ArticleBioinformatics (Oxford, England)2015
Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application toJournal of the Royal Society, Interface · 2020Article
- ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery.Bioinformatics (Oxford, England) · 2019Article
- ATR inhibition controls aggressive prostate tumors deficient in Y-linked histone demethylase KDM5D.The Journal of clinical investigation · 2018Article
- Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets.Nucleic acids research · 2018Article
- RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks.BMC bioinformatics · 2017Article
- GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- RNA inverse folding using Monte Carlo tree search.BMC bioinformatics · 2017Article
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
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2 authors.
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Abstract
motivationThe motif discovery problem consists of finding recurring patterns of short strings in a set of nucleotide sequences. This classical problem is receiving renewed attention as most early motif discovery methods lack the ability to handle large data of recent genome-wide ChIP studies. New ChIP-tailored methods focus on reducing computation time and pay little regard to the accuracy of motif detection. Unlike such methods, our method focuses on increasing the detection accuracy while maintaining the computation efficiency at an acceptable level. The major advantage of our method is that it can mine diverse multiple motifs undetectable by current methods.
resultsThe repulsive parallel Markov chain Monte Carlo (RPMCMC) algorithm that we propose is a parallel version of the widely used Gibbs motif sampler. RPMCMC is run on parallel interacting motif samplers. A repulsive force is generated when different motifs produced by different samplers near each other. Thus, different samplers explore different motifs. In this way, we can detect much more diverse motifs than conventional methods can. Through application to 228 transcription factor ChIP-seq datasets of the ENCODE project, we show that the RPMCMC algorithm can find many reliable cofactor interacting motifs that existing methods are unable to discover.
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