Model-Based Methods for Analyzing ChIP Sequencing Data

描述（由申请人提供）：蛋白质- dna相互作用是基因调控靶基因表达的基本机制。由于难以在基因组尺度上表征蛋白质结合DNA，因此破译这种机制具有挑战性。最近超高通量测序技术的出现彻底改变了这一领域，它允许以快速和经济有效的方式对目标dna进行定量测序分析。ChIP- seq将染色质免疫沉淀（ChIP）与下一代测序结合，提供了数百万个短读序列，代表了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。本文提出了一种基于隐马尔可夫模型（HMM）的算法来检测ChIP-Seq显著富集的基因组区域。我们的方法将解决诸如测序偏差和读取对齐不确定性等复杂问题。我们还提出了一种多级分层HMM，它将允许来自ChIP- seq和ChIP- ChIP的数据集成。接下来，我们将利用ChIP-Seq数据构建基于模型的从头基序查找策略。我们相信通过ChIP-Seq鉴定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有效的数据整合将最终导致对前列腺癌进展中介导转录调控机制的深入理解。

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.