Biological pathways represent knowledge about molecules, processes and their interactions. Maps of such pathways are used to design and analyze experiments, and for predicting the behavior of biological systems. Pathway information is extremely difficult for biologists to use in its current fragmented and incomplete state, involving a large amount of time and effort to wade through, piece together and analyze. The Pathway Commons research resource is being developed to overcome this roadblock by providing researchers with a convenient single point of access to diverse biological pathway information translated to a common data language. This project is an important step towards the development of a complete and integrated computable map of the cell across all species and developmental stages. Pathway Commons promotes and supports convergence, by the community, to a truly integrated computable and searchable representation of cellular biological processes. Pathway Commons does not compete with or duplicate efforts of pathway databases or softward tool providers. Existing database groups provide pathway curation, while Pathway Commons provides mechanims and technology for adding value, disseminating, and reducing duplication of effort. Collaboration with user and database groups is a central component, driven by the desire for maximum synergy and efficiency. The Pathway Commons resource will aggregate datasets from multiple major pathway databases;translate, store, validate, index, integrate, hyperlink and maintain the information for maximum quality access;freely deliver pathway information to the scientific public, both academic and commercial, using advanced internet technology;and, provide open-source end user software for pathway browsing and analysis. User support and training for Pathway Commons and related resources will be freely available to the scientific community.

Public Health Relevance

The completion of the human genome sequence and advances in molecular technologies has led to an explosion of biological data, which is driving biology towards increased use of computational tools. Pathway Commons is making biological knowledge available for computational processing, and is helping create predictive models of biological processes. These models will revolutionize biology and health research.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Biotechnology Resource Cooperative Agreements (U41)
Project #
1U41HG006623-01
Application #
8243036
Study Section
Special Emphasis Panel (ZHG1-HGR-M (O2))
Program Officer
Bonazzi, Vivien
Project Start
2012-09-22
Project End
2015-06-30
Budget Start
2012-09-22
Budget End
2013-06-30
Support Year
1
Fiscal Year
2012
Total Cost
$1,000,000
Indirect Cost
$329,039
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065
Genc, Begum; Dogrusoz, Ugur (2016) An algorithm for automated layout of process description maps drawn in SBGN. Bioinformatics 32:77-84
Şenbabaoğlu, Yasin; Sümer, Selçuk Onur; Sánchez-Vega, Francisco et al. (2016) A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers. PLoS Comput Biol 12:e1004765
Marcotte, Richard; Sayad, Azin; Brown, Kevin R et al. (2016) Functional Genomic Landscape of Human Breast Cancer Drivers, Vulnerabilities, and Resistance. Cell 164:293-309
Luna, Augustin; Babur, Özgün; Aksoy, Bülent Arman et al. (2016) PaxtoolsR: pathway analysis in R using Pathway Commons. Bioinformatics 32:1262-4
Nishi, Hafumi; Demir, Emek; Panchenko, Anna R (2015) Crosstalk between signaling pathways provided by single and multiple protein phosphorylation sites. J Mol Biol 427:511-20
Korkut, Anil; Wang, Weiqing; Demir, Emek et al. (2015) Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells. Elife 4:
Reznik, Ed; Sander, Chris (2015) Extensive decoupling of metabolic genes in cancer. PLoS Comput Biol 11:e1004176
Babur, Özgün; Gönen, Mithat; Aksoy, Bülent Arman et al. (2015) Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations. Genome Biol 16:45
Sari, Mecit; Bahceci, Istemi; Dogrusoz, Ugur et al. (2015) SBGNViz: A Tool for Visualization and Complexity Management of SBGN Process Description Maps. PLoS One 10:e0128985
Aksoy, Bülent Arman; Demir, Emek; Babur, Özgün et al. (2014) Prediction of individualized therapeutic vulnerabilities in cancer from genomic profiles. Bioinformatics 30:2051-9

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