Transcriptome & Networks Analysis Core

项目摘要-核心B：传输和网络分析核心 高通量组学技术的出现使得对细胞转录水平的分析成为可能， 程序，基因功能关系和全基因组调查在前所未有的细节。个人 研究人员在实施这些新技术和有效地将这些大的 将数据集转化为对分子途径的机械理解。转录组和网络的目标 分析核心（TNAC）是为了促进组学技术人员和下游的有效利用， 生物信息学分析。该核心利用了SDSU主任Gonzalez埃尔南德斯博士的专业知识 基因组学测序设施，具有遗传学和基因组学分析的专业知识，和博士Xijin Ge， 经验丰富的生物信息学家和生物医学研究人员。具体而言，这一核心将使项目能够 使用以下技术来研究炎症：RNA测序（RNA-seq），CRISPR全基因组 筛选、空间分辨RNA-seq和蛋白质组学。TNAC有3个具体目标：1）转录组学 通过常规RNA-Seq和深度测序进行分析以分析CRISPR插入; 空间分辨和单细胞RNA-seq技术和相关的生物信息学工作流程;以及3）数据 利用和调整生物信息学工具和资源，包括 由Xijin Ge博士开发的构建炎症基础基因调控网络的方法， 蛋白质组学数据，并对这三个项目产生的数据进行荟萃分析， 公共领域。这一核心是创新的，因为它汇集了尖端的实验技术， 最新的生物信息学工具和方法。与四个项目负责人密切合作，该核心将 跨学科合作，并大大促进采用尖端技术之间 该地区的生物医学研究人员。

Project Summary – Core B: TRANSCRIPTOME AND NETWORK ANALYSIS CORE The advent of high-throughput omics technologies enables powerful analyses of cellular transcriptional programs, gene-function relationships and genome-wide surveys at unprecedented detail. Individual investigators face many hurdles in implementing these new technologies and effectively translating these large datasets into mechanistic understanding of molecular pathways. The goal of the Transcriptome and Network Analysis Core (TNAC) is to facilitate the effective use of omics technologists and the downstream bioinformatics analyses. This core leverages the expertise of Dr. Gonzalez Hernandez, Director of the SDSU Genomics Sequencing Facility, with expertise in genetics and genomics analysis, and Dr. Xijin Ge, experienced bioinformatician and biomedical researcher. Specifically, this core will enable the project leads to use the following technology to study inflammation: RNA sequencing (RNA-seq), CRISPR whole genome screening, spatially resolved RNA-seq, and proteomics. The TNAC has 3 specific aims: 1) Transcriptomic analyses by conventional RNA-Seq and deep sequencing for analysis of CRISPR insertions; 2) The use of spatially resolved and single cell RNA-seq technology and associated bioinformatics workflows; and 3) Data integration and extensive pathway analysis using and adapting bioinformatics tools and resources, including those developed by Dr. Xijin Ge, to construct gene regulatory networks underlying inflammation, integrate proteomics data, and conduct meta-analysis of data generated by the three projects alongside data in the public domain. This core is innovative in that it brings together cutting-edge experimental technology with the latest bioinformatics tools and approaches. Working closely with the four project leads, this core will exemplify interdisciplinary collaboration, and significantly promote the adoption of cutting-edge technologies among biomedical researchers in the region.