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.
项目总结
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
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Hongkai Ji其他文献
Hongkai Ji的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ 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:
相似海外基金
BrainMaps - a unified web platform for novel model organism brain atlases
BrainMaps - 新型模型生物脑图谱的统一网络平台
- 批准号:
23KF0076 - 财政年份:2023
- 资助金额:
$ 40.94万 - 项目类别:
Grant-in-Aid for JSPS Fellows
Sexual dimorphic cell type and connectivity atlases of the aging and AD mouse brains
衰老和 AD 小鼠大脑的性二态性细胞类型和连接图谱
- 批准号:
10740308 - 财政年份:2023
- 资助金额:
$ 40.94万 - 项目类别:
Pre-cancer atlases of cutaneous and hematologic origin (PATCH Center)
皮肤和血液来源的癌前图谱(PATCH 中心)
- 批准号:
10818803 - 财政年份:2023
- 资助金额:
$ 40.94万 - 项目类别:
Multi-modal cell type atlases of somatosensory spinal cord neurons
体感脊髓神经元多模态细胞类型图谱
- 批准号:
10743857 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Ultra-high Resolution Structural Connectome Atlases of the Animal Brain and their Associated Toolbox
动物大脑的超高分辨率结构连接图谱及其相关工具箱
- 批准号:
10558629 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Multi-modal cell type atlases of somatosensory spinal cord neurons
体感脊髓神经元多模态细胞类型图谱
- 批准号:
10508739 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Atlases and statistical modeling of vascular networks from medical images
医学图像血管网络的图谱和统计建模
- 批准号:
RGPIN-2018-05283 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Discovery Grants Program - Individual
Modularly built, complete, coordinate- and template-free brain atlases
模块化构建、完整、无坐标和模板的大脑图谱
- 批准号:
10570256 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Ultra-high Resolution Structural Connectome Atlases of the Animal Brain and their Associated Toolbox
动物大脑的超高分辨率结构连接图谱及其相关工具箱
- 批准号:
10364874 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别:
Modularly built, complete, coordinate- and template-free brain atlases
模块化构建、完整、无坐标和模板的大脑图谱
- 批准号:
10467697 - 财政年份:2022
- 资助金额:
$ 40.94万 - 项目类别: