权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders

探索表观遗传异质性并解释精神疾病非编码变异的大数据方法

基本信息

批准号：
10640918
负责人：
JING ZHANG
金额：
$ 11.3万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-17 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10640918
关键词：
Acceleration Affect Autopsy Award Big Data Binding Biochemistry Biological Assay Biological Process Biophysics Brain ChIP-seq Chromatin Communities Computer software Computing Methodologies Data Data Scientist Databases Dependence Development Dimensions Disease Distal Elements Enhancers Entropy Epigenetic Process Event Gene Expression Regulation Genes Genetic Genetic Risk Genetic Transcription Genetic Variation Genome Genomics Heritability Heterogeneity Human Incidence Individual Knowledge Learning Link Machine Learning Maps Mental disorders Mentorship Methods Modeling Molecular Nucleic Acid Regulatory Sequences Patients Pattern Phenotype Population Prefrontal Cortex Proteins Psychiatric Diagnosis Regulatory Element Reporter Reporting Research Research Personnel Resolution Resources Risk Factors Sampling Scanning Scoring Method Shapes Signal Transduction Technology Training Training Programs Transcriptional Regulation Universities Untranslated RNA Variant Work career development cell type deep learning disorder risk epigenome experience experimental study functional genomics gene regulatory network genetic variant genome-wide genomic data genomic locus genomic profiles insight machine learning method multimodality multiple omics neurogenetics neuropsychiatric disorder new therapeutic target novel novel sequencing technology open source programs promoter recruit research and development risk variant therapeutic target transcription factor transcriptomics web services

项目摘要

PROJECT ABSTRACT The incidence of diagnosed psychiatric disorders has been increasing for decades, leaving millions of afflicted individuals. Despite the high heritability, their underlying molecular mechanisms remain elusive. Most risk loci are located in noncoding genomic elements without direct effects on protein products. Comprehensive functional annotation and variant impact quantification are essential to provide new molecular insights and discover therapeutic targets. Recent advances in novel sequencing technologies and community efforts to share genomic data provide unprecedented opportunities to understand how genetic variants contribute to psychiatric diseases. This application describes the development of integrative strategies and machine learning methods to combine novel assays (such as STARR-seq) with population-scale genomic profiles to elucidate the genetic regulatory grammar in the human prefrontal cortex (PFC) and to prioritize genetic variants in psychiatric disorders. Specifically, we will (1) dissect the cis- regulatory landscape of the PFC using population-scale epigenetics data, (2) construct multi- model gene regulatory networks by linking distal cis-regulatory elements to genes using chromatin co-variability analyses, (3) integrate genetic, epigenetic, and transcriptional data to identify key transcription factors and variants that contribute to psychiatric disorders. Distinct from existing efforts focusing on one genome, this proposed work presents a truly novel big-data approach for both modeling gene regulation and investigating disease-risk factors by incorporating heterogeneous multi-omics profiles from hundreds of individuals. The resultant comprehensive list of cis-regulatory elements will expand the number of known functional regions in the human brain by at least an order. We will release our methods and resources in the form of web services, distributed open-source software, and annotation databases, which will also benefit other investigators exploring the genetic underpinnings of neuropsychiatric disorders. In addition to its scientific content, this application proposes a comprehensive training program for preparing an independent investigator in computational genomics and neurogenetics. This training will take place at Yale University (in the Dept. of Molecular Biophysics and Biochemistry) under the mentorship of Prof. Mark Gerstein (functional genomics), Prof. Nenad Sestan (neurogenetics), and Prof. Hongyu Zhao (statistical genetics and machine learning). A committee of experienced psychiatric disease experts and data scientists will also provide advice on both scientific research and career development.

项目摘要几十年来，诊断出的精神疾病的发病率一直在增加，留下了数百万受折磨的人尽管遗传率很高，但其潜在的分子机制仍然难以捉摸。大多数风险基因座位于非编码基因组元件中，直接影响蛋白质产品。全面的功能注释和变体影响定量分析对于提供新的分子见解和发现治疗靶点至关重要。新测序技术的最新进展和社区努力分享基因组数据提供了前所未有的机会，以了解遗传变异如何贡献精神疾病。本申请描述了综合战略的发展，机器学习方法将新的测定（如STARR-seq）与群体规模结合起来，联合收割机基因组图谱，以阐明人类前额叶皮层（PFC）的遗传调控语法并优先考虑精神疾病的遗传变异。具体来说，我们将（1）解剖顺- 使用群体规模表观遗传学数据的PFC的监管景观，（2）构建多个通过使用染色质将远端顺式调控元件连接到基因来模拟基因调控网络协变分析，（3）整合遗传、表观遗传和转录数据，以确定关键的转录因子和变异导致精神疾病。区别于现有的专注于一个基因组的努力，这项拟议的工作提出了一个真正新颖的大数据方法，通过整合基因调控模型和研究疾病风险因素，来自数百个个体的异质多组学图谱。由此产生的全面顺式调节元件的列表将扩大人类中已知功能区域的数量。大脑至少有一个命令。我们将以Web服务的形式发布我们的方法和资源，分布式开源软件和注释数据库，这也将有利于其他研究人员正在探索神经精神疾病的遗传基础。除了其科学的内容，这一申请提出了一个全面的培训，程序准备在计算基因组学和神经遗传学的独立调查员。该培训将在耶鲁大学（系内）进行。分子生物物理学和 Mark Gerstein教授（功能基因组学）、Nenad教授 Sestan（神经遗传学）和Hongyu Zhao教授（统计遗传学和机器学习）。一经验丰富的精神疾病专家和数据科学家委员会也将提供建议科学研究和职业发展。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

One-Shot Learning With Attention-Guided Segmentation in Cryo-Electron Tomography.

DOI：
10.3389/fmolb.2020.613347
发表时间：
2020
期刊：
Frontiers in molecular biosciences
影响因子：
5
作者：
Zhou B;Yu H;Zeng X;Yang X;Zhang J;Xu M
通讯作者：
Xu M

STARRPeaker: uniform processing and accurate identification of STARR-seq active regions.

DOI：
10.1186/s13059-020-02194-x
发表时间：
2020-12-08
期刊：
Genome biology
影响因子：
12.3
作者：
Lee D;Shi M;Moran J;Wall M;Zhang J;Liu J;Fitzgerald D;Kyono Y;Ma L;White KP;Gerstein M
通讯作者：
Gerstein M

CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation.