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: 10205134
• 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/10205134
• 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 单细胞基因组技术的最新创新开辟了新的领域。 这些技术使科学家能够分析单个细胞中的各种功能基因组特征。 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 尽管如此，仍以前所未有的细胞分辨率重建样本的基因调控景观。 尽管发展前景光明，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 特别是，当前的技术缺乏精确测量单细胞分辨率的能力。 它们也无法测量单个细胞中每个单独顺式调节元件（CRE）的活性。 此外，单细胞基因组中普遍存在的技术偏差和噪音。 数据使得计算分析变得非常重要。随着数据的快速增长，数据计算工具的缺乏。 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 我们的目标 1 使用单细胞基因组数据绘制和分析基因调控景观。 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， 定义为基因组中所有顺式调控元件的活动，包含理解的重要信息 在单细胞中绘制调控组的最先进技术产生的稀疏数据 无法准确衡量单个 CRE 的活动。我们将开发一个新的计算框架来允许。 我们的目标 2 解决了使用稀疏数据对单个细胞中单个 CRE 的活动进行更准确的分析。 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， 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 我们的目标 3 解决了整合不同数据类型生成的挑战。 我们将开发一种比对单细胞 RNA-seq 和来自不同细胞的单细胞基因组技术。 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。 通过寻址，将广泛用于分析和整合单细胞调节组和转录组数据。 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 用于研究基因调控的单细胞基因组技术因此可以产生重大影响。 增进我们对基础生物学和人类疾病的理解。