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.

项目摘要 了解基因的活性如何控制对于阐明生物学的基本运营规则至关重要 和疾病的分子机制。单细胞基因组技术的最新创新已经打开了 分析单个细胞中各种功能基因组特征的门。这些技术使科学家 系统地发现复杂组织和疾病样品中未知的细胞亚群，并允许它们 在前所未有的细胞分辨率下重建样品的基因调节景观。尽管如此 有希望的发展，许多挑战仍然存在，必须克服，然后才能完全解码基因 单细胞分辨率下的调节。特别是，当前技术缺乏准确测量的能力 单个单个单独的单个单独的单个单元中的活性。他们也无法衡量所有功能 同一细胞中的基因组数据类型。此外，单细胞基因组中普遍的技术偏见和噪声 数据使计算分析非平凡。随着数据的快速增长，缺乏数据计算工具 分析已成为有效应用单细胞基因组技术的限制因素。 该建议的目的是开发计算和统计方法和软件工具 使用单细胞基因组数据映射和分析基因调节局势。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。调节物， 定义为基因组中所有顺式调节元素的活动，包含关键信息以理解 基因调节。单个单元格中绘制调节组的最先进技术产生稀疏数据 无法准确测量单个CRE的活动。我们将开发一个新的计算框架，以允许 使用稀疏数据对单个单元格中单个CRE的活性进行更准确的分析。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），最广泛使用的单细胞功能基因组技术， 预测细胞的调节景观。由于大多数SCRNA-SEQ数据集都没有涉及单细胞数据 对于其他数据类型，我们的方法还将显着扩大效用并增加scrna-的价值 SEQ实验。我们的目标3解决了整合不同数据类型的挑战 来自不同细胞的单细胞基因组技术。我们将开发一种使单细胞RNA-seq和 单细胞调节数据以生成转录组和调节的集成图。 该提案完成后，我们将通过开源软件工具提供我们的方法。这些工具 将广泛用于分析和集成单细胞调节组和转录组数据。通过解决 除非具有全部潜力，否 用于研究基因调节的单细胞基因组技术。因此，他们可能对 促进我们对基本生物学和人类疾病的理解。