Transcriptome & Networks Analysis Core

转录组

基本信息

批准号：
10593070
负责人：
Jose Luis Gonzalez Hernandez
金额：
$ 38.92万
依托单位：
SOUTH DAKOTA STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-20 至 2027-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10593070
关键词：
Access to Information Acute-Phase Reaction Adoption Algorithms Antibodies Bar Codes Binding Biochemical Bioinformatics Biomedical Research Cell Death Cells Cilia Clustered Regularly Interspaced Short Palindromic Repeats Cryoultramicrotomy Data Data Analyses Data Set Experimental Designs Face Frequencies Funding Future Gene Expression Gene Expression Profile Genes Genetic Genetic Transcription Genomics Goals Individual Inflammation Inflammation Process Inflammatory Libraries Lymphangiogenesis Lymphatic Macrophage Measurement Mediating Meta-Analysis Methods Microscopy Molecular Pathway Analysis Pathway interactions Pattern Phenotype Preparation Printing Proteomics Public Domains Research Personnel Research Project Grants Resolution Series Site Small RNA Surveys TNF gene Techniques Technology Testing Time Tissue-Specific Gene Expression Tissues Translating Translational Research United States National Institutes of Health Validation Work bioinformatics resource bioinformatics tool cell type data integration deep sequencing differential expression experience experimental analysis gene function gene network gene regulatory network genome-wide genome-wide analysis innovation insight interactive tool interdisciplinary collaboration large datasets new technology next generation sequencing novel optical imaging programs response screening single-cell RNA sequencing skills tissue repair tool transcription factor transcriptome transcriptome sequencing transcriptomics whole genome wound

项目摘要

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.

项目摘要 – 核心 B：转录组和网络分析核心高通量组学技术的出现使得细胞转录的强大分析成为可能项目、基因功能关系和全基因组调查的细节前所未有。研究人员在实施这些新技术并有效转化这些大型成果时面临着许多障碍转录组和网络的目标是对数据集进行分子途径的机械理解。分析核心（TNAC）是为了促进组学技术人员和下游的有效利用该核心利用了 SDSU 主任 Gonzalez Hernandez 博士的专业知识。基因组测序设施，拥有遗传学和基因组学分析方面的专业知识，以及葛西金博士，具体来说，该核心将使该项目成为经验丰富的生物信息学家和生物医学研究人员。使用以下技术研究炎症：RNA测序（RNA-seq）、CRISPR全基因组 TNAC 有 3 个具体目标：1) 转录组学。通过常规 RNA-Seq 和深度测序分析 CRISPR 插入；2) 使用空间解析和单细胞 RNA-seq 技术以及相关的生物信息学工作流程；3) 数据使用和调整生物信息学工具和资源进行整合和广泛的途径分析，包括葛西金博士开发的那些构建炎症基础基因调控网络、整合蛋白质组学数据，并对三个项目生成的数据以及该核心的创新之处在于它将尖端实验技术与该核心将与四位项目负责人密切合作，体现最新的生物信息学工具和方法。跨学科合作，显着促进前沿技术的采用该地区的生物医学研究人员。