Bioinformatics and Data Management Core

生物信息学和数据管理核心

• 批准号: 9205191
• 负责人: Daniel L Gillen
• 金额: $ 27.81万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9205191
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Animals; Area; Bioinformatics; Biological Markers; Cells; Clinical Data; Data; Data Analyses; Data Collection; Data Sources; Databases; Development; Disease Progression; Disease model; Encyclopedia of DNA Elements; Faculty; Gene Expression; Genetic; Genetic Transcription; Genotype-Tissue Expression Project; Heterogeneity; Housing; Human; Medicine; Methodology; Methods; Modeling; Molecular Profiling; Neurology; Pathological Staging; Phenotype; Preclinical Testing; Proteomics; Protocols documentation; Recording of previous events; Research; Research Methodology; Resources; Source; Standardization; Statistical Models; Subgroup; Testing; Time; Translating; Validation; acronyms; data acquisition; data management; design; flexibility; innovation; member; metabolomics; model development; mouse model; neuroimaging; neuropathology; next generation; novel; pre-clinical; predictive modeling; risk variant; statistics; transcriptome sequencing; web site

SUMMARY The primary objectives of the Bioinformatics and Data Management Core (BDMC) are to: (1) develop and implement standardized data acquisition and data analysis protocols across the NexGen AD Models Center (NG-ADMC), (2) acquire and integrate external data to inform AD animal models generated by the NG-ADMC, and (3) create and maintain a publicly available database to house research data generated by the NG-ADMC. The BDMC is led by faculty members with a long-standing history of directing bioinformatics, statistics, and data cores, with particular expertise in AD and neurology. The BDMC will be instrumental in the development and application of statistical models and bioinformatics methodologies as applied to the analysis of animal model discoveries. These analyses will be used for validation purposes as well as in the refinement of existing models. Specifically, faculty within the BDMC will build flexible predictive models using multiple clinical data sources in order to identify and prioritize LOAD risk alleles, and to analyze gene expression profiles, proteomics, and metabolomics data for implementation in animal models. They will also develop and apply efficient sequential testing designs for testing identified mouse models, and use robust post-hoc subgroup identification methods for assessing potential subpopulation heterogeneity in the effect of globally non- efficacious candidate therapies. The end result will allow the BDMC to identify and validate translatable biomarkers that can ultimately assist in clinico-pathological staging of an AD-like disease progression in AD animal models.

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