GeroInformatics Core

分析大型数据集的重要性(例如，RNASEQ、CHIP-SEQ、基因组研究等) 越来越多地用于对分子随年龄或随年龄变化的全球分析 干预措施。然而，获得必要的生物信息学和统计专业知识 分析和解释这些数据可能会限制许多调查人员的速度，特别是初级调查人员 调查人员。此外，在大数据时代，需要将发展重点放在 生物信息学方法，将帮助研究人员回答与衰老有关的问题。的目标是 老年信息学的核心是协助俄克拉荷马城和全国各地的调查人员 生物信息学和统计分析专门为研究衰老的研究人员设计 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 为实验设计和分析提供分析、统计支持，以及使用新颖和 老年信息学核心开发的数据分析创新方法，具体地说 旨在分析老化数据库。老年信息学核心由已建立的 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。 除了已建立的数据分析方法外，老年信息学核心还开发了 从不同角度询问大规模数据的生物信息学方法，例如 以文献为基础的相关实体网络(如基因、疾病、代谢物、概念)， 从公共数据来源汇编的转录相关性以比较新的实验 并对基因组区域进行注释，以寻找实验上的共性。 由于人们对使用单细胞RNA测序来研究肿瘤的作用越来越感兴趣 细胞培养物或组织中单个细胞的老化，因为缺乏方法来 严格分析单细胞转录数据，这是老年信息学核心的一项新功能 这个应用是开发迭代分析单细胞转录数据的方法和 将这些新颖的分析提供给老龄化社区。

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.