Open access
Date
2005-04Type
- Journal Article
ETH Bibliography
yes
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Abstract
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.
Conclusion
LLBG may provide an interesting alternative to current scoring functions for TFBS prediction. Show more
Permanent link
https://doi.org/10.3929/ethz-a-005046447Publication status
publishedExternal links
Journal / series
BMC bioinformaticsVolume
Pages / Article No.
Publisher
BioMed CentralSubject
TRANSKRIPTIONSFAKTOREN (MOLEKULARE GENETIK); BINDUNGSSTELLEN VON BIOMOLEKÜLEN; VORHERSAGETHEORIE (WAHRSCHEINLICHKEITSRECHNUNG); TRANSCRIPTION FACTORS (MOLECULAR GENETICS); BINDING SITES OF BIOMOLECULES; PREDICTION THEORY (PROBABILITY THEORY); SACCHAROMYCES (MYKOLOGIE); SACCHAROMYCES (MYCOLOGY); Promoter Sequence; Transcription Factor Binding Site; Background Model; Transcriptional Start Site; Base WordOrganisational unit
03309 - Gonnet, Gaston
02644 - Institut für Wissenschaftliches Rechnen, direkt
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ETH Bibliography
yes
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