Analysis of biological networks has exploded in recent years. A wide variety of technologies have been introduced for mapping networks of gene and protein interactions, including yeast-two-hybrid assays, afTinity purification coupled to mass spectrometry, chromatin immunoprecipitation measurements, synthetic-lethal and suppressor networks, expression QTLs, and many others. These technologies have the potential to revolutionize the study of human health, by enabling the construction of large pathway maps which can pinpoint the molecular mechanisms underlying normal and disease states of biological systems. However, the enormous variety and number of new molecular interaction measurements necessitate new algorithms, conceptual frameworks, and software to integrate, query, visualize, and interpret the resulting network data.
The aim of the Resource for Analysis, Visualization, and Integration of Networks (RAVIN) is to provide a freely available, open-source suite of software technology that broadly enables network-based visualization, analysis, and biomedical discovery for NIH-funded researchers. Our overall objectives are to assemble large-scale biological data into models of networks and pathways and to use these networks to better understand how biological systems operate under normal conditions ahd how they fail in disease. These goals will be carried out in four Technology Research and Development projects: A. Network-based biomarkers for disease classification;B. Recognizing trend motifs and dynamics in social networks;C. Network visualization and representation;and D. Predictive modeling and network inference. The centerpiece of our software development and support efforts will be Cytoscape, currently one ofthe most functional and widely used network visualization and analysis tools. Now in its sixth year of development, Cytoscape is co-developed by a multi-disciplinary team of institutions known as the Cytoscape Consortium, who also comprise the host institutions of our proposed resource.

Public Health Relevance

The RAVIN National Resource will benefit public health by transforming large networks of molecular interactions into knowledge about the mechanisms of disease. It will mine and visualize molecular networks to assemble quantiative and qualitative models of biological pathways and to use these models to predict patient outcomes.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Biotechnology Resource Grants (P41)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Ravichandran, Veerasamy
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California San Diego
Internal Medicine/Medicine
Schools of Medicine
La Jolla
United States
Zip Code
Buckley, Alexandra R; Ideker, Trey; Carter, Hannah et al. (2018) Exome-wide analysis of bi-allelic alterations identifies a Lynch phenotype in The Cancer Genome Atlas. Genome Med 10:69
Wang, Sheng; Ma, Jianzhu; Zhang, Wei et al. (2018) Typing tumors using pathways selected by somatic evolution. Nat Commun 9:4159
Zhang, Wei; Ma, Jianzhu; Ideker, Trey (2018) Classifying tumors by supervised network propagation. Bioinformatics 34:i484-i493
Nikolayeva, Iryna; Guitart Pla, Oriol; Schwikowski, Benno (2018) Network module identification-A widespread theoretical bias and best practices. Methods 132:19-25
Zhang, Wei; Bojorquez-Gomez, Ana; Velez, Daniel Ortiz et al. (2018) A global transcriptional network connecting noncoding mutations to changes in tumor gene expression. Nat Genet 50:613-620
Reznik, Ed; Luna, Augustin; Aksoy, B├╝lent Arman et al. (2018) A Landscape of Metabolic Variation across Tumor Types. Cell Syst 6:301-313.e3
Huang, Justin K; Jia, Tongqiu; Carlin, Daniel E et al. (2018) pyNBS: a Python implementation for network-based stratification of tumor mutations. Bioinformatics 34:2859-2861
MacParland, Sonya A; Liu, Jeff C; Ma, Xue-Zhong et al. (2018) Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun 9:4383
Pai, Shraddha; Bader, Gary D (2018) Patient Similarity Networks for Precision Medicine. J Mol Biol 430:2924-2938
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

Showing the most recent 10 out of 145 publications