Arguably the best-studied organism on earth, the bacterium Escherichia coli has been both the test bed and the beneficiary of genetic dissections, biochemical studies of proteins and metabolites, molecular biology manipulations and evolutionary experiments. More recently, E. coli has also been studied by and used to develop the rapidly advancing tools of genomics and systems biology, leading to a rapidly increasing body of data and computational tools for its analysis. Despite the critical importance of E. coli in uncovering fundamental biological truths and developing groundbreaking new technologies, there is still no online data resource that satisfies the diverse and sophisticated needs of the E. coli research community. To support the needs of this large research community, we propose to build the next generation web resource, EcoliHub2.0. EcoliHub2.0 will bring together and extend several existing software platforms to provide: 1) a powerful and intuitive user interface designed for biologists and using best practices of software engineering, 2) seamless access to state of the art tools for high-throughput """"""""omics"""""""" data, 3) expert curation of the scientific literature for commonly-used laboratory strains of E. coli, 4) powerful comparative genomics tools for E. coli and other enteric bacteria such as Salmonella, 5) use of evolutionary relationships to examine other model organisms, enteric bacterial pathogens, and human biomedical conditions, 6) integration with systems biology tools for E. coli, 7) extension and further integration of EcoliWiki for community-based annotation and primary source for educational materials. Public Health Relevance: The EcoliHub2.0 resource will promote human health by supporting research on a key model system. Thanks to evolution and the NIH's half-century of investment in pound coli research, this microbe continues to teach us about basic cellular processes common to all life, from beneficial and harmful bacteria to healthy and diseased humans. EcoliHub2.0 will also advance the state of the art for genome data resources, providing cost-effective mechanisms that can be applied to the managing the coordination of diverse experts and resources for many NIH-sponsored projects.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Resource-Related Research Projects--Cooperative Agreements (U24)
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Special Emphasis Panel (ZGM1-GDB-5 (EC))
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Portnoy, Matthew
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Sri International
Menlo Park
United States
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Gaudet, Pascale; Škunca, Nives; Hu, James C et al. (2017) Primer on the Gene Ontology. Methods Mol Biol 1446:25-37
Nehring, Ralf B; Gu, Franklin; Lin, Hsin-Yu et al. (2016) An ultra-dense library resource for rapid deconvolution of mutations that cause phenotypes in Escherichia coli. Nucleic Acids Res 44:e41
Mi, Huaiyu; Poudel, Sagar; Muruganujan, Anushya et al. (2016) PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res 44:D336-42
Foulger, R E; Osumi-Sutherland, D; McIntosh, B K et al. (2015) Representing virus-host interactions and other multi-organism processes in the Gene Ontology. BMC Microbiol 15:146
Hu, James C; Sherlock, Gavin; Siegele, Deborah A et al. (2014) PortEco: a resource for exploring bacterial biology through high-throughput data and analysis tools. Nucleic Acids Res 42:D677-84
Caspi, Ron; Altman, Tomer; Billington, Richard et al. (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 42:D459-71
Caspi, Ron; Dreher, Kate; Karp, Peter D (2013) The challenge of constructing, classifying, and representing metabolic pathways. FEMS Microbiol Lett 345:85-93
Keseler, Ingrid M; Mackie, Amanda; Peralta-Gil, Martin et al. (2013) EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res 41:D605-12
Liu, Xiaoqiu; Jiang, Huifeng; Gu, Zhenglong et al. (2013) High-resolution view of bacteriophage lambda gene expression by ribosome profiling. Proc Natl Acad Sci U S A 110:11928-33
Renfro, Daniel P; McIntosh, Brenley K; Venkatraman, Anand et al. (2012) GONUTS: the Gene Ontology Normal Usage Tracking System. Nucleic Acids Res 40:D1262-9

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