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

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
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Hongkai Ji其他文献

Hongkai Ji的其他文献

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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:
Big Data Methods for Decoding Gene Regulation
解码基因调控的大数据方法
  • 批准号:
    10171879
  • 财政年份:
    2018
  • 资助金额:
    $ 40.94万
  • 项目类别:
Big Data Methods for Decoding Gene Regulation
解码基因调控的大数据方法
  • 批准号:
    9762143
  • 财政年份:
    2018
  • 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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