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Computational tools for regulome mapping using single-cell genomic data

使用单细胞基因组数据进行调节组图谱的计算工具

基本信息

批准号：
10443743
负责人：
Hongkai Ji
金额：
$ 40.94万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-22 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10443743
关键词：
Address Atlases Behavior Biological Biology Biomedical Research Brain Cells Cellular Assay Chromatin Complex Computer Analysis Computing Methodologies Data Data Analyses Data Set Development Disease Emerging Technologies Foundations Gene Expression Regulation Genes Genome Genomics Human Immune system Individual Knowledge Malignant Neoplasms Maps Measures Methods Modality Molecular Multiomic Data Noise Organ Phase Population Regulator Genes Regulatory Element Research Personnel Resolution Sampling Scientist Software Tools Statistical Methods Stem Cell Development System Technology Therapeutic Tissues Training Transposase base computer framework computerized tools epigenome experimental study functional genomics genomic data histone modification human disease innovation multiple data types multiple omics novel strategies open source predictive modeling programs public database rapid growth single cell analysis single cell technology single-cell RNA sequencing supervised learning tool transcriptome transcriptome sequencing user-friendly

项目摘要

Project Summary Understanding how genes' activities are controlled is crucial for elucidating the basic operating rules of biology and molecular mechanisms of diseases. Recent innovations in single-cell genomic technologies have opened the door to analyzing a variety of functional genomic features in individual cells. These technologies enable scientists to systematically discover unknown cell subpopulations in complex tissue and disease samples, and allow them to reconstruct a sample's gene regulatory landscape at an unprecedented cellular resolution. Despite these promising developments, many challenges still exist and must be overcome before one can fully decode gene regulation at the single-cell resolution. In particular, current technologies lack the ability to accurately measure the activity of each individual cis-regulatory element (CRE) in a single cell. They also cannot measure all functional genomic data types in the same cell. Moreover, the prevalent technical biases and noises in single-cell genomic data make computational analysis non-trivial. With rapid growth of data, lack of computational tools for data analysis has become a rate-limiting factor for effective applications of single-cell genomic technologies. The objective of this proposal is to develop computational and statistical methods and software tools for mapping and analyzing gene regulatory landscape using single-cell genomic data. Our Aim 1 addresses the challenge of accurately measuring CRE activities in single cells using single-cell regulome data. Regulome, deﬁned as the activities of all cis-regulatory elements in a genome, contains crucial information for understanding gene regulation. The state-of-the-art technologies for mapping regulome in a single cell produce sparse data that cannot accurately measure activities of individual CREs. We will develop a new computational framework to allow more accurate analysis of individual CREs' activities in single cells using sparse data. Our Aim 2 addresses the challenge of collecting multiple functional genomic data types in the same cell. We will develop a method that uses single-cell RNA sequencing (scRNA-seq), the most widely used single-cell functional genomic technology, to predict cells' regulatory landscape. Since most scRNA-seq datasets do not have accompanying single-cell data for other -omics data types, our method will also signiﬁcantly expand the utility and increase the value of scRNA- seq experiments. Our Aim 3 addresses the challenge of integrating different data types generated by different single-cell genomic technologies from different cells. We will develop a method to align single-cell RNA-seq and single-cell regulome data to generate an integrated map of transcriptome and regulome. Upon completion of this proposal, we will deliver our methods through open-source software tools. These tools will be widely useful for analyzing and integrating single-cell regulome and transcriptome data. By addressing several major challenges in single-cell genomics, our new methods and tools will help unleash the full potential of single-cell genomic technologies for studying gene regulation. As such, they can have a major impact on advancing our understanding of both basic biology and human diseases.

项目摘要了解基因的活动是如何被控制的对于阐明生物学的基本运作规则至关重要和疾病的分子机制。单细胞基因组技术的最新创新开启了这是分析单个细胞中各种功能基因组特征的大门。这些技术使科学家系统地发现复杂组织和疾病样本中的未知细胞亚群，以前所未有的细胞分辨率重建样本的基因调控景观。尽管有这些尽管基因组研究取得了令人鼓舞的进展，但仍存在许多挑战，在完全解码基因组之前必须克服这些挑战。在单细胞分辨率下进行调节。特别地，当前的技术缺乏准确地测量所需能量的能力。在单个细胞中每个单独的顺式调节元件（CRE）的活性。他们也不能测量所有功能基因组数据类型在同一个细胞中。此外，单细胞基因组中普遍存在的技术偏差和噪音数据使计算分析变得重要。随着数据的快速增长，缺乏数据计算工具分析已经成为单细胞基因组技术有效应用的限速因素。本提案的目的是开发计算和统计方法及软件工具，使用单细胞基因组数据绘制和分析基因调控景观。我们的目标1解决了使用单细胞调节组数据准确测量单细胞中的CRE活性的挑战。Regulome，被定义为基因组中所有顺式调节元件的活动，包含理解基因调控用于在单个细胞中绘制调节组的最新技术产生稀疏数据，无法准确测量单个克雷斯的活动。我们将开发一个新的计算框架，使用稀疏数据更准确地分析单个细胞中的单个克雷斯活性。我们的目标2解决了在同一细胞中收集多种功能基因组数据类型的挑战。我们将开发一种方法，使用单细胞RNA测序（scRNA-seq），这是最广泛使用的单细胞功能基因组技术，来预测细胞的监管格局。由于大多数scRNA-seq数据集没有伴随的单细胞数据，对于其他组学数据类型，我们的方法也将显著扩展scRNA的实用性并增加其价值。 seq实验。我们的目标3解决了集成不同数据类型的挑战，不同细胞的单细胞基因组技术。我们将开发一种方法来比对单细胞RNA-seq，单细胞调节组数据以生成转录组和调节组的整合图谱。在完成这项提案后，我们将通过开源软件工具提供我们的方法。这些工具将广泛用于分析和整合单细胞调节组和转录组数据。通过解决单细胞基因组学的几个主要挑战，我们的新方法和工具将有助于释放全部潜力研究基因调控的单细胞基因组技术。因此，它们可以对以下方面产生重大影响：推进我们对基础生物学和人类疾病的理解。