title: On the detection and refinement of transcription factor binding sites using ChIP-Seq data.
abstract: Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic 'greedy' search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.
authors.affiliation: Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109, USA.
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.forename: Jindan
authors.initials: J
authors.lastname: Yu
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
authors.forename: Zhaohui S
authors.initials: ZS
authors.lastname: Qin
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D001499:Bayes Theorem; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology; D003198:Computer Simulation; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000737:chemistry / Q000378:metabolism*
chemical_list: D014157:Transcription Factors