Discovering gapped binding sites of yeast transcription factors

A gapped transcription factor-binding site (TFBS) contains one or more highly degenerate positions. Discovering gapped motifs is difficult, because allowing highly degenerate positions in a motif greatly enlarges the search space and complicates the discovery process. Here, we propose a method for d...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2008-02, Vol.105 (7), p.2527-2532
Main Authors: Chen, Chien-Yu, Tsai, Huai-Kuang, Hsu, Chen-Ming, May Chen, Mei-Ju, Hung, Hao-Geng, Huang, Grace Tzu-Wei, Li, Wen-Hsiung
Format: Article
Language:English
Subjects:
Binding Sites
Bioinformatics
Biological Sciences
DNA-binding domains
Evolution
Motifs
Nucleotides
P values
Phylogeny
Promoter regions
Proteins
Saccharomyces cerevisiae
transcription factor-binding site
transcription factors
Transcription Factors - genetics
Transcription Factors - metabolism
Wildlife conservation
Yeast
Yeasts
Yeasts - genetics
Yeasts - metabolism
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Summary:A gapped transcription factor-binding site (TFBS) contains one or more highly degenerate positions. Discovering gapped motifs is difficult, because allowing highly degenerate positions in a motif greatly enlarges the search space and complicates the discovery process. Here, we propose a method for discovering TFBSs, especially gapped motifs. We use ChIP-chip data to judge the binding strength of a TF to a putative target promoter and use orthologous sequences from related species to judge the degree of evolutionary conservation of a predicted TFBS. Candidate motifs are constructed by growing compact motif blocks and by concatenating two candidate blocks, allowing 0-15 degenerate positions in between. The resultant patterns are statistically evaluated for their ability to distinguish between target and nontarget genes. Then, a position-based ranking procedure is proposed to enhance the signals of true motifs by collecting position concurrences. Empirical tests on 32 known yeast TFBSs show that the method is highly accurate in identifying gapped motifs, outperforming current methods, and it also works well on ungapped motifs. Predictions on additional 54 TFs successfully discover 11 gapped and 38 ungapped motifs supported by literature. Our method achieves high sensitivity and specificity for predicting experimentally verified TFBSs.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0712188105