Model-Based Methods for Analyzing ChIP Sequencing Data

描述（由申请人提供）：蛋白质-DNA相互作用构成了靶基因表达遗传调控的基本机制。由于难以在基因组规模上表征蛋白质结合的DNA，因此破译这种机制具有挑战性。最近出现的超高通量测序技术通过允许以快速且成本有效的方式对靶DNA进行定量测序分析而彻底改变了该领域。ChIP-Seq将染色质免疫沉淀（ChIP）与下一代测序结合起来，提供数百万个短读段序列，代表特定转录因子和其他染色质相关蛋白结合的DNA标签。ChIP-Seq数据的快速积累带来了令人生畏的分析挑战。在这里，我们提出了一个隐马尔可夫模型（HMM）为基础的算法来检测基因组区域，显着丰富的ChIP-Seq。我们的方法将解决测序偏倚和读段比对不确定性等复杂问题。我们还提出了一个多层次的HMM，它将允许集成来自ChIP-Seq和ChIP-chip的数据。接下来，我们将利用ChIP-Seq数据构建基于模型的从头基序发现策略。我们相信，通过ChIP-Seq识别的所有序列的有效挖掘使我们能够精确地表征蛋白质-DNA相互作用位点。我们的长期生物医学研究兴趣是前列腺癌。我们将应用ChIP-Seq和本项目开发的数据分析工具来研究前列腺癌转录（dys-）调控。我们相信，在一个连贯的概率框架下进行有效的数据整合，最终将深入了解前列腺癌进展中介导转录调控的机制。 公共卫生相关性：转录调控在癌症进展中起着重要作用。本文提出的统计和计算策略的发展将有助于我们深入了解前列腺癌进展中介导转录调控的机制。

DESCRIPTION (provided by applicant): Protein-DNA interaction constitutes a basic mechanism for genetic regulation of target gene expression. Deciphering this mechanism is challenging due to the difficulty in characterizing protein-bound DNA on a genomic scale. The recent arrival of ultra-high throughput sequencing technologies has revolutionized this field by allowing quantitative sequencing analysis of target DNAs in a rapid and cost-effective way. ChIP-Seq, which couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, provides millions of short-read sequences, representing tags of DNAs bound by specific transcription factors and other chromatin-associated proteins. The rapid accumulation of ChIP-Seq data has created a daunting analysis challenge. Here we propose a hidden Markov model (HMM)-based algorithm to detect genomic regions that are significantly enriched by ChIP-Seq. Our method will address complications such as sequencing bias and read alignment uncertainty. We also propose a multi-level hierarchical HMM that will allow integration of data from both ChIP-Seq and ChIP- chip. Next, we will build model-based de novo motif finding strategies that utilizing ChIP-Seq data. We believe efficient mining of all sequences identified by ChIP-Seq allows us to precisely characterize the protein-DNA interaction sites. Our long term biomedical research interest is in prostate cancer. We will apply ChIP-Seq and the data analysis tools developed in this project to investigate prostate cancer transcription (dys-) regulation. We believe effective data integration under a coherent probability framework will eventually lead to an in-depth understanding of mechanisms mediating transcription regulation in prostate cancer progression. PUBLIC HEALTH RELEVANCE: Transcription regulation plays an important role in cancer progression. The development of statistical and computational strategies proposed here will help us gain in-depth understanding of mechanisms mediating transcriptional regulation in prostate cancer progression.