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

• 批准号: 10001077
• 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
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-22 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001077
• 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; Databases; 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; rapid growth; single cell analysis; single cell technology; single-cell RNA sequencing; supervised learning; tool; transcriptome; transcriptome sequencing; user-friendly

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.

项目概要 了解基因活动如何被控制对于阐明生物学的基本运行规则至关重要 和疾病的分子机制。单细胞基因组技术的最新创新开启了 分析单个细胞中各种功能基因组特征的大门。这些技术使科学家能够 系统地发现复杂组织和疾病样本中的未知细胞亚群，并允许它们 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 尽管进展有希望，但仍然存在许多挑战，必须克服这些挑战才能完全解码基因 单细胞分辨率的调控。特别是，当前的技术缺乏准确测量的能力 单个细胞中每个单独的顺式调节元件（CRE）的活性。他们也无法测量所有功能 同一细胞中的基因组数据类型。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非常重要。数据快速增长，缺乏数据计算工具 分析已成为单细胞基因组技术有效应用的限制因素。 该提案的目标是开发计算和统计方法以及软件工具 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 活性是一项挑战。调节组， 定义为基因组中所有顺式调控元件的活动，包含理解的关键信息 基因调控。在单细胞中绘制调节组的最先进技术产生的稀疏数据 无法准确衡量个别商业地产的活动。我们将开发一个新的计算框架，以允许 使用稀疏数据更准确地分析单个细胞中单个 CRE 的活动。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），这是应用最广泛的单细胞功能基因组技术， 预测细胞的调控格局。由于大多数 scRNA-seq 数据集没有附带的单细胞数据 对于其他组学数据类型，我们的方法也将显着扩展 scRNA 的实用性并增加其价值 序列实验。我们的目标 3 解决了集成不同数据类型生成的挑战。 来自不同细胞的单细胞基因组技术。我们将开发一种比对单细胞 RNA-seq 的方法 单细胞调节组数据生成转录组和调节组的集成图谱。 完成该提案后，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。通过寻址 单细胞基因组学的几个重大挑战，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 增进我们对基础生物学和人类疾病的理解。