DNA motif elucidation using belief propagation

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.

A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

“…The performance of the models is quite satisfactory since the mode of Spearman rank correlation coefficient is near 0.9. On the other hand, it can be observed that the model lengths are usually around 12 ∼ 14 nt which are consistent with the existing TFBS knowledge [20]. Each dot denotes a single method's performance on a single dataset.…”

“…Robust estimate procedures proposed in RankMotif++ [14] can then be applied to learn the positive k-mers. Nonetheless, it has been pointed out that such a robust procedure may not be suitable for all proteins [20]. In light of that, the highly ranked k-mers are regarded as the positive k-mers in this study.…”

“…It assumes independence between adjacent motif positions, as justified by the experimental and theoretical statistical mechanical study [18]. Nonetheless, it is well-known that adjacent nucleotide dependency exists in some DNA motifs [20]. Thus a recent attempt has been made to generalize the model to handle di-nucleotide representations [19].…”

A repetitive sequence assembler based on next-generation sequencing

“…For the repeats larger than the read length, the current tools can easily capture them by assembling from NGS reads, whereas for those smaller than the read length, such as motifs, the assembly-based tools are useless. Consequently, for capturing short repeats, a motif identification-based method, such as kmerHMM (Wong et al, 2013), will be more helpful than the assembly-based method.…”

Evolving Transcription Factor Binding Site Models From Protein Binding Microarray Data

“…Robust estimate procedures proposed in RankMotif++ [16] can then be applied to learn the positive k-mers. Nonetheless, it has been pointed out that such a robust procedure may not be suitable for all proteins [21]. In light of that, the highly ranked k-mers are regarded as the positive k-mers in this paper.…”