High dimensional statistical data modeling and integration for studying regulatory variation

用于研究监管变化的高维统计数据建模和集成

基本信息

批准号：
10413927
负责人：
Sunduz Keles
金额：
$ 37.88万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-04-26 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10413927
关键词：
3-Dimensional Address Animal Model Bioconductor Biodiversity Biologic Characteristic Biological Cells Chromatin Clinical Communities Computer Analysis Computer software Data Data Analytics Data Set Development Dimensions Disease Ensure Environmental Risk Factor Gene Expression Regulation Genes Genetic Translation Genome Genomic Segment Genomics Heterogeneity Hi-C Human Human Genome Individual Intervention Juice Lead Link Mammalian Cell Maps Measurement Mediating Methodology Methods Modality Modeling Molecular Molecular Conformation Multiomic Data Mus Nature Noise Non-Insulin-Dependent Diabetes Mellitus Nucleotides Phenotype Property Quantitative Trait Loci Regulator Genes Research Resolution Resources Risk Role Signal Transduction Source Statistical Methods Susceptibility Gene Technology Training Translations Untranslated RNA Validation Variant autoencoder cell type chromosome conformation capture data integration data modeling denoising epigenome epigenomics experimental study flexibility follow-up genome wide association study high dimensionality high throughput technology improved innovation interest multiple omics novel open source programs risk variant scale up simulation single cell sequencing trait transcriptomics

项目摘要

Project Summary Gene regulatory programs of mammalian cells are largely influenced by long-range chromatin interactions. We propose to develop robust and scalable statistical methods for two critical genomic inference problems hinging upon long-range chromatin interactions. First, the study of long-range interactions at the single cell-level with 3C- based method scHi-C is fundamental to fully understanding cell type-specific gene regulation. scHi-C measurements harbor unexplored biological diversity. However, these measurements are prone to extreme sparsity, technological bias, and noise. While initial inference methods simply focused on lower dimensional representations of scHi-C data, lack of a scalable framework that can exploit nonlinearities in de-noising of the data impedes key inference tasks from these experiments. We will address these critical shortcomings by developing a novel deep generative model for scHi-C data. By de- noising the data, these methods will improve the power with which signals of interest can be studied. Second, while advances in sequencing and large-scale availability of epigenome data improved the power and interpretation of genome-wide association studies (GWAS), shortcomings in identifying which genes noncoding SNPs might be impacting through long-range chromatin interactions hinder the translation of GWAS findings into clinical interventions. Leveraging existing large-scale studies of diversity outbred mice, we will develop a rigorous framework that integrates multi-omics functional data modalities to fine-map model organism molecular quantitative trait loci and transfer the results to humans for linking noncoding GWAS SNPs to their effector, i.e., susceptibility, genes. Large-scale application with type 2 diabetes (T2D) traits will deliver candidate T2D effector genes and their regulatory loci that are amenable for experimental follow-up. Both aims will be accomplished through a combination of methodological development, theoretical analysis, data-driven simulation, computational analysis, and experimental validation. Statistical resources generated from this project will be disseminated as open-source software. Successful completion of the project will help to ensure that maximal information is obtained from powerful scHi-C experiments and model organism multi-omics data.

项目概要哺乳动物细胞的基因调控程序很大程度上受长程影响染色质相互作用。我们建议开发稳健且可扩展的统计方法解决与长程染色质相关的两个关键基因组推断问题互动。首先，研究单细胞水平与 3C- 的长程相互作用基于 scHi-C 的方法是充分理解细胞类型特异性基因的基础规定。 scHi-C 测量蕴藏着未经探索的生物多样性。然而，这些测量结果很容易出现极度稀疏、技术偏差和噪声的情况。虽然最初推理方法仅关注 scHi-C 数据的低维表示，缺乏可利用非线性进行数据去噪的可扩展框架阻碍了这些实验的关键推理任务。我们将解决这些关键问题通过为 scHi-C 数据开发一种新颖的深度生成模型来弥补缺点。通过德- 对数据进行噪声处理，这些方法将提高感兴趣信号的能力被研究。其次，虽然测序和大规模可用性的进步表观基因组数据提高了全基因组关联的能力和解释研究（GWAS），识别哪些基因非编码 SNP 可能存在的缺陷通过长程染色质相互作用的影响阻碍了 GWAS 的翻译研究结果转化为临床干预措施。利用现有的大规模多样性研究近交系小鼠，我们将开发一个严格的框架，整合多组学功能精细绘制模型生物分子数量性状位点和转移的数据模式将非编码 GWAS SNP 与其效应子连接起来的结果，即易感性、基因。 2 型糖尿病 (T2D) 特征的大规模应用将带来候选 T2D 效应基因及其调控位点实验后续。这两个目标将通过以下措施的结合来实现：方法发展、理论分析、数据驱动模拟、计算分析和实验验证。本项目产生的统计资源将作为开源软件传播。该项目的顺利完成将有助于确保从强大的 scHi-C 实验中获得最大信息和模型生物多组学数据。