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ArticleDate
2013-06-29Online Publication Date
2013-06-29Print Publication Date
2013-09Permanent link to this record
http://hdl.handle.net/10754/325454
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Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ?10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors' websites: e.g. http://www.cs.toronto.edu/?wkc/kmerHMM. 2013 The Author(s).Citation
Wong K-C, Chan T-M, Peng C, Li Y, Zhang Z (2013) DNA motif elucidation using belief propagation. Nucleic Acids Research 41: e153-e153. doi:10.1093/nar/gkt574.Publisher
Oxford University Press (OUP)Journal
Nucleic Acids ResearchPubMed ID
23814189PubMed Central ID
PMC3763557ae974a485f413a2113503eed53cd6c53
10.1093/nar/gkt574
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