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

摘要 人类基因组在每个基因位点有两个等位基因，其中一个等位基因遗传自每个父母。等位基因- 特异性已经在人类转录组、表观基因组和3D染色质中被广泛观察和研究 从GTEx项目、ENCODE项目和 4DN项目。然而，转录组、表观基因组和3D染色质组织之间的等位基因水平的相互作用 还没有被系统地研究过。在该提案中，我们的目标是利用这些机构之间的共享捐助者， 联合体，并系统地研究这些连接在等位基因水平在许多人类组织类型。与 从这些供体获得的单细胞数据集，我们将从组织水平缩小到细胞类型水平， 并进一步询问这些等位基因特异性的跨模态连接。基本上，我们的分析 整合了两个NIH共同基金数据集，即4DN和GTEx数据集以及ENCODE数据集。与此 集成的数据源，生物医学研究人员可以轻松地导航，浏览，比较和调查高 关于染色质组织，调控元件， 表观基因组状态和具有等位基因特异性和细胞类型特异性的转录活性。这不仅会 这一成就对共同基金数据集的效用和使用产生了巨大的积极影响， 还将有助于促进计算基因组学领域的开放科学和可重复研究， 数据科学

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.