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

Background 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 co...

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Vydané v:Algorithms for molecular biology Ročník 8; číslo 1; s. 16
Hlavní autori: Kilpatrick, Alastair M, Ward, Bruce, Aitken, Stuart
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 27.06.2013
BioMed Central Ltd
Springer Nature B.V
Predmet:
Algorithms
Analysis
Bioinformatics
Biomedical and Life Sciences
Cellular and Medical Topics
Computational Biology/Bioinformatics
E coli
Heuristic
Life Sciences
Logos
Physiological
Studies
Taiwan
Motif width
Transcription factor binding sites
Bacterial motifs
Motif discovery
ISSN:1748-7188, 1748-7188
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Shrnutí:Background 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. Results 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. Conclusions 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.
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ISSN:1748-7188
1748-7188
DOI:10.1186/1748-7188-8-16