MCOIN: a novel heuristic for determining transcription factor binding site motif width

Alastair Kilpatrick, Bruce Ward, Stuart Aitken

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In transcription factor binding site discovery, the true width of the motif to be discovered is generally not known a priori. The ability to compute the most likely width of a motif is therefore a highly desirable property for motif discovery algorithms. However, this is a challenging computational problem as a result of changing model dimensionality at changing motif widths. The complexity of the problem is increased as the discovered model at the true motif width need not be the most statistically significant in a set of candidate motif models. Further, the core motif discovery algorithm used cannot guarantee to return the best possible result at each candidate width.
We present MCOIN, a novel heuristic for automatically determining transcription factor binding site motif width, based on motif containment and information content. Using realistic synthetic data and previously characterised prokaryotic data, we show that MCOIN outperforms the current most popular method (E-value of the resulting multiple alignment) as a predictor of motif width, based on mean absolute error. MCOIN is also shown to choose models which better match known sites at higher levels of motif conservation, based on ROC analysis.
We demonstrate the performance of MCOIN as part of a deterministic motif discovery algorithm and conclude that MCOIN outperforms current methods for determining motif width.
Original languageEnglish
Article number16
Number of pages12
JournalAlgorithms for Molecular Biology
Early online date24 Jun 2013
Publication statusPublished - 27 Jun 2013

Keywords / Materials (for Non-textual outputs)

  • Transcription factor binding sites
  • Motif discovery


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