The mission of the National Resource for Network Biology (nrnb.org) is to advance the science of biological networks by providing leading-edge bioinformatic methods, software and infrastructure, and by engaging the scientific community in a portfolio of collaboration and training opportunities. Biomedical research is increasingly dependent on knowledge of biological networks of multiple types and scales, including gene, protein and drug interactions, cell-cell and cell-host communication, and vast social networks. The NRNB technologies enable researchers to assemble and analyze these networks and to use them to better understand biological systems and, in particular, how they fail in disease. The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010; the present application is a competitive renewal. During the previous funding period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. We put in place major software and hardware infrastructure such as the Cytoscape App Store, an online market place of network analysis tools; GeneMANIA, a popular online network query and gene function prediction tool; cBioPortal, a powerful web portal for analyzing cancer mutations in a network and pathway context; and a high-performance computing cluster for network analysis. We established a large portfolio of research collaborations (75 currently active); two annual international network biology meetings, the Network Biology SIG and the Cytoscape Symposium; and a rich set of training opportunities and published network analysis protocols. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
Although we are all familiar with some of the components of biological systems - DNA, proteins, cells, organs, individuals - understanding life involves more than just cataloging its component parts. It is critical to understand the many interactions between these parts, and how this complex network of parts gives rise to biological functions and responses, health and disease. The National Resource for Network Biology provides the biomedical research community with a shared set of computational tools for studying a wide range of biological networks, including networks of genes and proteins, networks of cell-to-cell communication, and social networks of human individuals.
|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, Bülent 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