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Motto: Representing Motifs in Consensus Sequences with Minimum Information Loss.

Mengchi Wang1, David Wang2, Kai Zhang1

  • 1Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, California 92093.

Genetics
|August 21, 2020
PubMed
Summary

We developed sequence Motto, a novel method to convert position weight matrices (PWMs) into concise consensus sequences. This approach minimizes information loss, improving motif analysis and transcription factor binding site identification.

Keywords:
consensusinformation theorymotifsequence logotranscription factor binding

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Motif representation is crucial for sequence analysis.
  • Position weight matrices (PWMs) and sequence logos are common but can be cumbersome.
  • Wildcard-style consensus sequences offer a more compact representation.

Purpose of the Study:

  • To develop a mathematical framework for converting PWMs to consensus sequences with minimal information loss.
  • To introduce sequence Motto, an efficient algorithm for this conversion.
  • To demonstrate the utility of sequence Motto for identifying transcription factor binding sites.

Main Methods:

  • Utilized mutual information theory and Jensen-Shannon divergence for framework development.
  • Implemented an efficient algorithm supporting various character sets (nucleotides, amino acids, custom).
  • Benchmarked sequence Motto against existing methods using FIMO for PWM scanning.

Main Results:

  • Sequence Motto effectively represents motif information.
  • The method accurately identifies binding sites for 1156 human transcription factors.
  • Achieved an average 0.81 area under the precision-recall curve, outperforming existing methods significantly (P < 0.01).

Conclusions:

  • Sequence Motto provides a statistically justified, distilled summary of motifs.
  • This representation enhances motif interpretation and binding site identification.
  • The method offers a significant improvement over existing consensus sequence generation techniques.