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-BST-N (40))
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
Yu, Michael Ku; Kramer, Michael; Dutkowski, Janusz et al. (2016) Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems. Cell Syst 2:77-88
Gauthier, Nicholas Paul; Reznik, Ed; Gao, Jianjiong et al. (2016) MutationAligner: a resource of recurrent mutation hotspots in protein domains in cancer. Nucleic Acids Res 44:D986-91
Narayan, Soumil; Bader, Gary D; Reimand, Jüri (2016) Frequent mutations in acetylation and ubiquitination sites suggest novel driver mechanisms of cancer. Genome Med 8:55
Genc, Begum; Dogrusoz, Ugur (2016) An algorithm for automated layout of process description maps drawn in SBGN. Bioinformatics 32:77-84
Jones, Robert A; Robinson, Tyler J; Liu, Jeff C et al. (2016) RB1 deficiency in triple-negative breast cancer induces mitochondrial protein translation. J Clin Invest 126:3739-3757
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
Saito, Rintaro; Rocanin-Arjo, Anaïs; You, Young-Hyun et al. (2016) Systems biology analysis reveals role of MDM2 in diabetic nephropathy. JCI Insight 1:e87877
Jaeger, Philipp A; Lucin, Kurt M; Britschgi, Markus et al. (2016) Network-driven plasma proteomics expose molecular changes in the Alzheimer's brain. Mol Neurodegener 11:31
Vlasblom, James; Zuberi, Khalid; Rodriguez, Harold et al. (2015) Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli. Bioinformatics 31:306-10

Showing the most recent 10 out of 114 publications