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:
- 金额:$ 40.94万
- 依托单位:
- 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:
- 关键词:AddressAtlasesBehaviorBiologicalBiologyBiomedical ResearchBrainCellsCellular AssayChromatinComplexComputer AnalysisComputing MethodologiesDataData AnalysesData SetDevelopmentDiseaseEmerging TechnologiesFoundationsGene Expression RegulationGenesGenomeGenomicsHumanImmune systemIndividualKnowledgeMalignant NeoplasmsMapsMeasuresMethodsModalityMolecularMultiomic DataNoiseOrganPhasePopulationRegulator GenesRegulatory ElementResearch PersonnelResolutionSamplingScientistSoftware ToolsStatistical MethodsStem Cell DevelopmentSystemTechnologyTherapeuticTissuesTrainingTransposasebasecomputer frameworkcomputerized toolsepigenomeexperimental studyfunctional genomicsgenomic datahistone modificationhuman diseaseinnovationmultiple data typesmultiple omicsnovel strategiesopen sourcepredictive modelingprogramspublic databaserapid growthsingle cell analysissingle cell technologysingle-cell RNA sequencingsupervised learningtooltranscriptometranscriptome sequencinguser-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,包含了理解
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,
来预测细胞的调控格局。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。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.
EDClust:一种 EM-MM 混合方法,用于多受试者单细胞 RNA 测序中的细胞聚类。
- DOI:10.1093/bioinformatics/btac168
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Wei,Xin;Li,Ziyi;Ji,Hongkai;Wu,Hao
- 通讯作者:Wu,Hao
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{{ truncateString('Hongkai Ji', 18)}}的其他基金
Immune Development Across the Life Course: Integrating Exposures and Multi-Omics in the Boston Birth Cohort
整个生命过程中的免疫发展:在波士顿出生队列中整合暴露和多组学
- 批准号:
10418079 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Immune Development Across the Life Course: Integrating Exposures and Multi-Omics in the Boston Birth Cohort
整个生命过程中的免疫发展:在波士顿出生队列中整合暴露和多组学
- 批准号:
10704536 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
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 - 财政年份:2019
- 资助金额:
$ 40.94万 - 项目类别:
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 - 财政年份:2019
- 资助金额:
$ 40.94万 - 项目类别:
Computational Tools for Mining Large Amounts of ChIP and Gene Expression Data
用于挖掘大量 ChIP 和基因表达数据的计算工具
- 批准号:
8516554 - 财政年份:2012
- 资助金额:
$ 40.94万 - 项目类别:
Computational Tools for Mining Large Amounts of ChIP and Gene Expression Data
用于挖掘大量 ChIP 和基因表达数据的计算工具
- 批准号:
8372529 - 财政年份:2012
- 资助金额:
$ 40.94万 - 项目类别:
Statistical and Computational Tools for Next-generation ChIP-seq Applications
用于下一代 ChIP-seq 应用的统计和计算工具
- 批准号:
8342445 - 财政年份:2012
- 资助金额:
$ 40.94万 - 项目类别:
Statistical and Computational Tools for Next-generation ChIP-seq Applications
用于下一代 ChIP-seq 应用的统计和计算工具
- 批准号:
8666661 - 财政年份:2012
- 资助金额:
$ 40.94万 - 项目类别:
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