Network models of biology, whether curated or derived from large-scale data analysis, are critical tools in the understanding of cancer mechanisms and in the design and personalization of therapies. The NDEx Project (Network Data Exchange) will create, deploy, and maintain an open-source, web-based software platform and public website to enable scientists, organizations, and software applications to share, store, manipulate, and publish biological networks. The system will present a user interface focused on the needs of biologists, such as cancer researchers and developers of drugs and biomarkers. NDEx will support diverse types of biological networks, including basic formats such as SIF and XGMML and semantically rich formats such as OpenBEL, SBML, BioPAX, and PSI-MI. It will create a common ground for the exchange of biological information in multiple formats and will promote interoperability and convergence of semantics. NDEx will be closely integrated with the widely used Cytoscape Open Source bioinformatics environment to leverage its extensive capabilities in biological network analysis, visualization, and modeling. The NDEx site will also enable use by external applications via a web REST API, simplifying access to biological networks by analytic and therapeutic applications. NDEx will apply practices from social networking and collaboration systems to create a novel information commons where scientists share analysis results, hypotheses, and mechanistic models expressed as networks. Scientists and organizations using NDEx will control access to their networks, facilitating public sharing and private, pre-publication collaboration. NDEx users will be able to publish immutable versions suitable for literature reference via stable URIs and DOIs. This collaboration infrastructure will create an environment in which novel models of scientific discourse and publishing can evolve. NDEx will be designed as an everyday tool for cancer researchers and other biologists, enabling practical collaboration, review, discussion, analysis, and publication mediated by biological networks. It will be a backbone of aggregation and interoperability for diverse applications and knowledge sources.
The NDEx website will accelerate cancer research by providing scientists access to a broad range of biological knowledge in network form for search, visualization, download and direct use by analytic software. Moreover, it will create novel opportunities for collaboration, discussion and publishing based on biological networks. Finally, it will be unique in its focus on the needs of the bench biologist.
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| Auer, Florian; Hammoud, Zaynab; Ishkin, Alexandr et al. (2018) ndexr-an R package to interface with the network data exchange. Bioinformatics 34:716-717 |
| 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 |
| 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 |
| Bui, Nam; Huang, Justin K; Bojorquez-Gomez, Ana et al. (2018) Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol Cancer Ther 17:1585-1594 |
| Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku et al. (2018) Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 6:484-495.e5 |
| Marty, Rachel; Kaabinejadian, Saghar; Rossell, David et al. (2017) MHC-I Genotype Restricts the Oncogenic Mutational Landscape. Cell 171:1272-1283.e15 |
| Carter, Hannah; Marty, Rachel; Hofree, Matan et al. (2017) Interaction Landscape of Inherited Polymorphisms with Somatic Events in Cancer. Cancer Discov 7:410-423 |
| Pratt, Dexter; Chen, Jing; Pillich, Rudolf et al. (2017) NDEx 2.0: A Clearinghouse for Research on Cancer Pathways. Cancer Res 77:e58-e61 |
| Hofree, Matan; Carter, Hannah; Kreisberg, Jason F et al. (2016) Challenges in identifying cancer genes by analysis of exome sequencing data. Nat Commun 7:12096 |
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