High dimensional statistical data integration for studying regulatory variation

项目摘要 下一代测序（NGS）技术彻底改变了遗传学和基因组学领域。 基因组学，允许快速和廉价的测序数十亿个碱基。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源，以解决关键的、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整合工具。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地结合起来。 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， 缺乏可验证的假设。第二，与生成 （epi）基因组图谱，全基因组关联研究（GWAS）已经成功 在识别疾病和性状相关的遗传变异（GV）。然而，我们的能力， 确定因果变异，并阐明基因型影响的机制 表型受到重大障碍的阻碍。第三，尽管效用的含义是， 映射到参考基因组上的多个位置（多读段）已经很好地 建立了一些NGS应用程序，如RNA-seq和ChIP-seq， 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段（uni-reads）， 不可靠的推论我们计划通过开发（1）快速 用于多个ChIP-seq的联合分析的可扩展的综合统计方法 数据集，使个人数据水平的推断和联合效应的识别; (2)一个统计分析框架，用于将GWAS结果与日益增长的 （3）一个整合的多读段 通过CLIP-seq研究RNA-蛋白质相互作用的映射框架 实验这些项目将通过以下方法的结合来完成： 开发、模拟、计算分析和实验验证。方法 将使用ENCODE和REMC的数据集进行开发和评估 as novel新datasets数据集from collaborators合作者.该项目产生的统计资源将 以公开可用的软件传播。总的来说，这些目标将大大 提高研究人员可用的全基因组数据类型的实用性。

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.