ensembl version update

Installation of TRANSPATH & HumanPSD 2017.3 (information download)

– Integration of new clinical trial (CT) data sources

– Improved user data management

– Quick search for disease and drug entries

– Link-out to BRENDA professional – the comprehensive enzyme information system

New phosphorylation targets content


LRPath is a Gene Set Enrichment Analysis (GSEA) method that uses logistic regression models to discover categories that are significantly correlated with a predictor.

New protein category (TRANSPATH® isogroups) to enhance identification of master regulators. This enhancement was updated to all workflows which included ‘Regulator search’ or ‘Effector search’ method.


Installation of TRANSFAC public:

– Available for everyone

– 219 profiles (matrices) for site search tools

– Search function implemented

DESeq tool:

– Bug fixed that prevented analysis from completing correctly

– Added option to run DESeq or DESeq2


– New versions of PROTEOMETM data now named HumanPSDTM database

– Latest release 2017.2 available in the geneXpain platform

– Platform Java API available from

– Executable jar can be configured with JSON config files to invoke platform processes from the command line

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Choose product(s) you are interested in

geneXplain platformTRANSFACTRANSPATH
PASS & PharmaExpertGUSAR
Bioinformatic/ System Biology/ Pharmacogenomic services


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!


Intro video of the geneXplain platform.

Information downloads


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