Scoring functions for transcription factor binding site prediction.
BACKGROUND
Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations.


RESULTS
Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome. Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study.


CONCLUSIONS
LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.
Friberg
Markus
M
Institute of Computational Science, ETH, 8092 Zurich, Switzerland. friberg@inf.ethz.ch
von Rohr
Peter
P
Gonnet
Gaston
G
D000465:Algorithms; D020816:Amino Acid Motifs; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D019277:Entropy; D005656:Fungal Proteins / Q000737:chemistry; D008024:Ligands; D015233:Models, Statistical; D011485:Protein Binding; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae; D016415:Sequence Alignment; D012984:Software; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics*
D005656:Fungal Proteins; D008024:Ligands; D011506:Proteins; D014157:Transcription Factors; D004247:DNA