BV-BRC is the Bacterial and Viral Bioinformatic Resource Center in this program, providing access to comprehensive datasets including genomics, structures, functions, and more. BV-BRC also provides computational tools to quickly analyze data and make predictions using artificial intelligence techniques. The BV-BRC platform enables researchers to fully maximize the value of data related to pathogens. In addition, it has been designed to enable next-generation infectious disease research by creating new datasets and tools that help researchers take advantage of high-performance computing, machine learning, and other advanced informatics approaches.
The BV-BRC project joins two existing legacy centers, PATRIC, for bacteria, and IRD/ViPR, for viruses. Both resources are used extensively by the research community with 33,000+ and 8,300+ active users respectively, and over 10,000 total citations. The new BV-BRC is integrating these resources into a common infrastructure that supports richer scientific data and more powerful analytic tools. The BV-BRC team has fostered extensive community outreach by conducting 100+ multi-day training workshops for 3,000+ participants over the past 10 years and have a strong record of collaborative interactions.
The combined BV-BRC currently hosts over 350,000 bacterial and 3,000,000 viral genomes that are uniformly annotated along with curated metadata, such as isolation source and clinical phenotypes. In addition, BV-BRC hosts data on protein structure and function, clinical studies, drug targets and resistance, epidemiology, and other features. It also provides open source tools for data analysis and genomic annotation, with private workspaces for users to analyze their own data. Detailed descriptions of the resources can be found in the following publications:
- The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities (Davis et al. 2020)
- Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center (Wattam et al. 2017)
- Influenza Research Database: An integrated bioinformatics resource for influenza virus research (Zhang et al. 2016)
- Database and Analytical Resources for Viral Research Community (Phadke et al. 2019)
Research in BV-BRC focuses on developing tools and methods to help researchers address important health issues. Antimicrobial resistance (AMR) is a growing international concern as bacteria develop immunity to existing antibiotics, limiting our ability to treat infections. The BV-BRC team has developed machine learning methods to predict AMR from bacterial genome sequences (Davis et al. 2016) and has worked with collaborators to better understand and characterize the mechanisms of susceptibility (Gagetti et al. 2019, Nguyen et al. 2018, Saranathan et al. 2020). BV-BRC research also supports understanding and methodologies to address chronic viral diseases such as influenza (Lee et al. 2015, Vincent et al. 2017), and emerging epidemic viral diseases such as COVID-19 (Grifoni et al. 2020).
- Anderson TK, Macken CA, Lewis NS, et al. A Phylogeny-Based Global Nomenclature System and Automated Annotation Tool for H1 Hemagglutinin Genes from Swine Influenza A Viruses. mSphere. 2016;1(6):e00275-16. Published 2016 Dec 14. doi:10.1128/mSphere.00275-16. PMID: 27981236.
- Davis, J. J., Boisvert S, Brettin T, Kenyon RW, Mao C, Olson R, Overbeek R, Santerre J, Shukla M, Wattam AR, Will R, Xia F, Stevens R. "Antimicrobial Resistance Prediction in PATRIC and RAST." Sci Rep (2016) 6:27930. PMID: 27297683. PMCID: PMC4906388.
- Gagetti P, Wattam AR, Giacoboni G, De Paulis A, Bertona E, Corso A, Rosato AE. Identification and molecular epidemiology of methicillin resistant Staphylococcus pseudintermedius strains isolated from canine clinical samples in Argentina. BMC Vet Res. 2019 Jul 27;15(1):264. doi: 10.1186/s12917-019-1990-x.PMID: 31351494. PMCID: PMC6660709.
- Grifoni A, Sidney J, Zhang Y, Scheuermann RH, Peters B, Sette A. A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2. Cell Host Microbe. 2020 Mar 12. pii: S1931-3128(20)30166-9. doi: 10.1016/j.chom.2020.03.002. PMID: 32183941.
- Lee AJ, Das SR, Wang W, Fitzgerald T, Pickett BE, Aevermann BD, Topham, DJ, Falsey AR, Scheuermann RH. Diversifying selection analysis predicts antigenic evolution of 2009 pandemic H1N1 influenza A virus in humans. J Virol., 2015; doi: 10.1128/JVI.03636-14. PMID: 25741011.
- Nguyen M, Long SW, McDermott PF, Olsen RJ, Olson R, Stevens RL, Tyson GH, Zhao S, Davis JJ. Using machine learning to predict antimicrobial minimum inhibitory concentrations and associated genomic features for nontyphoidal Salmonella. Journal of clinical microbiology. 2019 Feb 1;57(2):e01260-18. PMID: 30333126. DOI: 10.1128/JCM.01260-18.
- Nguyen M, Brettin T, Long SW, Musser JM, Olsen RJ, Olson R, Shukla M, Stevens RL, Xia F, Yoo H, Davis JJ. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci Rep. 2018 Jan 11;8(1):421. doi: 10.1038/s41598-017-18972-w. PMID: 29323230. PMCID: PMC5765115.
- Saranathan R, Levi MH, Wattam AR, Malek A, Asare E, Behin DS, Pan DH, Jacobs WR Jr, Szymczak WA. J Clin Microbiol. Helicobacter pylori Infections in the Bronx, New York: Surveying Antibiotic Susceptibility and Strain Lineage by Whole-Genome Sequencing. 2020 Feb 24;58(3). pii: e01591-19. doi: 10.1128/JCM.01591-19. Print 2020 Feb 24. PMID: 31801839 PMCID: PMC7041580.
The BV-BRC team includes collaborators from the University of Chicago (UC), the University of Virginia (UVA), the Fellowship for Interpretation of Genomes (FIG), and the J. Craig Venter Institute (JCVI).