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Compo: composite motif discovery using discrete models

Sandve, Geir Kjetil; Abul, Osman; Drabløs, Finn
Journal article, Peer reviewed
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1471-2105-9-527.pdf (358.5Kb)
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http://hdl.handle.net/11250/2366787
Utgivelsesdato
2008
Metadata
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  • Institutt for klinisk og molekylær medisin [2599]
  • Publikasjoner fra CRIStin - NTNU [26591]
Originalversjon
BMC Bioinformatics 2008, 9(527)   10.1186/1471-2105-9-527
Sammendrag
Background: Computational discovery of motifs in biomolecular sequences is an established field,

with applications both in the discovery of functional sites in proteins and regulatory sites in DNA.

In recent years there has been increased attention towards the discovery of composite motifs,

typically occurring in cis-regulatory regions of genes.

Results: This paper describes Compo: a discrete approach to composite motif discovery that

supports richer modeling of composite motifs and a more realistic background model compared

to previous methods. Furthermore, multiple parameter and threshold settings are tested

automatically, and the most interesting motifs across settings are selected. This avoids reliance on

single hard thresholds, which has been a weakness of previous discrete methods. Comparison of

motifs across parameter settings is made possible by the use of p-values as a general significance

measure. Compo can either return an ordered list of motifs, ranked according to the general

significance measure, or a Pareto front corresponding to a multi-objective evaluation on sensitivity,

specificity and spatial clustering.

Conclusion: Compo performs very competitively compared to several existing methods on a

collection of benchmark data sets. These benchmarks include a recently published, large

benchmark suite where the use of support across sequences allows Compo to correctly identify

binding sites even when the relevant PWMs are mixed with a large number of noise PWMs.

Furthermore, the possibility of parameter-free running offers high usability, the support for multiobjective

evaluation allows a rich view of potential regulators, and the discrete model allows

flexibility in modeling and interpretation of motifs.
Utgiver
BioMed Central
Tidsskrift
BMC Bioinformatics

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