期刊
NUCLEIC ACIDS RESEARCH
卷 38, 期 7, 页码 2154-2167出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkp1180
关键词
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资金
- Burroughs Welcome Foundation
- Doris Duke Charitable Foundation
- Prostate Cancer Foundation
- American Cancer Society
- National Institute of Health [K99CA129565, U01CA111275, P30CA46592, P30CA69568, R01GM72007, R01HG005119]
- Department of Defense [PC080665, BCO75023, W81XWH-09-2-0014]
- Howard Hughes Medical Institute
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.
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