ArticlePLoS computational biology2013
A discriminative approach for unsupervised clustering of DNA sequence motifs.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transposable elements modulate human RNA abundance and splicing via specific RNA-protein interactions.Genome biology · 2014Pooled it
- abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis.BMC bioinformatics · 2022Article
- Bioinformatics Approaches for Determining the Functional Impact of Repetitive Elements on Non-coding RNAs.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Using systems medicine to identify a therapeutic agent with potential for repurposing in inflammatory bowel disease.Disease models & mechanisms · 2020Article
- GSMC: Combining Parallel Gibbs Sampling with Maximal Cliques for Hunting DNA Motif.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- RSAT matrix-clustering: dynamic exploration and redundancy reduction of transcription factor binding motif collections.Nucleic acids research · 2017Article
- DynaMIT: the dynamic motif integration toolkit.Nucleic acids research · 2016Article
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4 authors.
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Abstract
Algorithmic comparison of DNA sequence motifs is a problem in bioinformatics that has received increased attention during the last years. Its main applications concern characterization of potentially novel motifs and clustering of a motif collection in order to remove redundancy. Despite growing interest in motif clustering, the question which motif clusters to aim at has so far not been systematically addressed. Here we analyzed motif similarities in a comprehensive set of vertebrate transcription factor classes. For this we developed enhanced similarity scores by inclusion of the information coverage (IC) criterion, which evaluates the fraction of information an alignment covers in aligned motifs. A network-based method enabled us to identify motif clusters with high correspondence to DNA-binding domain phylogenies and prior experimental findings. Based on this analysis we derived a set of motif families representing distinct binding specificities. These motif families were used to train a classifier which was further integrated into a novel algorithm for unsupervised motif clustering. Application of the new algorithm demonstrated its superiority to previously published methods and its ability to reproduce entrained motif families. As a result, our work proposes a probabilistic approach to decide whether two motifs represent common or distinct binding specificities.
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