Bioinformatics and Data Management Core

生物信息学和数据管理核心

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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.

概括生物信息学和数据管理核心（BDMC）的主要目标是：（1）开发和在整个Nexgen AD模型中心实施标准化的数据采集和数据分析协议（NG-ADMC），（2）获取和整合外部数据，以告知NG-ADMC产生的AD动物模型，（3）创建并维护一个公开可用的数据库，以容纳由NG-ADMC生成的研究数据。 BDMC由具有指导生物信息学，统计和的长期历史的教职员工领导数据核心，在AD和神经病学方面具有特别的专业知识。 BDMC将有助于开发以及应用于动物分析的统计模型和生物信息学方法的应用模型发现。这些分析将用于验证目的以及现有的改进型号。具体而言，BDMC中的教师将使用多个临床数据构建灵活的预测模型来源以识别和优先考虑负载风险等位基因，并分析基因表达谱，蛋白质组学和代谢组学数据用于动物模型。他们还将开发和应用用于测试已识别的鼠标模型的有效顺序测试设计，并使用可靠的事后亚组评估潜在亚群异质性的鉴定方法有效的候选疗法。最终结果将允许BDMC识别和验证可翻译最终可以帮助AD中AD样疾病进展的临床病理分期的生物标志物动物模型。