Literature DB >> 18272477

Discovering gapped binding sites of yeast transcription factors.

Chien-Yu Chen1, Huai-Kuang Tsai, Chen-Ming Hsu, Mei-Ju May Chen, Hao-Geng Hung, Grace Tzu-Wei Huang, Wen-Hsiung Li.   

Abstract

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.

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Year:  2008        PMID: 18272477      PMCID: PMC2268170          DOI: 10.1073/pnas.0712188105

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  32 in total

1.  BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes.

Authors:  X Liu; D L Brutlag; J S Liu
Journal:  Pac Symp Biocomput       Date:  2001

2.  Discovering regulatory elements in non-coding sequences by analysis of spaced dyads.

Authors:  J van Helden; A F Rios; J Collado-Vides
Journal:  Nucleic Acids Res       Date:  2000-04-15       Impact factor: 16.971

3.  Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation.

Authors:  L J Jensen; S Knudsen
Journal:  Bioinformatics       Date:  2000-04       Impact factor: 6.937

4.  Regulatory element detection using correlation with expression.

Authors:  H J Bussemaker; H Li; E D Siggia
Journal:  Nat Genet       Date:  2001-02       Impact factor: 38.330

5.  Characterization of the ECB binding complex responsible for the M/G(1)-specific transcription of CLN3 and SWI4.

Authors:  Bernard Mai; Shawna Miles; Linda L Breeden
Journal:  Mol Cell Biol       Date:  2002-01       Impact factor: 4.272

6.  Identifying regulatory networks by combinatorial analysis of promoter elements.

Authors:  Y Pilpel; P Sudarsanam; G M Church
Journal:  Nat Genet       Date:  2001-10       Impact factor: 38.330

7.  A statistical method for finding transcription factor binding sites.

Authors:  S Sinha; M Tompa
Journal:  Proc Int Conf Intell Syst Mol Biol       Date:  2000

8.  An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments.

Authors:  X Shirley Liu; Douglas L Brutlag; Jun S Liu
Journal:  Nat Biotechnol       Date:  2002-07-08       Impact factor: 54.908

9.  Transcriptional regulatory networks in Saccharomyces cerevisiae.

Authors:  Tong Ihn Lee; Nicola J Rinaldi; François Robert; Duncan T Odom; Ziv Bar-Joseph; Georg K Gerber; Nancy M Hannett; Christopher T Harbison; Craig M Thompson; Itamar Simon; Julia Zeitlinger; Ezra G Jennings; Heather L Murray; D Benjamin Gordon; Bing Ren; John J Wyrick; Jean-Bosco Tagne; Thomas L Volkert; Ernest Fraenkel; David K Gifford; Richard A Young
Journal:  Science       Date:  2002-10-25       Impact factor: 47.728

10.  MYBS: a comprehensive web server for mining transcription factor binding sites in yeast.

Authors:  Huai-Kuang Tsai; Meng-Yuan Chou; Ching-Hua Shih; Grace Tzu-Wei Huang; Tien-Hsien Chang; Wen-Hsiung Li
Journal:  Nucleic Acids Res       Date:  2007-05-30       Impact factor: 16.971

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  13 in total

1.  Setting boundaries for genome-wide heterochromatic DNA deletions through flanking inverted repeats in Tetrahymena thermophila.

Authors:  Chih-Yi Gabriela Lin; Ju-Lan Chao; Huai-Kuang Tsai; Douglas Chalker; Meng-Chao Yao
Journal:  Nucleic Acids Res       Date:  2019-06-04       Impact factor: 16.971

2.  The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based high-throughput data.

Authors:  Junbai Wang
Journal:  BMC Bioinformatics       Date:  2010-08-04       Impact factor: 3.169

3.  Evaluation of methods for modeling transcription factor sequence specificity.

Authors:  Matthew T Weirauch; Atina Cote; Raquel Norel; Matti Annala; Yue Zhao; Todd R Riley; Julio Saez-Rodriguez; Thomas Cokelaer; Anastasia Vedenko; Shaheynoor Talukder; Harmen J Bussemaker; Quaid D Morris; Martha L Bulyk; Gustavo Stolovitzky; Timothy R Hughes
Journal:  Nat Biotechnol       Date:  2013-01-27       Impact factor: 54.908

4.  Aiolos collaborates with Blimp-1 to regulate the survival of multiple myeloma cells.

Authors:  K-H Hung; S-T Su; C-Y Chen; P-H Hsu; S-Y Huang; W-J Wu; M-J M Chen; H-Y Chen; P-C Wu; F-R Lin; M-D Tsai; K-I Lin
Journal:  Cell Death Differ       Date:  2016-01-29       Impact factor: 15.828

5.  Efficient motif search in ranked lists and applications to variable gap motifs.

Authors:  Limor Leibovich; Zohar Yakhini
Journal:  Nucleic Acids Res       Date:  2012-03-13       Impact factor: 16.971

6.  The spatial distribution of cis regulatory elements in yeast promoters and its implications for transcriptional regulation.

Authors:  Zhenguo Lin; Wei-Sheng Wu; Han Liang; Yong Woo; Wen-Hsiung Li
Journal:  BMC Genomics       Date:  2010-10-19       Impact factor: 3.969

7.  Predicting RNA-binding residues from evolutionary information and sequence conservation.

Authors:  Yu-Feng Huang; Li-Yuan Chiu; Chun-Chin Huang; Chien-Kang Huang
Journal:  BMC Genomics       Date:  2010-12-02       Impact factor: 3.969

8.  Predicting DNA-binding locations and orientation on proteins using knowledge-based learning of geometric properties.

Authors:  Chien-Chih Wang; Chien-Yu Chen
Journal:  Proteome Sci       Date:  2011-10-14       Impact factor: 2.480

9.  BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors.

Authors:  Junbai Wang
Journal:  BMC Bioinformatics       Date:  2009-10-20       Impact factor: 3.169

10.  A structural-based strategy for recognition of transcription factor binding sites.

Authors:  Beisi Xu; Dustin E Schones; Yongmei Wang; Haojun Liang; Guohui Li
Journal:  PLoS One       Date:  2013-01-08       Impact factor: 3.240

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