Version 8: Over 42 000 Pathways and 1 350 000 Interactions from 22 Data Sources
Pathway information. Single point of access.
Pathway Commons aims to store and disseminate knowledge about biological pathways. Information is sourced from public pathway databases and is readily searched, visualized, and downloaded. The data is freely available under the license terms of each contributing database.Pathway Commons, a web resource for biological pathway data. Cerami E et al. Nucleic Acids Research (2011).
Visualize, Edit, and Analyze Pathways.
Build apps. Dig into BioPAX. Use R.
BioPAX level 3 integrated data
PaxTools Java library
PC BioPAX integrated data -
SPARQL endpoint and Faceted browser
Validate your BioPAX models
Web and console tool for pathway data authors
Pathway Commons is a collection of publicly available pathway information from multiple organisms. It provides researchers with convenient access to a comprehensive collection of biological pathways from multiple sources represented in a common language for gene and metabolic pathway analysis. Access is via a web portal for query and download. Database providers can share their pathway data via a common repository and avoid duplication of effort and reduce software development costs. Bioinformatics software developers can increase efficiency by sharing pathway analysis software components. Pathways can include biochemical reactions, complex assembly, transport and catalysis events, physical interactions involving proteins, DNA, RNA, small molecules and complexes, gene regulation events and genetic interactions involving genes.
Yes, the Pathway Commons data are available for free! Pathway Commons distributes pathway information with the intellectual property restrictions of the source database. However, only databases that are freely available or free to academics are included. Additionally, this site provides several free pathway analysis software examples to conduct gene pathway analysis.
No. Pathway Commons does not compete with or duplicate efforts of pathway databases or software tool providers. Pathway Commons will add value to these existing efforts by providing a shared resource for publishing, distributing, querying, and analyzing pathway information. Existing database groups will provide pathway curation, Pathway Commons will provide a mechanism and the technology for sharing. A key aspect of Pathway Commons is clear author attribution. Curation teams at existing databases must be supported by researchers to ensure they can keep performing their valuable work.
The Pathway Commons work group will continue to provide software systems to collect, store and integrate pathway data from database groups, with clear author attribution; store, validate, index and maintain the information to enable efficient, quality access; distribute pathway information to the scientific public; and, provide a basic set of end user software for querying and analysis of metabolic and gene pathways. We will be adding more databases over time.
BioPAX, or Biological Pathway Exchange, is a standard exchange format for biological pathways. Pathway databases that make their data available in this format can be imported into Pathway Commons. BioPAX is developed through a collaborative effort by many pathway databases. More information is available at http://biopax.org.
Benefits of exporting your data to BioPAX and distributing it via Pathway Commons include:
Pathway Commons will avoid duplication of advanced features of source databases. Users are encouraged to explore these features by following hyperlinks from Pathway Commons.
You can freely query available pathway information and answer questions such as:
Pathways from different databases are defined by different levels of detail. Details that may be included are proteins, small molecules, DNA, RNA, complexes and their cellular locations, different types of physical interactions, such as molecular interaction, biochemical reaction, catalysis, complex assembly and transport, gene regulation, genetic interactions, post-translational protein modifications, original citations, experimental evidence and links to other databases e.g. of protein sequence annotation. Some information is only available in the downloaded BioPAX files.
Pathways were downloaded directly from source databases. Each source pathway database has been created differently, some by manual extraction of pathway information from the literature and some by computational prediction.
The quality of Pathway Commons pathways is dependent on the quality of the pathways from source databases. Pathway Commons allows users to filter data by various criteria, including data source, which should allow viewing a restricted subset of high quality data. In the future, Pathway Commons will implement published algorithms to automatically assess data quality and allow this as an additional filter.
You can download and incorporate this biological pathway data as part of metabolic and gene pathway analysis software in BioPAX Level 3 format. Details about the BioPAX format
Please see the statistics page for up to the minute information.
cPath2 is an open-source data management software that runs the Pathway Commons web service. You can download it for your own use the developer site.
Yes! A web service is available to answer specific queries with computer readable responses for intergration with other network analysis components. This is designed to enable third party software and scripts to easily access the information.
|Aksoy B.A., et al.||Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles||Bioinformatics||2014|
|Babur O., et al.||Pattern search in biopax models||Bioinformatics||2014|
|Mitra S., et al.||Systems biology of cancer biomarker detection||Cancer Biomarkers||2013|
|Wodak S.J., et al.||Protein-protein interaction networks: The puzzling riches||Current Opinion in Structural Biology||2013|
|Araujo G.S., et al.||Random forest and gene networks for association of SNPs to Alzheimer's disease||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||2013|
|Hofree M., et al.||Network-based stratification of tumor mutations||Nature Methods||2013|
|Snijder B., et al.||Predicting functional gene interactions with the hierarchical interaction score||Nature Methods||2013|
|Li J., et al.||Pathway-based drug repositioning using causal inference||BMC Bioinformatics||2013|
|Wang Z., et al.||Incorporating prior knowledge into Gene Network Study||Bioinformatics||2013|
|Tamborero D., et al.||Comprehensive identification of mutational cancer driver genes across 12 tumor types||Scientific Reports||2013|
|Klapa M.I., et al.||Reconstruction of the experimentally supported human protein interactome: What can we learn?||BMC Systems Biology||2013|
|Tieri P., et al.||Signalling pathway database usability: Lessons learned||Molecular BioSystems||2013|
|Carbonetto P., et al.||Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes, and Cytokine Signaling Genes in Crohn's Disease||PLoS Genetics||2013|
|Croze E., et al.||Interferon-beta-1b-induced short-and long-term signatures of treatment activity in multiple sclerosis||Pharmacogenomics Journal||2013|
|Miller M.L., et al.||Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets||Science Signaling||2013|
|Grandori C.||A high-throughput siRNA screening platform to identify MYC-synthetic lethal genes as candidate therapeutic targets||Methods in Molecular Biology||2013|
|Cun Y., et al.||Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics||PLoS ONE||2013|
|Melas I.N., et al.||Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs||PLoS Computational Biology||2013|
|Demir E., et al.||Using Biological Pathway Data with Paxtools||PLoS Computational Biology||2013|
|Kortenhorst M.S.Q., et al.||Analysis of the genomic response of human prostate cancer cells to histone deacetylase inhibitors||Epigenetics||2013|
|Aksoy B.A., et al.||PiHelper: An open source framework for drug-target and antibody-target data||Bioinformatics||2013|
|Sambarey A., et al.||Mining large-scale response networks reveals 'topmost activities' in Mycobacterium tuberculosis infection||Scientific Reports||2013|
|Garcia Godoy M.J., et al.||Sharing and executing linked data queries in a collaborative environment||Bioinformatics||2013|
|Ho A.S., et al.||The mutational landscape of adenoid cystic carcinoma||Nature Genetics||2013|
|Rajagopalan P., et al.||Systems biology characterization of engineered tissues||Annual Review of Biomedical Engineering||2013|
|Stobbe M.D., et al.||Consensus and conflict cards for metabolic pathway databases||BMC Systems Biology||2013|
|Praveen P., et al.||Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources||PLoS ONE||2013|
|Goudarzi A., et al.||Protein kinase c epsilon and genetic networks in osteosarcoma metastasis||Cancers||2013|
|Sun J., et al.||IBIG: An Integrative Network Tool for Supporting Human Disease Mechanism Studies||Genomics, Proteomics and Bioinformatics||2013|
|Pagliarini R., et al.||A genome-scale modeling approach to study inborn errors of liver metabolism: Toward an in silico patient||Journal of Computational Biology||2013|
|Sorokina S.Y., et al.||Databases as instruments for analysis of large-scale data sets of interactions between molecular biological objects||Biology Bulletin||2013|
|Sadeghi A., et al.||Steiner tree methods for optimal sub-network identification: An empirical study||BMC Bioinformatics||2013|
|Dolinski K., et al.||Systematic curation of protein and genetic interaction data for computable biology||BMC Biology||2013|
|Fanayan S., et al.||Proteogenomic analysis of human colon carcinoma cell lines LIM1215, LIM1899, and LIM2405||Journal of Proteome Research||2013|
|Gao J., et al.||Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal||Science Signaling||2013|
|Gu Y., et al.||Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for glioblastoma||Molecular BioSystems||2013|
|Mukherjee S., et al.||Current trends in modeling host-pathogen interactions||Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery||2013|
|Liu Z., et al.||In silico drug repositioning-what we need to know||Drug Discovery Today||2013|
|Mayer M.L., et al.||Rescue of dysfunctional autophagy attenuates hyperinflammatory responses from cystic fibrosis cells||Journal of Immunology||2013|
|Kamburov A., et al.||The ConsensusPathDB interaction database: 2013 Update||Nucleic Acids Research||2013|
|Zhao M., et al.||TSGene: A web resource for tumor suppressor genes||Nucleic Acids Research||2013|
|Chatr-Aryamontri A., et al.||The BioGRID interaction database: 2013 Update||Nucleic Acids Research||2013|
|Cheng L., et al.||Global gene expression and functional network analysis of gastric cancer identify extended pathway maps and GPRC5A as a potential biomarker||Cancer Letters||2012|
|Johnson S., et al.||StRAP: An Integrated Resource for Profiling High-Throughput Cancer Genomic Data from Stress Response Studies||PLoS ONE||2012|
|Droniou-Bonzom M.E., et al.||A systems biology starter kit for arenaviruses||Viruses||2012|
|Mitsos A., et al.||Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways||PLoS ONE||2012|
|West J., et al.||Differential network entropy reveals cancer system hallmarks||Scientific Reports||2012|
|Blinov M.L., et al.||Logic modeling and the ridiculome under the rug||BMC Biology||2012|
|Feiglin A., et al.||Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks||Bioinformatics||2012|
|Saito R., et al.||A travel guide to Cytoscape plugins||Nature Methods||2012|
|Videla S., et al.||Revisiting the training of logic models of protein signaling networks with ASP||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||2012|
|Terfve C., et al.||CellNOptR: A flexible toolkit to train protein signaling networks to data using multiple logic formalisms||BMC Systems Biology||2012|
|Eo H.-S., et al.||A pathway-based classification of breast cancer integrating data on differentially expressed genes, copy number variations and microrna target genes||Molecules and Cells||2012|
|Forst C.V.||Influenza infection and therapy: A systems approach||Future Virology||2012|
|Tsuji S., et al.||A simple knowledge-based mining method for exploring hidden key molecules in a human biomolecular network||BMC Systems Biology||2012|
|Wyner A., et al.||Argumentation to represent and reason over biological systems||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||2012|
|Guziolowski C., et al.||Automatic generation of causal networks linking growth factor stimuli to functional cell state changes||FEBS Journal||2012|
|Eduati F., et al.||Integrating literature-constrained and data-driven inference of signalling networks||Bioinformatics||2012|
|Engin H.B., et al.||A strategy based on protein-protein interface motifs may help in identifying drug off-targets||Journal of Chemical Information and Modeling||2012|
|Davis M.P.A., et al.||Large-scale identification of microRNA targets in murine Dgcr8-deficient embryonic stem cell lines||PLoS ONE||2012|
|Helikar T., et al.||The Cell Collective: Toward an open and collaborative approach to systems biology||BMC Systems Biology||2012|
|Tang H., et al.||A quick guide to biomolecular network studies: Construction, analysis, applications, and resources||Biochemical and Biophysical Research Communications||2012|
|Kavlock R., et al.||Update on EPA's ToxCast program: Providing high throughput decision support tools for chemical risk management||Chemical Research in Toxicology||2012|
|Al-Lazikani B., et al.||Combinatorial drug therapy for cancer in the post-genomic era||Nature Biotechnology||2012|
|Carkacioglu L., et al.||iSNP: An integrated, automatically updated SNP database||2012 7th International Symposium on Health Informatics and Bioinformatics, HIBIT 2012||2012|
|Komurov K., et al.||NetWalker: A contextual network analysis tool for functional genomics||BMC Genomics||2012|
|Papp D., et al.||The NRF2-related interactome and regulome contain multifunctional proteins and fine-tuned autoregulatory loops||FEBS Letters||2012|
|Mori T., et al.||NIRF/UHRF2 occupies a central position in the cell cycle network and allows coupling with the epigenetic landscape||FEBS Letters||2012|
|Weile J., et al.||Bayesian integration of networks without gold standards||Bioinformatics||2012|
|Julfayev E.S., et al.||KB-Rank: Efficient protein structure and functional annotation identification via text query||Journal of Structural and Functional Genomics||2012|
|Kirik U., et al.||Multimodel pathway enrichment methods for functional evaluation of expression regulation||Journal of Proteome Research||2012|
|Cerami E., et al.||The cBio Cancer Genomics Portal: An open platform for exploring multidimensional cancer genomics data||Cancer Discovery||2012|
|Kirouac D.C., et al.||Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks||BMC Systems Biology||2012|
|Cun Y., et al.||Prognostic gene signatures for patient stratification in breast cancer - accuracy, stability and interpretability of gene selection approaches using prior knowledge on protein-protein interactions||BMC Bioinformatics||2012|
|Garcia-Godoy M.J., et al.||Bioqueries: A social community sharing experiences while querying Biological Linked Data||ACM International Conference Proceeding Series||2012|
|Kholodenko B., et al.||Computational approaches for analyzing information flow in biological networks||Science Signaling||2012|
|Ronnberg T., et al.||Searching for cellular partners of hantaviral nonstructural protein NSs: Y2H screening of mouse cDNA library and analysis of cellular interactome||PLoS ONE||2012|
|Garay J.P., et al.||Omics and therapy - A basis for precision medicine||Molecular Oncology||2012|
|Bebek G.||Identifying gene interaction networks||Methods in Molecular Biology||2012|
|Kozhenkov S., et al.||Mining and integration of pathway diagrams from imaging data||Bioinformatics||2012|
|Komurov K.||Modeling community-wide molecular networks of multicellular systems||Bioinformatics||2012|
|Goh W.W.B., et al.||How advancement in biological network analysis methods empowers proteomics||Proteomics||2012|
|Ciriello G., et al.||Mutual exclusivity analysis identifies oncogenic network modules||Genome Research||2012|
|Sales G., et al.||Graphite - a Bioconductor package to convert pathway topology to gene network||BMC Bioinformatics||2012|
|Alexeyenko A., et al.||Comparative interactomics with Funcoup 2.0||Nucleic Acids Research||2012|
|Kuchta K., et al.||DNAtraffic - A new database for systems biology of DNA dynamics during the cell life||Nucleic Acids Research||2012|
|Kelder T., et al.||WikiPathways: Building research communities on biological pathways||Nucleic Acids Research||2012|
|Haibe-Kains B., et al.||Predictive networks: A flexible, open source, web application for integration and analysis of human gene networks||Nucleic Acids Research||2012|
|Cheng Y.-K., et al.||A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis||PLoS Computational Biology||2012|
|Yu N., et al.||hiPathDB: A human-integrated pathway database with facile visualization||Nucleic Acids Research||2012|
|Sreenivasaiah P.K., et al.||IPAVS: Integrated pathway resources, analysis and visualization system||Nucleic Acids Research||2012|
|Mohammad F., et al.||A heuristic algorithm for detecting intercellular interactions||Proceedings - 2011 11th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2011||2011|
|le Novere N., et al.||Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE)||Standards in Genomic Sciences||2011|
|Doderer M.S., et al.||Multisource biological pathway consolidation||Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics||2011|
|Brockschmidt F.F., et al.||Susceptibility variants on chromosome 7p21.1 suggest HDAC9 as a new candidate gene for male-pattern baldness||British Journal of Dermatology||2011|
|Hsu C.-L., et al.||Prioritizing disease candidate genes by a gene interconnectedness-based approach||10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)||2011|
|Ghosh S., et al.||Software for systems biology: From tools to integrated platforms||Nature Reviews Genetics||2011|
|Goldenberg A., et al.||Unsupervised detection of genes of influence in lung cancer using biological networks||Bioinformatics||2011|
|Frohlich H.||Network based consensus gene signatures for biomarker discovery in breast cancer||PLoS ONE||2011|
|Martha V.-S., et al.||Constructing a robust protein-protein interaction network by integrating multiple public databases||BMC Bioinformatics||2011|
|Yaspan B.L., et al.||Strategies for pathway analysis from UNIT 1.20 GWAS Data||Current Protocols in Human Genetics||2011|
|Fortney K., et al.||Integrative computational biology for cancer research||Human Genetics||2011|
|Mori T., et al.||NIRF constitutes a nodal point in the cell cycle network and is a candidate tumor suppressor||Cell Cycle||2011|
|Symons S., et al.||MGV: A generic graph viewer for comparative omics data||Bioinformatics||2011|
|Nishiyama T., et al.||A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data||BMC Bioinformatics||2011|
|Julfayev E.S., et al.||A new approach to assess and predict the functional roles of proteins across all known structures||Journal of Structural and Functional Genomics||2011|
|Saltzman A.L., et al.||Regulation of alternative splicing by the core spliceosomal machinery||Genes and Development||2011|
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Pathway Commons is a collaboration between the Bader Lab at the University of Toronto, the Sander Lab at the cBio Center for Information Biology, Dana-Farber Cancer Institute and the Computational biology collaboratory at Harvard Medical School, and the Demir Lab, Oregon Health & Science University.
Pathway Commons was originally developed at the Memorial Sloan Kettering Cancer Center and the University of Toronto.