权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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：
Hongkai Ji
金额：
$ 40.94万
依托单位：
JOHNS HOPKINS UNIVERSITY
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：
https://reporter.nih.gov/project-details/10443743
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 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 public database rapid growth single cell analysis single cell technology single-cell RNA sequencing supervised learning tool transcriptome transcriptome sequencing user-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