A discriminative approach for unsupervised clustering of DNA sequence motifs

PLoS Comput Biol. 2013;9(3):e1002958. doi: 10.1371/journal.pcbi.1002958. Epub 2013 Mar 21.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cluster Analysis
  • Computational Biology / methods*
  • DNA / genetics
  • DNA / metabolism
  • Databases, Genetic
  • Gene Regulatory Networks
  • Logistic Models
  • Nucleotide Motifs*
  • Phylogeny
  • Sequence Alignment / methods*
  • Sequence Analysis, DNA / methods*
  • Transcription Factors / genetics
  • Transcription Factors / metabolism


  • Transcription Factors
  • DNA

Grants and funding

PS was partially funded by the ERA-Net EuroTransBio-5 project “ANEUDIA.” The work of AK was funded by the Russian federal program “Living systems,” State Contract #11.519.11.2031 and by FP7 project “SysCol” and BMBF project “GerontoShield.” The author JB gratefully acknowledges support from The Virtual Liver Network (grant 031 6154) of the German Federal Ministry of Education and Research (BMBF). PS, AK, and EW were further supported by the EU 7th Framework project “LipidomicNet” (grant no. 202272). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.