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.

项目概要 下一代测序 (NGS) 技术彻底改变了遗传学和 通过允许快速且廉价地对数十亿个碱基进行测序来实现基因组学。虽然 每种数据类型的基本分析工具都很丰富，统计方法 可以整合不同来源的数据来解决关键的、具有挑战性的问题 缺乏。我们建议为关键的、广泛使用的应用开发综合方法 迫切需要可靠的统计集成工具。我们方法的核心是 将多种适当的数据类型与新颖的统计方法有效整合。 首先，尽管迄今为止，大量的蛋白质-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并 缺乏生成可检验的假设。其次，与生成并行 （表观）基因组图谱、全基因组关联研究（GWAS）已取得成功 识别疾病和性状相关的遗传变异（GV）。然而，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，虽然读起来的效用 映射到参考基因组上的多个位置（多重读取）已经很好 为一些 NGS 应用（例如 RNA-seq 和 ChIP-seq）建立，所有分析 用于询问 RNA 结合蛋白的新兴数据类型 CLIP-seq 的方法 依赖于仅使用唯一映射到参考基因组的读取（uni-reads），从而导致 不可靠的推论。我们计划通过开发 (1) 快速解决这些关键挑战 和可扩展的综合统计方法，用于多个 ChIP-seq 的联合分析 数据集，以实现单独数据级别的推断和联合效应的识别； (2) 将 GWAS 结果与不断增长的数据相结合的统计分析框架 功能注释的全基因组图谱数量； (3) 综合多读 通过 CLIP-seq 研究 RNA-蛋白质相互作用的绘图框架 实验。这些项目将通过方法论的结合来完成 开发、模拟、计算分析和实验验证。方法 也将使用来自 ENCODE 和 REMC 的数据集进行开发和评估 作为合作者的新颖数据集。该项目产生的统计资源将 在公开可用的软件中传播。总的来说，这些目标将显着 提高研究人员可用的全基因组数据类型的实用性。