Research article
Assessment of composite motif discovery methods
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* Corresponding author: Kjetil Klepper kjetil.klepper@ntnu.no
1 Department of Cancer Reasearch and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
2 Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway
3 Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey
BMC Bioinformatics 2008, 9:123 doi:10.1186/1471-2105-9-123
Published: 26 February 2008Abstract
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 cis-regulatory 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.