High dimensional statistical data integration for studying regulatory variation

用于研究监管变化的高维统计数据集成

• 批准号: 9344668
• 负责人: Sunduz Keles
• 金额: $ 32.5万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-26 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9344668
• 关键词: Address; Binding; Binding Sites; Bioconductor; Biologic Characteristic; Cells; ChIP-seq; Chromatin; Collection; Communities; Computer Analysis; Computer software; DNA; DNA Methylation; DNA-Protein Interaction; Data; Data Set; Data Sources; Derivation procedure; Development; Diagnosis; Disease; Elements; Galaxy; Generations; Genetic; Genetic Transcription; Genome; Genomics; Genotype; Histones; Human; Individual; International; Investigation; Joints; Knowledge; Letters; Location; Maps; Messenger RNA; Methodology; Methods; Phenotype; Protein Isoforms; RNA; RNA analysis; RNA-Binding Proteins; RNA-Protein Interaction; Regulation; Repetitive Sequence; Research; Research Personnel; Resources; Sampling; Source; Statistical Data Interpretation; Statistical Methods; Statistical Models; Technology; Tissues; Training; Untranslated RNA; Validation; Variant; base; cell type; crosslinking and immunoprecipitation sequencing; data integration; epigenome; experience; experimental study; genetic variant; genome wide association study; genome-wide; genomic data; genomic profiles; high dimensionality; high throughput technology; histone modification; human disease; improved; innovation; next generation sequencing; novel; protein profiling; reference genome; simulation; tool; trait; transcription factor; whole genome

Project Summary Next generation sequencing (NGS) technologies revolutionized the fields of genetics and genomics by allowing rapid and inexpensive sequencing of billions of bases. Although basic analysis tools for each individual data type are abundant, statistical methods that can integrate different sources of data for addressing key, challenging questions are lacking. We propose to develop integrative methods for critical, widely used, applications urgently requiring reliable statistical integration tools. At the core of our methods is effective integration of multiple appropriate data types with novel statistical methods. First, although, to date, large numbers of protein-DNA interactions and histone modifications are mapped, systematic methods that allow users to query these data and generate testable hypotheses are lacking. Second, in parallel to generation of (epi)genomic profiles, genome-wide association studies (GWAS) have been successful at identifying disease and trait-associated genetic variants (GVs). However, our ability to identify causal variants and elucidate the mechanisms by which genotypes influence phenotypes is hampered by significant obstacles. Third, although the utility of reads that map to multiple locations on the reference genome (multi-reads) has been well established for some NGS applications such as RNA-seq and ChIP-seq, all the analysis methods for the emerging data type CLIP-seq that interrogates RNA binding proteins rely on using only reads that map uniquely to reference genome (uni-reads) leading to unreliable inference. We plan to address these critical challenges by developing (1) Fast and scalable integrative statistical methods for joint analysis of multiple ChIP-seq datasets to enable both individual data level inference and identification of joint effects; (2) A statistical analysis framework for integrating GWAS results with the increasing numbers of genome-wide maps of functional annotations; (3) An integrative multi-read mapping framework for studying RNA-protein interactions through CLIP-seq experiments. The projects will be accomplished through a combination of methodological development, simulation, computational analysis, and experimental validation. Methods will be developed and evaluated using datasets from the ENCODE and REMC as well as novel datasets from collaborators. Statistical resources generated from the project will be disseminated in publicly available software. Collectively, these aims will significantly improve the utility of genome-wide data types that are available to researchers.

项目摘要 下一代测序（NGS）技术彻底改变了遗传学和 通过允许数十亿个碱基的快速和廉价的测序通过基因组学。虽然 每种数据类型的基本分析工具都是丰富的统计方法 可以整合不同的数据来源以解决密钥，具有挑战性的问题是 缺乏。我们建议为关键，广泛使用的应用程序开发整合方法 紧急需要可靠的统计集成工具。我们方法的核心是 有效地集成了多种适当的数据类型与新型统计方法。 首先，虽然迄今为止，大量蛋白质-DNA相互作用和组蛋白 修改是映射的，系统的方法，允许用户查询这些数据和 缺乏生成可检验的假设。其次，与一代平行 （EPI）基因组谱，全基因组关联研究（GWAS）已成功 在鉴定疾病和性状相关的遗传变异（GVS）方面。但是，我们的能力 识别因果变异并阐明基因型影响的机制 表型受到重大障碍的阻碍。第三，尽管效用是 参考基因组（多阅读）上的多个位置的地图已经很好 为某些NGS应用（例如RNA-Seq和Chip-Seq）建立，所有分析 新兴数据类型夹式seq的方法，该夹子询问RNA结合蛋白 依靠仅使用读取唯一映射的读物来参考基因组（UNI-Reads），导致 不可靠的推理。我们计划通过快速开发（1）来应对这些关键挑战 以及可扩展的集成统计方法，用于联合分析多个CHIP-SEQ 数据集以实现单个数据水平推断和关节效应的识别； （2）将GWAS结果整合到增加的统计分析框架 功能注释的全基因组图数； （3）综合多阅读 通过夹式研究RNA - 蛋白质相互作用的映射框架 实验。这些项目将通过方法学的结合来完成 开发，仿真，计算分析和实验验证。方法 将使用来自Encode和REMC的数据集开发和评估 作为合作者的新颖数据集。项目产生的统计资源将 被公开软件传播。总的来说，这些目标将显着 改善研究人员可用的全基因组数据类型的效用。