ArticleBioinformatics (Oxford, England)2014
Improving MEME via a two-tiered significance analysis.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Genome-wide identification and functional characterization of the CaD27 gene family in pepper under cold stress.Plant molecular biology · 2026Article
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- The transcript interactome of skeletal muscle RNA binding protein motif 3 (RBM3).Physiological reports · 2023Article
- Profiling lariat intermediates reveals genetic determinants of early and late co-transcriptional splicing.Molecular cell · 2022Article
- Selective modulation by PARP-1 of HIF-1α-recruitment to chromatin during hypoxia is required for tumor adaptation to hypoxic conditions.Redox biology · 2021Article
- Genome-Wide Comparative Analysis of HIF Binding Sites inFrontiers in genetics · 2019Article
- Direct AUC optimization of regulatory motifs.Bioinformatics (Oxford, England) · 2017Article
- WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data.Scientific reports · 2017Article
- Centromeres of the Yeast Komagataella phaffii (Pichia pastoris) Have a Simple Inverted-Repeat Structure.Genome biology and evolution · 2016Article
- Forward genetic screen of human transposase genomic rearrangements.BMC genomics · 2016Article
- Review of Different Sequence Motif Finding Algorithms.Avicenna journal of medical biotechnologyReview
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Authors and funding
3 authors.
Funding
Abstract
motivationWith over 9000 unique users recorded in the first half of 2013, MEME is one of the most popular motif-finding tools available. Reliable estimates of the statistical significance of motifs can greatly increase the usefulness of any motif finder. By analogy, it is difficult to imagine evaluating a BLAST result without its accompanying E-value. Currently MEME evaluates its EM-generated candidate motifs using an extension of BLAST's E-value to the motif-finding context. Although we previously indicated the drawbacks of MEME's current significance evaluation, we did not offer a practical substitute suited for its needs, especially because MEME also relies on the E-value internally to rank competing candidate motifs.
resultsHere we offer a two-tiered significance analysis that can replace the E-value in selecting the best candidate motif and in evaluating its overall statistical significance. We show that our new approach could substantially improve MEME's motif-finding performance and would also provide the user with a reliable significance analysis. In addition, for large input sets, our new approach is in fact faster than the currently implemented E-value analysis.
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