NDLI: DNA motif elucidation using belief propagation

Content Provider	PubMed Central
Author	Wong, Ka-chun Chan, Tak-ming Peng, Chengbin Yue, Li Zhang, Zhaolei
Copyright Year	2013
Abstract	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.
Related Links	http://dx.doi.org/10.1093/nar/gkt574
Starting Page	153
File Format	PDF
ISSN	03051048
e-ISSN	13624962
Journal	Nucleic Acids Research
Issue Number	16
Volume Number	41
Language	English
Publisher	Oxford University Press
Publisher Date	2013-09-01
Access Restriction	Open
Rights Holder	Oxford University Press
Subject Keyword	Genetics Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics

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