Allele-specific analysis of human epigenome, transcriptome and high-resolution chromatin organization

人类表观基因组、转录组和高分辨率染色质组织的等位基因特异性分析

基本信息

批准号：
10576452
负责人：
Jie Liu
金额：
$ 28万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
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：
2022-09-20 至 2024-05-19
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10576452
关键词：
3-Dimensional ATAC-seq Address Alleles Area Cells Chromatin Chromatin Loop Communities Computing Methodologies DNA Data Data Analyses Data Science Data Set Data Sources Funding Genes Genetic Genetic Transcription Genomic Segment Genotype-Tissue Expression Project Goals Human Human Genome Human Genome Project Inherited Internet Joints Knowledge Maps Modality Molecular Parents Population Regulatory Element Reproducibility Research Research Personnel Resolution Specificity Tissue-Specific Gene Expression Tissues United States National Institutes of Health Work cell type data resource data visualization deep learning epigenetic variation epigenome epigenomics expectation genetic analysis genetic variant genome sciences genomic data genomic locus human disease human tissue insight interest novel novel sequencing technology open data organizational structure transcriptome transcriptomics web portal web server

项目摘要

Abstract The human genome has two alleles at each genetic locus, with one allele inherited from each parent. Allele- specificity has been widely observed and investigated across human transcriptome, epigenome and 3D chromatin organization respectively, as evidenced by the data collected from the GTEx project, the ENCODE project, and the 4DN project. However, the allele-level interplay among transcriptome, epigenome and 3D chromatin organization has not been systematically explored. In the proposal, we aim to leverage shared donors between these consortia and systematically investigate these connections at allele-level in many human tissue types. With the single cell datasets available from these donors, we will narrow down from tissue level to cell type level, and further interrogate these allele-specific cross-modality connections. In essential, our analysis seamlessly integrates two NIH Common Fund datasets, namely 4DN and GTEx datasets, and ENCODE datasets. With this integrated data source, biomedical researchers can easily navigate, browse, compare and investigate the high quality, high resolution, and comprehensive datasets regarding chromatin organization, regulatory elements, epigenomic status and transcriptional activity with allele-specificity and cell-type-specificity. Not only would this accomplishment have an enormous positive impact on the utility and usage of the Common Fund datasets, it would also help to promote open science and reproducible research in the areas of computational genomics and data science.

抽象的人类基因组在每个遗传基因座都有两个等位基因，每个父母都遗传了一个等位基因。等位基因 - 在人类转录组，表观基因组和3D染色质中广泛观察并研究了特异性组织分别从GTEX项目，编码项目和 4DN项目。但是，转录组，表观基因组和3D染色质组织之间的等位基因级相互作用尚未系统地探索。在提案中，我们旨在利用这些共同的捐助者在许多人类组织类型中，在等位基因级别的联盟和系统研究这些连接。和这些供体可用的单细胞数据集，我们将从组织水平到细胞类型水平缩小范围，并进一步询问这些等位基因特异性的跨模式连接。至关重要的是，我们的分析无缝集成了两个NIH通用基金数据集，即4DN和GTEX数据集，并编码数据集。与此综合数据源，生物医学研究人员可以轻松浏览，浏览，比较和调查高质量，高分辨率和有关染色质组织，监管元素的全面数据集，具有等位基因特异性和细胞类型特异性的表观基因组状态和转录活性。这不仅会成就对共同基金数据集的效用和使用有巨大的积极影响，还将有助于在计算基因组学领域促进开放科学和可重复的研究数据科学。