UCLA IDDRC: Functional Genomics and Genetics Core

加州大学洛杉矶分校 IDDRC：功能基因组学和遗传学核心

• 批准号: 10686881
• 负责人: Michael Gandal
• 金额: $ 10.5万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686881
• 关键词: ATAC-seq; Animal Model; Atlases; Automobile Driving; Basic Science; Bioinformatics; Biological Assay; Brain; Cell Nucleus; Cell model; Cells; Chromatin; Clinical; Collaborations; Computerized Medical Record; Data; Data Analyses; Data Set; Data Storage and Retrieval; Database Management Systems; Databases; Dedications; Development; Environment; Epigenetic Process; Equipment and supply inventories; Experimental Designs; Experimental Models; Explosion; Foundations; Funding; Gene Expression; Gene Expression Profiling; Genes; Genetic; Genetic study; Genome; Genomic approach; Genomics; Genotype; Goals; High Performance Computing; Human; Informatics; Infrastructure; Institution; Intellectual and Developmental Disabilities Research Centers; Investigation; Journals; Knowledge; Laboratories; Lead; Maps; Medical Genetics; Methods; Methylation; Modernization; Nature; Network-based; Neurons; Neurosciences; Online Systems; Paper; Pathway Analysis; Patients; Phenotype; Postdoctoral Fellow; Pregnancy; Privatization; Process; Protocols documentation; Publications; Publishing; Quantitative Trait Loci; RNA Splicing; Recording of previous events; Regulatory Element; Research; Research Design; Research Personnel; Resolution; Resource Informatics; Resources; Running; Science; Services; Solid; Students; Study models; Technology; Training; United States National Institutes of Health; Untranslated RNA; Update; Validation; Work; analysis pipeline; analytical method; cohort; computing resources; data access; data mining; data sharing; design; exome sequencing; experience; experimental analysis; experimental study; fetal; functional genomics; genetic approach; genome-wide; genomic data; innovation; multiple datasets; neurogenesis; neuroimaging; new technology; next generation sequencing; polygenic risk score; single cell analysis; single-cell RNA sequencing; student training; tool; transcriptome sequencing; translational genetics; translational study; web-based tool

CORE C: Abstract The Genetics, Genomics, and Informatics Core (GGIC) focuses on the application of genome- level analyses in neuroscientific investigation, both at the sequence (genetic), gene expression and epigenetic (genomics) levels. The explosion of next-generation sequencing (NGS)-based methods has made advanced computational expertise and infrastructure needed for all sequencing-based applications. The proposed Core aims at providing support for basic and advanced genetics and genomics experiments in both patient cohorts for translational studies and experimental models for basic research. Modern genetic and genomic approaches rely on sequencing technology and require substantial bioinformatics expertise and access to solid computational resources. This Core leverages a proven history of expertise, as well as support and collaboration with other investigators in the Gandal and Geschwind groups with regards to computational and informatics resources funded by NIH and private foundations. Based on this proven track record, the Core will provide IDDRC investigators with the necessary expertise and infrastructure to perform high-throughput, genome-wide genetic and genomic studies. State-of- the-art analytical methods will be used to analyze NGS, gene expression, chromatin accessibility (e.g., ATAC-seq), methylation, and single cell/nucleus scRNA-seq data, and the resulting datasets will be posted onto a database accessible to IDDRC investigators, facilitating data sharing and collaborative analyses. Over the past 15 years, UCLA computational biologists and statisticians have lead the field of integrative data analysis (Geschwind and Konopka, 2009) and network-based methods (Oldham et al., 2008; Parikshak et al., 2013; Zhang and Horvath, 2005). Further, over the past 5 years, IDDRC investigators have published pioneering work interrogating the functional genomic landscape of human brain development, including the first comprehensive atlas of single-cell gene expression in the mid-gestation human brain (Polioudakis et al., 2019), high-resolution mapping of non-coding regulatory elements driving human neurogenesis with ATAC-seq (de La Torre Ubieta et al, 2018), and large-scale expression and splicing quantitative trait loci (QTL) profiling in fetal brain (Walker et al., 2019). Expertise in the development and implementation of these methods (including single-cell analysis, network methods, and integrative approaches) will be made directly available to IDDRC investigators.

核心C：抽象 遗传学、基因组学和信息学核心(GGIC)专注于基因组的应用-- 神经科学研究中的水平分析，包括序列(遗传)、基因表达 和表观遗传学(基因组学)水平。基于下一代测序(NGS)的爆炸性增长 方法使所有人都需要先进的计算专业知识和基础设施 基于测序的应用程序。拟议的核心旨在为以下方面提供支持 在翻译研究和患者队列中进行先进的遗传学和基因组学实验 基础研究的实验模型。现代遗传学和基因组学方法依赖于 测序技术，并需要大量的生物信息学专业知识和获得固体 计算资源。此核心利用久经考验的专业知识和支持历史 以及与Gandal和Geschind小组的其他调查人员合作 由NIH和私人基金会资助的计算和信息学资源。在此基础上 经过验证的跟踪记录，核心将为IDDRC调查人员提供必要的专业知识和 基础设施，以执行高通量、全基因组的遗传和基因组研究。现状-- 将使用-ART分析方法来分析NGS、基因表达、染色质可及性 (例如，ATAC-seq)、甲基化和单细胞/核scRNA-seq数据，以及由此产生的 数据集将张贴到IDDRC调查人员可访问的数据库上，以方便数据 共享和协作分析。在过去15年里，加州大学洛杉矶分校的计算生物学家和 统计学家在综合数据分析领域处于领先地位(Geschind和Konopka，2009)和 基于网络的方法(Oldham等人，2008；Parikshak等人，2013；Zhang和Horvath，2005)。 此外，在过去的5年里，IDDRC的调查人员发表了开创性的工作 人脑发育的功能基因组图景，包括第一个全面的 妊娠中期人脑单细胞基因表达图谱(Polioudakis等人，2019年)， 驱动人类神经发生的非编码调控元件的高分辨率图谱 Atac-seq(De La Torre Ubieta et al，2018)，以及大规模表达和剪接定量 胎儿大脑的性状基因座(QTL)分析(Walker等人，2019年)。在开发和开发方面的专业知识 实施这些方法(包括单细胞分析、网络方法和综合方法 方法)将直接提供给国际发展研究中心的调查人员。