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

描述(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次序列读数，并正迅速应用于各种类型的实验(例如，RNA-Seq、miRNA-Seq、ChIP-Seq、BS-Seq、CNV-Seq)，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。虽然测序被认为克服了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和新的统计和计算挑战。迫切需要统计和计算工具，以解决该领域领导人所说的最大问题：数据分析和数据整合。我们建议为高通量测序(HTS)开发一套全面和协调的统计方法，直接解决表观基因组学中的许多重要数据分析问题。具体地说，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，用于分析单端和成对末端标签运行的芯片序列数据，特别是关注在全基因组核小体位置分析中的应用。2)制定分析BS-SEQ数据的统计方法，产生基本水平的DNA甲基化图谱。3)为数据集成开发新的统计工具和方法，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，以展示我们方法的相关性和实用性。我们计划使用刺激的STAT1和STAT3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、DNase I超敏反应和基因表达。 公共卫生相关性：我们建议为高通量测序(HTS)开发一套全面和协调的统计方法，直接解决表观基因组学中的许多重要数据分析问题。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，以更好地了解调控细胞行为的机制。我们建议的大部分内容不仅涉及开发新的统计和计算方法，而且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的应用确保或良好开发的方法将在分子生物学中产生广泛的影响，特别是在转录调节、染色质动力学、发育和癌症方面。

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.