Integrated Genome Discovery at Single Base Pair Resolution

单碱基对分辨率的综合基因组发现

• 批准号: 8402454
• 负责人: David K Gifford
• 金额: $ 48.38万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-17 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8402454
• 关键词: Active Learning; Architecture; Base Pairing; Binding; Biological Assay; Cell Line; Cell physiology; ChIP-seq; Chromatin; Computer Architectures; Computing Methodologies; DNA Sequence; Data; Deoxyribonucleases; Disease; EP300 gene; Elements; Enhancers; Gene Expression; Gene Expression Regulation; Genes; Genetic Polymorphism; Genome; Genome Mappings; Genomics; Goals; Health; Human; Human Genome; Individual; Language; Learning; Link; Maps; Mass Spectrum Analysis; Methods; Modeling; Output; Protein Binding; Reading; Regulatory Element; Reporter; Resolution; Software Tools; Space Models; Statistical Models; Structure; Techniques; Testing; Variant; Writing; base; computer studies; data format; design; follow-up; genome sequencing; human disease; improved; novel; research study

DESCRIPTION (provided by applicant): We propose to produce computationally predicted and experimentally improved single-base-pair resolution maps of genome regulatory elements and their higher-level architectures with ENCODE consortium data. To accomplish this goal, we will accomplish four Aims: Aim 1 will discover genome regulatory elements at single base pair resolution by simultaneously modeling ChIP-seq data, DNase-seq data, and genome sequence to discover where regulators bind to the genome along with explanatory DNA sequence motifs; Aim 2 will use integrative analysis to learn probabilistic models of enhancer grammars that include symbol spacing models; Aim 3 will develop active learning methods to precisely design synthetic enhancer sequences to construct Enhancer Grammar Activity Models (EGAMs) that explain the consequences of different forms of enhancer grammar on gene regulation, and will also learn regulatory factors that are associated with unlinked motifs; Aim 4 will discover regulatory networks that describe how chromatin and gene expression state is established based on regulator activity, and relate human disease associated genomic variation to potential disease mechanisms. The results of our Aims will be validated with both experimental and computational studies. PUBLIC HEALTH RELEVANCE: We will develop and use new methods to understand the language of the genome - the words and sentences of symbols that describe how cells function both in health and disease. Because the language is complicated, we will use new experimental methods to write and test thousands of genomic sentences for function in a dish. Our ultimate goal is to improve human health by understanding how disease related changes in our genome cause things to go wrong.

描述（由申请人提供）：我们建议生产基因组调节元素及其具有编码财团数据的高级体系结构的计算预测和实验改进的单基准分辨率图。为了实现这一目标，我们将实现四个目标：AIM 1通过同时建模芯片seq数据，DNase-seq数据和基因组序列来发现单个基对分辨率的基因组调节元素，以发现调节剂与解释性DNA序列基因组结合的位置； AIM 2将使用集成分析来学习增强器语法的概率模型，包括符号间距模型； AIM 3将开发积极的学习方法，以精确设计合成增强子序列，以构建增强子语法活性模型（EGAM），这些模型（EGAM）解释了不同形式的增强剂语法对基因调节的后果，并且还将学习与未连接基序相关的调节因素； AIM 4将发现调节网络，描述如何基于调节剂活性建立染色质和基因表达状态，并将相关的人类疾病的基因组变异与潜在疾病机制相关联。我们的目标结果将通过实验和计算研究验证。 公共卫生相关性：我们将开发和使用新方法来理解基因组的语言 - 描述细胞在健康和疾病中如何发挥作用的符号的单词和句子。由于该语言很复杂，我们将使用新的实验方法来编写和测试数千种基因组句子以在菜肴中的功能。我们的最终目标是通过了解基因组中疾病相关的变化如何导致问题出错来改善人类健康。