ArticlePloS one2014
STEME: a robust, accurate motif finder for large data sets.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A noncanonical AR addiction drives enzalutamide resistance in prostate cancer.Nature communications · 2021Article
- MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs.Bioinformatics (Oxford, England) · 2020Article
- Article
- SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing.Bioinformatics (Oxford, England) · 2019Article
- Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets.Nucleic acids research · 2018Article
- Meta-expression analysis of unannotated genes in rice and approaches for network construction to suggest the probable roles.Plant molecular biology · 2018Article
- Performance evaluation for MOTIFSIM.Biological procedures online · 2018Article
- GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
Motif finding is a difficult problem that has been studied for over 20 years. Some older popular motif finders are not suitable for analysis of the large data sets generated by next-generation sequencing. We recently published an efficient approximation (STEME) to the EM algorithm that is at the core of many motif finders such as MEME. This approximation allows the EM algorithm to be applied to large data sets. In this work we describe several efficient extensions to STEME that are based on the MEME algorithm. Together with the original STEME EM approximation, these extensions make STEME a fully-fledged motif finder with similar properties to MEME. We discuss the difficulty of objectively comparing motif finders. We show that STEME performs comparably to existing prominent discriminative motif finders, DREME and Trawler, on 13 sets of transcription factor binding data in mouse ES cells. We demonstrate the ability of STEME to find long degenerate motifs which these discriminative motif finders do not find. As part of our method, we extend an earlier method due to Nagarajan et al. for the efficient calculation of motif E-values. STEME's source code is available under an open source license and STEME is available via a web interface.
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