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Assessment of composite motif discovery methods

Klepper, Kjetil; Sandve, Geir Kjetil; Abul, Osman; Johansen, Jostein; Drabløs, Finn
Journal article, Peer reviewed
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1471-2105-9-123.pdf (309.6Kb)
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http://hdl.handle.net/11250/2366791
Utgivelsesdato
2008
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  • Institutt for datateknologi og informatikk [3873]
  • Institutt for klinisk og molekylær medisin [2070]
  • Publikasjoner fra CRIStin - NTNU [20946]
Originalversjon
BMC Bioinformatics 2008, 9(123)   10.1186/1471-2105-9-123
Sammendrag
Background: Computational discovery of regulatory elements is an important area of

bioinformatics research and more than a hundred motif discovery methods have been published.

Traditionally, most of these methods have addressed the problem of single motif discovery –

discovering binding motifs for individual transcription factors. In higher organisms, however,

transcription factors usually act in combination with nearby bound factors to induce specific

regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets

of motifs bound by multiple cooperating transcription factors, so called composite motifs or cisregulatory

modules. Given the large number and diversity of methods available, independent

assessment of methods becomes important. Although there have been several benchmark studies

of single motif discovery, no similar studies have previously been conducted concerning composite

motif discovery.

Results: We have developed a benchmarking framework for composite motif discovery and used

it to evaluate the performance of eight published module discovery tools. Benchmark datasets were

constructed based on real genomic sequences containing experimentally verified regulatory

modules, and the module discovery programs were asked to predict both the locations of these

modules and to specify the single motifs involved. To aid the programs in their search, we provided

position weight matrices corresponding to the binding motifs of the transcription factors involved.

In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to

test the response of programs to varying levels of noise.

Conclusion: Although some of the methods tested tended to score somewhat better than others

overall, there were still large variations between individual datasets and no single method

performed consistently better than the rest in all situations. The variation in performance on

individual datasets also shows that the new benchmark datasets represents a suitable variety of

challenges to most methods for module discovery.
Utgiver
BioMed Central
Tidsskrift
BMC Bioinformatics

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