Reliable prediction of transcription factor binding sites by phylogenetic verification.
We present a statistical methodology that largely improves the accuracy in computational predictions of transcription factor (TF) binding sites in eukaryote genomes. This method models the cross-species conservation of binding sites without relying on accurate sequence alignment. It can be coupled with any motif-finding algorithm that searches for overrepresented sequence motifs in individual species and can increase the accuracy of the coupled motif-finding algorithm. Because this method is capable of accurately detecting TF binding sites, it also enhances our ability to predict the cis-regulatory modules. We applied this method on the published chromatin immunoprecipitation (ChIP)-chip data in Saccharomyces cerevisiae and found that its sensitivity and specificity are 9% and 14% higher than those of two recent methods. We also recovered almost all of the previously verified TF binding sites and made predictions on the cis-regulatory elements that govern the tight regulation of ribosomal protein genes in 13 eukaryote species (2 plants, 4 yeasts, 2 worms, 2 insects, and 3 mammals). These results give insights to the transcriptional regulation in eukaryotic organisms.
Li
Xiaoman
X
Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, CA 94305-4065, USA. shawnli@iupui.edu
Zhong
Sheng
S
Wong
Wing H
WH
D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D017124:Conserved Sequence; D004268:DNA-Binding Proteins / Q000378:metabolism*; D010802:Phylogeny; D011237:Predictive Value of Tests; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000378:metabolism*
D004268:DNA-Binding Proteins; D014157:Transcription Factors