Data management and analysis

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ABSTRACT Vaccines capitalize on our knowledge of the immune system to safeguard against infectious diseases and historically have revolutionized human health. However, the powerful success of vaccination strategies is not complete and certain vulnerable populations, even when receiving the recommended regimen, have ineffective or inefficient responses. A fundamental challenge in clinical medicine and public health is to understand the mechanisms underlying the heterogeneity of responses in these populations in order to design rational approaches to improve vaccine strategies. Addressing this challenge will require integration of information about the complex interactions among cells and molecules of the immune system across time and space. The projects in this proposal will carry out systems-level immune profiling of vaccination responses across three cohorts of vulnerable patient populations: autoimmune patients with B cell depletion (Project 1), aged and frail individuals (Project 2), and patients with sickle cell disease (Project 3). A multi-omics immune profiling core will generate multi-omics data from longitudinal samples of circulating immune cells along with tissue biopsies to capture the early response in situ in the skin and peak responses in draining lymph nodes. This Data Management and Analysis Core (DMAC), led by Dr. Steven Kleinstein, will support the database, bioinformatics analysis and complex data analysis needs of Projects 1, 2 and 3. In addition, the core will collaborate with project investigators on experimental design and reporting, and provide a training environment for project personnel on software tools and principles of methods and interpretation of results. Specifically, the DMAC will: (1) Provide a secure data management system for clinical and research data. (2) Submit data to the NIAID ImmPort repository and other public portals designated by NIAID, to ensure that all data are findable, accessible, interoperable, and reusable (FAIR). (3) Provide comprehensive biostatistical analysis support and consultation. (4) Analyze and integrate cohort-wide data using multivariate statistical approaches to discover immune signatures associated with vulnerable populations and differential vaccine responses. (5) Support investigators in Projects 1-3 by carrying out analyses to address their scientific questions. (6) Collaborate with Core C (Multi-omic Immune Profiling) on pre-processing of all immune profiling data, submission of raw and computable data into the management system, and systems-level analyses. (7) Integrate data across projects to detect common and unique signatures of vaccine responses. (8) Actively participate in the HIPC Data Management and Integration subcommittee. (9) Continue our efforts within HIPC towards the development and adoption of common data standards.