The purpose of the Bioinformatics Core is to develop, validate, and use several established bioinformatics tools for the study of protective mechanisms against pandemic respiratory virus. The informatics platforms developed through the Bioinformatics Core will be used to study samples provided by Projects 1-3. Datasets, protocols, reagent lists, and informatics tools will be placed on a website that will be developed as part of this proposal. The goals of the Bioinformatics Core will be (1) to connect with all the various Projects that are generating primary data, (2) to acquire all the relevant data and store it in open formats, (3) to collaborate with the other NIH-funded Cooperative Centers for Translational Research on Human Immunology and Biodefense and share data in a bi-directional manner, (4) to serve data to the Projects for hypothesis testing, (5) to publish the data for public availability, and (6) to provide robust statistical and analytical methods to analyze the data. The highest priority for the Bioinformatics Core is to directly work with all Projects to address their need for robust statistical techniques. In addition to analytic support, the Bioinformatics Core will operationalize collaboration, data-, and method-sharing with other NIH-funded Cooperative Centers for Translational Research on Human Immunology and Biodefense. Finally, the Bioinformatics Core will work with all Projects to publish data to the Internet. To achieve these goals, the Bioinformatics Core will create a software infrastructure to enable state-of-the-art distribution, storage and analysis of multiple types of genome-scale data. This will enable researchers from all Cooperative Centers to maximally utilize the genomic, proteomic, immunogenomic, and phenotypic data sets to determine functional dependencies among the measured elements and direct further biological validation of these putative dependencies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AI057229-10
Application #
8508806
Study Section
Special Emphasis Panel (ZAI1-KS-I)
Project Start
Project End
Budget Start
2013-04-01
Budget End
2014-03-31
Support Year
10
Fiscal Year
2013
Total Cost
$113,809
Indirect Cost
$32,253
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Sibener, Leah V; Fernandes, Ricardo A; Kolawole, Elizabeth M et al. (2018) Isolation of a Structural Mechanism for Uncoupling T Cell Receptor Signaling from Peptide-MHC Binding. Cell 174:672-687.e27
Ju, Chia-Hsin; Blum, Lisa K; Kongpachith, Sarah et al. (2018) Plasmablast antibody repertoires in elderly influenza vaccine responders exhibit restricted diversity but increased breadth of binding across influenza strains. Clin Immunol 193:70-79
Sweeney, Timothy E; Perumal, Thanneer M; Henao, Ricardo et al. (2018) A community approach to mortality prediction in sepsis via gene expression analysis. Nat Commun 9:694
Davis, Mark M; Tato, Cristina M (2018) Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? Seeing the Forest Rather than a Few Trees. Cold Spring Harb Perspect Biol 10:
Gee, Marvin H; Han, Arnold; Lofgren, Shane M et al. (2018) Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell 172:549-563.e16
Keeffe, Jennifer R; Van Rompay, Koen K A; Olsen, Priscilla C et al. (2018) A Combination of Two Human Monoclonal Antibodies Prevents Zika Virus Escape Mutations in Non-human Primates. Cell Rep 25:1385-1394.e7
Wagar, Lisa E; DiFazio, Robert M; Davis, Mark M (2018) Advanced model systems and tools for basic and translational human immunology. Genome Med 10:73
Good, Zinaida; Sarno, Jolanda; Jager, Astraea et al. (2018) Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24:474-483
Satpathy, Ansuman T; Saligrama, Naresha; Buenrostro, Jason D et al. (2018) Transcript-indexed ATAC-seq for precision immune profiling. Nat Med 24:580-590
Vallania, Francesco; Tam, Andrew; Lofgren, Shane et al. (2018) Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases. Nat Commun 9:4735

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