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：
10610872
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/10610872
关键词：
3-Dimensional Address Bioconductor Biodiversity Biologic Characteristic Biological Cells Chromatin Clinical Communities Computer Analysis Computer software Data Data Analytics Data Set Dedications Development Dimensions Disease Environmental Risk Factor Gene Expression Regulation Genes Genetic Genome Genomic Segment Genomics Heterogeneity Hi-C Human Human Genome Individual Intervention Laboratory Organism 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 Predisposition Property Quantitative Trait Loci Regulator Genes Research Resolution Resources Risk Role Signal Transduction Source Statistical Methods 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 model organism multiple omics novel open source programs risk variant scale up simulation single cell sequencing trait transcriptomics translational genetics

3-Dimensional Address Bioconductor Biodiversity Biologic Characteristic Biological Cells Chromatin Clinical Communities Computer Analysis Computer software Data Data Analytics Data Set Dedications Development Dimensions Disease Environmental Risk Factor Gene Expression Regulation Genes Genetic Genome Genomic Segment Genomics Heterogeneity Hi-C Human Human Genome Individual Intervention Laboratory Organism 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 Predisposition Property Quantitative Trait Loci Regulator Genes Research Resolution Resources Risk Role Signal Transduction Source Statistical Methods 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 model organism multiple omics novel open source programs risk variant scale up simulation single cell sequencing trait transcriptomics translational genetics

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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.

项目摘要哺乳动物细胞的基因调节程序在很大程度上受远距离的影响染色质相互作用。我们建议开发可靠且可扩展的统计方法对于两个关键的基因组推断问题，在远距离染色质上取决于互动。首先，研究单细胞水平与3C-的远程相互作用的研究基于基于细胞类型特异性基因的方法是基础规定。 SCHI-C测量值未开发生物学多样性。但是，这些测量很容易出现极端的稀疏性，技术偏见和噪音。初始推理方法只是专注于schi-c数据的较低维表示，缺乏可扩展的框架，该框架可以利用数据降低数据阻碍了这些实验的关键推理任务。我们将解决这些关键通过为SCHI-C数据开发新颖的深层生成模型来构成缺点。通过缩小数据，这些方法将提高感兴趣的信号可以的功能被研究。其次，虽然测序和大规模可用性的进展表观基因组数据改善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的缺点可能是通过远程染色质相互作用影响GWAS的翻译发现临床干预措施。利用现有的大规模多样性研究杂种鼠标，我们将开发一个严格的框架，以整合多词的功能数据模态至微图模型有机体分子定量性状基因座和转移人类将非编码GWAS SNP与其效应子联系起来的结果，即敏感性，基因。大规模应用2型糖尿病（T2D）特征将提供候选T2D效应子基因及其调节基因座实验随访。这两个目标都将通过结合方法发展，理论分析，数据驱动模拟，计算分析和实验验证。该项目产生的统计资源将作为开源软件传播。成功完成项目将帮助确保从强大的SCHI-C实验获得最大信息和模型有机体多摩学数据。