The overall goal of the Bioinformatics Core is to use advanced bioinformatics tools for identification and improved understanding of the innate and adaptive immune response in vaccination and diseases. The Bioinformatics Core will utilize existing informatics platforms, and adapt them as needed, to achieve these goals. The goals of the Bioinformatics Core will (1) provide robust bioinformatics methods to analyze the data generated by Projects 1, 2 and 3, (2) serve data to the Projects for hypothesis testing, and (3) publish the data for public availability in repositories including the NIAID-funded ImmPort, and the NCBI Gene Expression Omnibus (GEO). The Bioinformatics Core will directly work with all Projects to address their need for robust bioinformatics techniques. The Bioinformatics Core will create a central repository of the genomic and immune profiling data, generated by all Projects, and integrate with genomic and immune profiling data from public repositories, to enable multi-cohort integrated analysis. Furthermore, the Bioinformatics Core will work closely with the Genomics Core and the Human Immune Monitoring Center (HIMC) for this purpose. This will enable participating Projects to maximally utilize the genomic, immune monitoring and clinical phenotypic data sets to determine functional dependencies among the measured elements and to direct further biological validation of these putative dependencies.

Public Health Relevance

The Bioinformatics Core will facilitate analysis of genomic and immune profiling data generated by the Genomics Core and the Human Immune Monitoring Center (HIMC) for the Projects of this proposal. The Bioinformatics Core will also compare the results in publicly genomic and immune data repositories (e.g., the NCBI GEO and ImmPort) for in silico validation in independent cohorts and to generated further refined hypotheses. The Bioinformatics Core will work closely with all Projects and the Genomics Core and HIMC to achieve its goals.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AI057229-14
Application #
9250077
Study Section
Special Emphasis Panel (ZAI1-LAR-I)
Project Start
2003-09-30
Project End
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
14
Fiscal Year
2017
Total Cost
$126,167
Indirect Cost
$47,558
Name
Stanford University
Department
Type
Domestic Higher Education
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
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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