Integrated Genome Discovery at Single Base Pair Resolution

描述（由申请人提供）：我们建议使用ENCODE联盟数据产生基因组调控元件的计算预测和实验改进的单碱基对分辨率图及其更高级别的架构。为了实现这一目标，我们将实现四个目标：目标1将通过同时建模ChIP-seq数据、DNase-seq数据和基因组序列来发现基因组调控元件，从而以单碱基对分辨率发现调控元件与基因组沿着解释性DNA序列基序结合的位置;目标2将使用综合分析来学习增强子语法的概率模型，包括符号间距模型;目标3：开发主动学习方法，精确设计合成增强子序列，构建增强子语法活性模型（EGAM），解释不同形式的增强子语法对基因调控的影响，并学习与未连接基序相关的调控因子;目的4将发现调控网络，描述如何染色质和基因表达状态的基础上建立调节活性，并与人类疾病相关的基因组变异的潜在疾病机制。我们的目标的结果将与实验和计算研究进行验证。

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.