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Principal Investigator: Purveshkumar Khatri
Organization: STANFORD UNIVERSITY
Fiscal Year: 2024
Award: $334,498
Funding agency: National Institute of Allergy and Infectious Diseases
ABSTRACT – Data Management and Analysis Core.
Identifying the biological features of the human immune response that correlate with and predict the
development of an effective immune response to vaccination is an overarching goal of this U19 consortium.
The Data Management and Analysis Core (DMAC) in this proposal has two roles to support this overarching
goal. First, the core will provide reliable data management service for all the data generated by the U19 study.
It will provide management of large amounts of de-identified data, along with timely data submission to NIH
databases. Second, the DMAC will apply a suite of computational tools to analyze data from human samples of
serum, plasma, or peripheral blood mononuclear cell (PBMC), and individual immune cells obtained before and
after vaccination to create new knowledge about the biological basis for effective vaccine-mediated immunity.
To achieve these goals, we have assembled a team of computational biologists and immunologists with deep
expertise in the generation and analysis of highly complex datasets of transcript abundance and metabolic
profiles, who will support Projects 1, 2, and 3 in the following aims:
Aim 1. To provide data management service for the data generated by Stanford HIPC. The DMAC will
provide efficient HIPAA-compliant data storage, backup, and transfer with adequate data security and the
protection of subject identity. It will also facilitate data sharing between the Projects, and with public by
submitting data to appropriate public repositories such as the NIH GEO and ImmPort.
Aim 2: Provide bioinformatics support to Projects 1, 2 and 3 for analyzing systems immunology data
generated in those projects. The DMAC will assist the Projects in this proposal with various statistical and
bioinformatics analysis including differential expression, pathway, transcription factor, interaction network and
other statistical analyses as needed.
Aim 3: Integrative analysis of multiomics signatures of vaccine immunity from orthogonal data sets,
and from public datasets of similar studies. Developing a holistic and system-level view of immune
responses to vaccines requires (1) tools that can accurately capture interactions across the diverse
components of the immune system as they coordinate during response to vaccination and (2) cohorts
representative of the heterogeneity observed in the real world. We will satisfy both criteria in in this aim.
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