TRANSPATH® is a database of mammalian signal transduction and metabolic pathways. As one of the earliest pathway databases ever created, it has grown since to the remarkable volume of more than 1,221,552 manually curated reactions. One of the largest pathway databases available, optimally suited for geneXplain’s proprietary Upstream Analysis

Its contents can be used for pathway analysis with the geneXplain platform, requiring separate licensing (see below).


Reaction hierarchy in the TRANSPATH® database of molecular pathways.

Reaction hierarchy in the TRANSPATH® database on molecular pathways. (Click image for an enlarged view.)

TRANSPATH organizes the information about genes/molecules and reactions according to multiple hierarchies. Its sophisticated structure makes it one of the scientifically best conceptualized pathway resources, suitable for multi-purpose uses. It is complemented by one of the richest corpora of pathway data available among all public domain and commercial sources, all manually curated by experts.

Individual reactions are documented with all experimental details, in a strictly mechanistic way that includes all reaction partners and the taxonomic origin of each molecule as reported in the published experiment (“molecular evidence level”). All evidences for a certain pathway step are accumulated to provide a more comprehensive and complete picture (“pathway step level”). On top, a semantic view is provided, which focuses on the key components only and omits mechanistic details as well as small abundant molecules (“semantic projection”). Complete networks and pathways are built from molecules and their reactions.

To consider the heterogeneity of information given in the original publications, TRANSPATH transparently but precisely differentiates protein molecules according to:

    • their relatedness within one genome
      Information can be specifically retrieved regarding:

(a) specific individual proteins,

(b) all products of a certain gene (isoforms),

(c) different family relation levels (e.g., paralogs);


    • their relatedness between different genomes (orthology)


  • their association and modification status

(a) protein complexes are specified with their exact composition;

(b) post-translational modifications are given with their exact positions in the protein.

TGFbeta network

Visualization of a part of the TGFbeta network with the geneXplain platform; data were retrieved from the TRANSPATH® database. The one molecule of the displayed network that is not genome-encoded (PtdIns(3)P), phosphatidylinositol 3-phosphate) is shown in purple.

Visualization of the TGFbeta network with the geneXplain platform; data were retrieved from the TRANSPATH® database. (Click image to enlarge the picture.)

Key features

Information about pathways
More than 1,026,270 molecules and more than 111,360 genes involved in signaling or metabolic pathways of mammals (mostly human, mouse, rat), gathered by manual expert curation.
More than one million reactions
Extracted from original scientific literature and evaluated by experts.
Experimentally verified
About 3000 experimentally verified and annotated pathways and chains.
Transcription factor – target gene interactions
More than 98,000, manually annotated and quality-checked.
Peer-reviewed scientific publications
More than 98,700 publications evaluated.
Pathway visualization tool
Highly customizable view of pathways and networks under the geneXplain platform.


Quickly access information
About signal transduction and metabolic pathway components and their reactions without tedious and time consuming literature searches.
Predict potential pathways
Targeting the genes of your interest.
Build customized regulatory and metabolic networks
With more than one million reactions extracted from original scientific literature and evaluated by experts.
Use network visualization
With advanced TRANSPATH®-based technology of the geneXplain platform.


In addition to that, all our customers enjoy the following advantages or our products:

–       Technical and scientific support or your research: rapid answers to your questions from our professional support team will be always provided

–       Secure cloud space connected with online licenses that can be accessed from any location

–       Extensive manuals, documentation, examples, and tutorials available for all our products

–       Frequent releases and updates of our databases contents and software functionality

–       Ability to request personal training sessions

–       All our servers are running on CO2-neutral water or wind power

Pathway analysis

Get a picture of TRANSPATH®

Pathway analysis and network visualization with the geneXplain platform using the TRANSPATH® database.

Pathway analysis and network visualization with the geneXplain platform using the TRANSPATH® database. (Click image to enlarge the picture.)

Learn more about pathway analysis with the geneXplain platform.

Recent applications

Selection of articles reporting about TRANSPATH applications:
  • Novikova S., Tolstova T., Kurbatov L., Farafonova T., Tikhonova O., Soloveva N., Rusanov A., Zgoda V. (2024) Systems Biology for Drug Target Discovery in Acute Myeloid Leukemia. Int. J. Mol. Sci.  25(9), 4618 (2024) Link

  • Rajavel A., Klees S., Hui Y., Schmitt A.O., Gültas M. (2022) Deciphering the Molecular Mechanism Underlying African Animal Trypanosomiasis by Means of the 1000 Bull Genomes Project Genomic Dataset. Biology (Basel). 11(5), 742. Link
  • Menck K., Wlochowitz D., Wachter A., Conradi L.C., Wolff A., Scheel A.H., Korf U., Wiemann S., Schildhaus H.U., Bohnenberger H., Wingender E., Pukrop T., Homayounfar K., Beißbarth T., Bleckmann A. (2022) High-Throughput Profiling of Colorectal Cancer Liver Metastases Reveals Intra- and Inter-Patient Heterogeneity in the EGFR and WNT Pathways Associated with Clinical Outcome. Cancers 14(9), 2084. Link
  • Kechin A.A., Ivanov A.A., Kel A.E., Kalmykov A.S., Oskorbin I.P., Boyarskikh U.A., Kharpov E.A., Bakharev S.Y., Oskina N.A., Samuilenkova O.V., Vikhlyanov I.V., Kushlinskii N.E., Filipenko M.L. (2022) Prediction of EVT6-NTRK3-Dependent Papillary Thyroid Cancer Using Minor Expression Profile. Bull Exp Biol Med. 173(2),252-256. Link
  • Myer, P. A., Kim, H., Blümel, A. M., Finnegan, E., Kel, A., Thompson, T. V., Greally, J. M., Prehn, J. H., O’Connor, D. P., Friedman, R. A., Floratos, A., & Das, S. (2022). Master Transcription Regulators and Transcription Factors Regulate Immune-Associated Differences Between Patients of African and European Ancestry With Colorectal Cancer. Gastro Hep Adv. 1(3),328-341. Link
  • Chereda, H., Bleckmann, A., Menck, K., Perera-Bel, J., Stegmaier, P., Auer, F., Kramer, F., Leha, A., & Beißbarth, T. (2021). Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer. Genome Med. 13(1),42. Link
  • Kalya M., Kel A., Wlochowitz D., Wingender E., Beißbarth T. (2021) IGFBP2 Is a Potential Master Regulator Driving the Dysregulated Gene Network Responsible for Short Survival in Glioblastoma Multiforme. Front Genet. 12, 670240. Link
  • Benjamin, S.J., Hawley, K.L., Vera-Licona, P., La Vake, C.J., Cervantes, J.L., Ruan, Y., Radolf, J.D., Salazar, J.C. (2021) Macrophage mediated recognition and clearance of Borrelia burgdorferi elicits MyD88-dependent and -independent phagosomal signals that contribute to phagocytosis and inflammation. BMC Immunol. 22, 32. Link
  • Ivanov, S., Filimonov, D., & Tarasova, O. (2021) A computational analysis of transcriptional profiles from CD8(+) T lymphocytes reveals potential mechanisms of HIV/AIDS control and progression. Comput Struct Biotechnol J. 19, 2447–2459. Link
  • Meier, T., Timm, M., Montani, M., Wilkens, L. (2021) Gene networks and transcriptional regulators associated with liver cancer development and progression. BMC Med. Genomics 14, 41. Link
  • Lloyd K., Papoutsopoulou S., Smith E., Stegmaier P., Bergey F., Morris L., Kittner M., England H., Spiller D., White M.H.R., Duckworth C.A., Campbell B.J., Poroikov V., Martins Dos Santos V.A.P., Kel A., Muller W., Pritchard D.M., Probert C., Burkitt M.D.; SysmedIBD Consortium. Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease. Dis Model Mech. 13(11), dmm044040. Link
  • Ramzan, F., Klees, S., Schmitt, A. O., Cavero, D., & Gültas, M. (2020) Identification of Age-Specific and Common Key Regulatory Mechanisms Governing Eggshell Strength in Chicken Using Random Forests. Genes (Basel). 11(4), 464. Link
  • Ayyildiz D., Antoniali G., D’Ambrosio C., Mangiapane G., Dalla E., Scaloni A., Tell G., Piazza S. (2020) Architecture of The Human Ape1 Interactome Defines Novel Cancers Signatures. Sci Rep. 10, 28. Link
  • Mekonnen, Y.A., Gültas, M., Effa, K., Hanotte, O., Schmitt, A.O. (2019) Identification of Candidate Signature Genes and Key Regulators Associated With Trypanotolerance in the Sheko Breed. Front. Genet. 10, 1095. Link
  • Nobis, C. C., Dubeau Laramée, G., Kervezee, L., Maurice De Sousa, D., Labrecque, N., & Cermakian, N. (2019) The circadian clock of CD8 T cells modulates their early response to vaccination and the rhythmicity of related signaling pathways. Proc Natl Acad Sci U S A. 116(40), 20077–20086. 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., Bukrinsky M. (2018) Modified LDL Particles Activate Inflammatory Pathways in Monocyte-derived Macrophages: Transcriptome Analysis. Curr Pharm Des. 24(26),3143-3151. 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. 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. Link
  • Malusa, F., Taranta, M., Zaki, N., Cinti, C., & Capobianco, E. (2015) Time-course gene profiling and networks in demethylated retinoblastoma cell line. Oncotarget. 6(27), 23688–23707. Link
  • Kutumova E.O., Kiselev I.N., Sharipov R.N., Lavrik I.N., Kolpakov F.A. (2012) A modular model of the apoptosis machinery. Adv Exp Med Biol. 736, 235-45. Link
  • Schuler, M., Keller, A., Backes, C., Philippar, K., Lenhof, H. P., & Bauer, P. (2011) Transcriptome analysis by GeneTrail revealed regulation of functional categories in response to alterations of iron homeostasis in Arabidopsis thaliana. BMC Plant Biol. 11, 87. Link
  • Ante M., Wingender E., Fuchs M. (2011) Integration of gene expression data with prior knowledge for network analysis and validation. BMC Res Notes. 4,520. Link
  • Chiu SC, Tsao SW, Hwang PI, Vanisree S, Chen YA, Yang NS. (2010) Differential functional genomic effects of anti-inflammatory phytocompounds on immune signaling. BMC Genomics. 11, 513. Link


Selection of publications authored by the geneXplain team:
  • Kisakol, B., Matveeva, A., Salvucci, M., Kel, A., McDonough, E., Ginty, F., Longley, D., Prehn, J. (2024) Identification of unique rectal cancer-specific subtypes. Br J Cancer. DOI Link
  • Kolpakov, F., Akberdin, I., Kiselev, I., Kolmykov, S., Kondrakhin, Y., Kulyashov, M., Kutumova, E., Pintus, S., Ryabova, A., Sharipov, R., Yevshin, I., Zhatchenko, S., & Kel, A. (2022). BioUML-towards a universal research platform. Nucleic Acids Res. 50(W1),W124–31. Link
  • Orekhov A.N., Sukhorukov V.N., Nikiforov N.G., Kubekina M.V., Sobenin I.A., Foxx K.K., Pintus S., Stegmaier P., Stelmashenko D., Kel A., Poznyak A.V., Wu W.K., Kasianov A.S., Makeev V.Y., Manabe I., Oishi Y. (2020) Signaling Pathways Potentially Responsible for Foam Cell Formation: Cholesterol Accumulation or Inflammatory Response-What is First? Int J Mol Sci. 21(8),2716. Link
  • Kel A., Boyarskikh U., Stegmaier P., Leskov L.S., Sokolov A.V., Yevshin I., Mandrik N., Stelmashenko D., Koschmann J., Kel-Margoulis O., Krull M., Martínez-Cardús A., Moran S., Esteller M., Kolpakov F., Filipenko M., Wingender E. (2019) Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer. BMC Bioinformatics. 20(Suppl 4),119. 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. 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. Link


Current TRANSPATH® release

TRANSPATH® release 2024.1

The TRANSPATH® database on mammalian signal transduction and metabolic pathways contains these new data features:

·       Reactome integration

1,365 human pathways consisting of 12,786 reactions have been integrated and converted to the TRANSPATH data model. Subsequent reactions can be viewed in dedicated pathway reports and the Pathfinder tool visualizes whole pathways with options for e.g. editing and network expansion.

·       Increase in number of reactions

6,289 new binding reactions from recent publications have been added.

·       Update of links to Wikipathways

Links from genes/proteins to the pathway database Wikipathways (20240310) have been updated.

    Price request TRANSPATH

    TRANSPATH applications

    Information downloads

    TRANSPATH® Statistics (download)
    TRANSPATH® Features (download)
    TRANSPATH® Flyer (download)
    TRANSPATH® Documentation (download)
    Sample flat file for 3 molecule entries (download)
    Sample flat file for 4 reaction entries (download)
    Sample flat file for 2 pathway entries (download)
    TRANSPATH® is a registered trademark of geneXplain GmbH.



    Wingender, E., Hogan, J., Schacherer, F., Potapov, A.P., Kel-Margoulis, O. (2007) Integrating pathway data for systems pathology. In Silico Biol. 7:S17-S25. PubMed.

    Kel, A., Voss, N., Jauregui, R., Kel-Margoulis, O., Wingender, E. (2006) Beyond microarrays: find key transcription factors controlling signal transduction pathways. BMC Bioinformatics 7:S13. PubMed.

    Krull, M., Pistor, S., Voss, N., Kel, A., Reuter, I., Kronenberg, D., Michael, H., Schwarzer, K., Potapov, A., Choi, C., Kel-Margoulis, O., Wingender, E. (2006) TRANSPATH: an information resource for storing and visualizing signaling pathways and their pathological aberrations. Nucleic Acids Res. 34:D546-D551. PubMed

    Choi, C., Crass, T., Kel, A., Kel-Margoulis, O., Krull, M., Pistor, S., Potapov, A., Voss, N., Wingender, E. (2004) Consistent re-modeling of signaling pathways and its implementation in the TRANSPATH database. Genome Inform. 15:244-254. PubMed

    Choi, C., Krull, M., Kel, A., Kel-Margoulis, O., Pistor, S., Potapov, A., Voss, N., Wingender, E. (2004) TRANSPATH – a high quality database focused on signal transduction. Comp. Funct. Genomics 5:163-168. PubMed

    Krull, M., Voss, N., Choi, C., Pistor, S., Potapov, A., Wingender, E. (2003) TRANSPATH: an integrated database on signal transduction and a tool for array analysis. Nucleic Acids Res. 31:97-100. PubMed

    Schacherer, F., Choi, C., Götze, U., Krull, M., Pistor, S., Wingender, E. (2001) The TRANSPATH signal transduction database: a knowledge base on signal transduction networks. Bioinformatics 17:1053-1057. PubMed

    Heinemeyer, T., Chen, X., Karas, H., Kel, A.E., Kel, O.V., Liebich, I., Meinhardt, T., Reuter, I., Schacherer, F., Wingender, E. (1999) Expanding the TRANSFAC database towards an expert system of regulatory molecular mechanisms. Nucleic Acids Res. 27:318-322. PubMed