Methods for the analysis and integrations of next-generation sequencing with appl

描述（由申请人提供）：下一代测序技术能够在每次仪器运行期间产生数千万个序列读数，并迅速应用于各种类型的实验（例如RNA-Seq，miRNA-Seq，ChIP-Seq，BS-seq，CNV-Seq），以通过经济高效地生成全基因组数据集来解决生物医学问题。虽然测序已被推广为克服基于微阵列的研究的长期局限性，但其数据文件比微阵列大得多，其多样化的数据类型提出了类似的以及新的统计和计算挑战。迫切需要统计和计算工具来解决该领域领导人所说的最大问题：数据分析和数据集成。我们建议开发一套全面协调的高通量测序（HTS）统计方法，直接解决表观基因组学中许多重要的数据分析问题。具体来说，我们计划解决进行HTS实验的研究人员面临的以下计算和统计挑战：1）开发敏感的统计方法，用于分析单端和双端标签运行的ChIP-seq数据，特别是专注于核小体位置的全基因组分析应用。2)开发用于分析BS-seq数据的统计方法，产生基础水平的DNA甲基化图谱。3)开发新的统计工具和数据整合方法，以获得关于全球转录和调控的新的生物学见解。我们还计划将这些方法应用于各种高通量测序数据集，以证明我们的方法的相关性和实用性。我们计划使用刺激的STAT 1和STAT 3数据，以及来自ETS转录因子家族及其辅助因子的数据，我们已经通过合作收集了重要数据，包括转录因子，组蛋白标记，DNA酶I超敏反应和基因表达。 公共卫生相关性：我们建议开发一套全面协调的高通量测序（HTS）统计方法，直接解决表观基因组学中许多重要的数据分析问题。特别是，我们计划整合来自多个来源的数据，包括表达，转录因子结合，核小体定位，组蛋白标记和DNA甲基化，以更好地了解调节细胞行为的机制。我们的大部分提案不仅涉及新的统计和计算方法的开发，还涉及支持这些想法的软件工具的设计、实施和交付。下一代测序的许多有用应用确保了或开发良好的方法将在分子生物学中产生广泛的影响，特别是在转录调控、染色质动力学、发育和癌症中。

DESCRIPTION (provided by applicant): Next-generation sequencing technologies are capable of producing tens of millions of sequence reads during each instrument run, and are quickly being applied in diverse types of experiments (e.g. RNA-Seq, miRNA-Seq, ChIP-Seq, BS-seq, CNV-Seq) to address biomedical questions by cost-effectively generating genome-wide datasets. While sequencing has been promoted as overcoming longstanding limitations of microarray-based studies, its data files are much larger than for microarrays, and its diverse data types raise similar as well as novel statistical and computational challenges. There is a pressing need for statistical and computational tools to address what leaders in the field have stated are the largest problems: data analysis and data integration. We propose to develop a comprehensive and coordinated set of statistical methods for high throughput sequencing (HTS) that directly address many important data analysis problems in epigenomics. Specifically we plan to address the following computational and statistical challenges facing researchers conducting HTS experiments: 1) develop sensitive statistical methods for the analysis of ChIP-seq data both for single- and paired-end-tag runs, particularly the focusing on applications in genome-wide profiling of nucleosome positions. 2) develop statistical methods for the analysis of BS-seq data, producing base-level DNA methylation profiles. 3) develop new statistical tools and methods for data integration in order to gain new biological insights about global transcription and regulation. We also plan to apply these approaches to a variety of high throughput sequencing data sets to demonstrate the relevance and utility of our methods. We plan to work with stimulated STAT1 and STAT3 data, and data from the ETS transcription factor family and its cofactors, for which we have already gathered significant data through our collaborations, including transcription factors, histone marks, DNAse I hypersensitivity and gene expression. PUBLIC HEALTH RELEVANCE: We propose to develop a comprehensive and coordinated set of statistical methods for high throughput sequencing (HTS) that directly address many important data analysis problems in epigenomics. In particular, we plan to integrate data from multiple sources including expression, transcription factor binding, nucleosome positioning, histone marks and DNA methylation to better understand the mechanisms that regulate the behavior of a cell. Much of our proposal involves not just the development of new statistical and computational methods, but also the design, implementation and delivery of software tools that support these ideas. The many useful applications of next-generation sequencing with assure that or well- developed methods will have a broad impact in molecular biology, specifically in transcription regulation, chromatin dynamics, development, and cancer.