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 software tool providers. Existing database groups provide pathway curation, while Pathway Commons provides mechanism 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 #
2U41HG006623-04
Application #
8935277
Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Di Francesco, Valentina
Project Start
2012-09-22
Project End
2018-06-30
Budget Start
2015-09-08
Budget End
2016-06-30
Support Year
4
Fiscal Year
2015
Total Cost
$969,361
Indirect Cost
$270,075
Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065
Ebhardt, H Alexander; Root, Alex; Liu, Yansheng et al. (2018) Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer. NPJ Syst Biol Appl 4:26
Liyanage, Sanduni U; Hurren, Rose; Voisin, Veronique et al. (2017) Leveraging increased cytoplasmic nucleoside kinase activity to target mtDNA and oxidative phosphorylation in AML. Blood 129:2657-2666
Michailidou, Kyriaki (see original citation for additional authors) (2017) Association analysis identifies 65 new breast cancer risk loci. Nature 551:92-94
Milne, Roger L (see original citation for additional authors) (2017) Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet 49:1767-1778
Tong, Jiefei; Helmy, Mohamed; Cavalli, Florence M G et al. (2017) Integrated analysis of proteome, phosphotyrosine-proteome, tyrosine-kinome, and tyrosine-phosphatome in acute myeloid leukemia. Proteomics 17:
Kucera, Mike; Isserlin, Ruth; Arkhangorodsky, Arkady et al. (2016) AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations. F1000Res 5:1717
?enbabao?lu, 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
Genc, Begum; Dogrusoz, Ugur (2016) An algorithm for automated layout of process description maps drawn in SBGN. Bioinformatics 32:77-84
?enbabao?lu, Yasin; Gejman, Ron S; Winer, Andrew G et al. (2016) Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol 17:231
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

Showing the most recent 10 out of 29 publications