GeroInformatics Core

分析大型数据集的重要性（例如，RNAseq、ChIP-seq、基因组研究等） 越来越多地用于全球分析分子随年龄或衰老的变化 干预措施。然而，获得必要的生物信息学和统计专门知识， 分析和解释这些数据对许多研究者来说是一个限制，尤其是初级研究者。 investigators.此外，在大数据时代，需要专注于以下方面的发展： 生物信息学方法将帮助研究人员回答与衰老有关的问题。的目标 老年信息学核心是协助俄克拉荷马州和全国各地的调查人员 专门为研究衰老的研究人员设计的生物信息学和统计分析 问题.核心将使用已建立的生物信息学数据方法来实现这一目标 分析，实验设计和分析的统计支持，以及使用新的和 GeroInformatics Core开发的数据分析创新方法，特别是 用来分析老化的数据库GeroInformatics核心包括建立 生物信息学方法开发、统计学和衰老分子生物学方面的专家。 除了既定的数据分析方法外， 生物信息学方法从不同角度询问大规模数据，例如 相关实体的基于文献的网络（例如，基因、疾病、代谢物、概念）， 从公共数据源编译的转录相关性， 对照和注释基因组区域以搜索实验共性。 由于人们对使用单细胞RNA测序来研究 老化对来自细胞培养物或组织的单个细胞的影响， 批判性地分析单细胞转录组数据，这是GeroInformatics Core的一项新功能， 本申请是开发迭代分析单细胞转录组数据的方法， 使这些新的分析能为老年人社区所用。

The importance of analyzing large data sets (e.g., RNAseq, ChIP-seq, genomic studies, etc) are increasingly used for a global analysis of molecular changes with age or with aging interventions. However, access to the bioinformatics and statistical expertise necessary to analyze and interpret these data can be rate-limiting for many investigators, especially junior investigators. Additionally, in the age of Big Data, there is a need to focus the development of bioinformatics methods that will help investigators answer aging-related questions. The goal of the GeroInformatics Core is to assist investigators in Oklahoma City and throughout the nation with bioinformatics and statistical analyses specifically designed for investigators studying aging questions. The Core will accomplish this goal using established bioinformatic methods for data analysis, statistical support for experimental design and analysis, as well as using novel and innovative approaches for data analysis developed by the GeroInformatics Core, specifically designed to analyze aging data bases. The GeroInformatics Core consists of established experts in bioinformatics methods development, statistics, and the molecular biology of aging. Besides established approaches for data analysis, the GeroInformatics Core has developed bioinformatics methods to interrogate large-scale data from different perspectives, such as literature-based networks of related entities (e.g., genes, diseases, metabolites, concepts), transcriptional correlations compiled from public data sources to compare new experiments against, and annotations of genomic regions to search for experimental commonalities. Because of the growing interest in the use of single-cell RNA-sequencing to study the effect of aging on individual cells from cell cultures or tissues and because of the lack of methods to critically analyze single-cell transcriptomic data, a new function of the GeroInformatics Core in this application is to develop methods to iteratively analyze single-cell transcriptomic data and make these novel analyses available to the aging community.