PATRIC

For other uses, see Patric (disambiguation).

PATRIC[1] (Pathosystems Resource Integration Center) is the Bacterial Bioinformatics Resource Center, an information system designed to support the biomedical research community’s work on bacterial infectious diseases via integration of vital pathogen information with rich data and analysis tools. PATRIC sharpens and hones the scope of available bacterial phylogenomic data from numerous sources specifically for the bacterial research community, in order to save biologists time and effort when conducting comparative analyses. The freely available PATRIC platform provides an interface for biologists to discover data and information and conduct comprehensive comparative genomics and other analyses in a one-stop shop. PATRIC, a project of Virginia Tech’s Cyberinfrastructure Division, is funded by the National Institutes of Allergy and Infectious Diseases (NIAID), a component of the National Institutes of Health (NIH).

What PATRIC Offers

  • Perform a PATRIC global search or conduct advanced searches based on Taxonomy, Gene Name, Locus Tag, Protein Function/Families, Pathways, EC Numbers, GO Terms, Host-Pathogen Interactions, and more.
  • Perform a BLAST search against plasmid-specific BLAST databases containing genomic sequences or proteins in PATRIC.
  • Perform enhanced literature search and text mining techniques to identify genes, proteins, diseases, drugs, organisms, and other entities of interest.

The PATRIC team is committed to supporting and growing the bacterial research communities. To help us identify your specific bioinformatic needs, please take a moment to fill out our short PATRIC survey. Your participation is greatly appreciated.

PATRIC Data Release Policy

Bacterial Organisms Covered in the PATRIC Database

Tools and Comparative Analyses on PATRIC

About Cyberinfrastructure Division and VBI

The CyberInfrastructure Division at VBI develops methods, infrastructure, and resources to help enable scientific discoveries in infectious disease research and other research fields. The group applies the principles of cyberinfrastructure to integrate data, computational infrastructure, and people. CyberInfrastructure Division has developed many public resources for curated, diverse molecular and literature data from various infectious disease systems, and implemented the processes, systems, and databases required to support them. It also conducts research by applying its methods and data to make new discoveries of its own.

The Virginia Bioinformatics Institute (VBI) at Virginia Tech has a research platform centered on understanding the "disease triangle" of host-pathogen-environment interactions in plants, humans and other animals. By successfully channeling innovation into transdisciplinary approaches that combine information technology and biology, researchers at VBI are addressing some of today’s key challenges in the biomedical, environmental, and plant sciences.

See also

References

  1. Gillespie JJ, Wattam AR, Cammer SA, et al. (November 2011). "PATRIC: the comprehensive bacterial bioinformatics resource with a focus on human pathogenic species". Infection and Immunity. 79 (11): 4286–98. doi:10.1128/IAI.00207-11. PMC 3257917Freely accessible. PMID 21896772.
  2. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990). "Basic local alignment search tool" (PDF). J Mol Biol. 215 (3): 403–410. doi:10.1016/S0022-2836(05)80360-2. PMID 2231712.
  3. Aziz, RK; Bartels, D; Best, AA; DeJongh, M; Disz, T; Edwards, RA; Formsma, K; Gerdes, S; Glass, EM; Kubal, Michael; Meyer, Folker; Olsen, Gary J; Olson, Robert; Osterman, Andrei L; Overbeek, Ross A; McNeil, Leslie K; Paarmann, Daniel; Paczian, Tobias; Parrello, Bruce; Pusch, Gordon D; Reich, Claudia; Stevens, Rick; Vassieva, Olga; Vonstein, Veronika; Wilke, Andreas; Zagnitko, Olga (2008). "The RAST Server: Rapid Annotations using Subsystems Technology". BMC Genomics. 9: 75. doi:10.1186/1471-2164-9-75. PMC 2265698Freely accessible. PMID 18261238.
  4. C. Nobata, P. Cotter, N. Okazaki, B. Rea, Y. Sasaki, Y. Tsuruoka, J. Tsujii and S. Ananiadou (2008). "Kleio: a knowledge-enriched information retrieval system for biology" (PDF). Proc. of the 31st Annual International ACM SIGIR Conference: 787–788.
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