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A Discriminative Approach for Unsupervised Clustering of DNA Sequence Motifs

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.

10.1371/journal.pcbi.1002958

Projects: A3.6: Transcription factor network and links to metabolism in NAFLD

PLoS Comput Biol
PLoS Comput Biol 9(3) : e1002958
21st Mar 2013

Philip Stegmaier, Alexander Kel, Edgar Wingender, Jürgen Borlak

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[Jürgen Borlak]

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Views: 2265
  • Created: 28th Mar 2013 at 11:59
  • Last updated: 24th Oct 2013 at 16:14

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