Literature DB >> 16644789

Method for identifying transcription factor binding sites in yeast.

Huai-Kuang Tsai1, Grace Tzu-Wei Huang, Meng-Yuan Chou, Henry Horng-Shing Lu, Wen-Hsiung Li.   

Abstract

MOTIVATION: Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit.
RESULTS: We develop a TFBS identification method, TFBSfinder, which utilizes several data sources, including DNA sequences, phylogenetic information, microarray data and ChIP-chip data. For a TF, TFBSfinder rigorously selects a set of reliable target genes and a set of non-target genes (as a background set) to find overrepresented and conserved motifs in target genes. A new metric for measuring the degree of conservation at a binding site across species and methods for clustering motifs and for inferring position weight matrices are proposed. For synthetic data and yeast cell cycle TFs, TFBSfinder identifies motifs that are highly similar to known consensuses. Moreover, TFBSfinder outperforms well-known methods. AVAILABILITY: http://cg1.iis.sinica.edu.tw/~TFBSfinder/.

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Year:  2006        PMID: 16644789     DOI: 10.1093/bioinformatics/btl160

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

1.  Discovering gapped binding sites of yeast transcription factors.

Authors:  Chien-Yu Chen; Huai-Kuang Tsai; Chen-Ming Hsu; Mei-Ju May Chen; Hao-Geng Hung; Grace Tzu-Wei Huang; Wen-Hsiung Li
Journal:  Proc Natl Acad Sci U S A       Date:  2008-02-13       Impact factor: 11.205

2.  Prediction of human miRNAs using tissue-selective motifs in 3' UTRs.

Authors:  Yao-Ming Chang; Hsueh-Fen Juan; Tzu-Ying Lee; Ya-Ya Chang; Yao-Ming Yeh; Wen-Hsiung Li; Arthur Chun-Chieh Shih
Journal:  Proc Natl Acad Sci U S A       Date:  2008-10-24       Impact factor: 11.205

3.  Identifying functional mechanisms of gene and protein regulatory networks in response to a broader range of environmental stresses.

Authors:  Cheng-Wei Li; Bor-Sen Chen
Journal:  Comp Funct Genomics       Date:  2010-04-28

4.  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

5.  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

6.  Impact of DNA-binding position variants on yeast gene expression.

Authors:  Krishna B S Swamy; Chung-Yi Cho; Sufeng Chiang; Zing Tsung-Yeh Tsai; Huai-Kuang Tsai
Journal:  Nucleic Acids Res       Date:  2009-11       Impact factor: 16.971

7.  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

8.  On the adaptive design rules of biochemical networks in evolution.

Authors:  Bor-Sen Chen; Wan-Shian Wu; Wei-Sheng Wu; Wen-Hsiung Li
Journal:  Evol Bioinform Online       Date:  2007-02-28       Impact factor: 1.625

9.  Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data.

Authors:  Wei-Sheng Wu; Wen-Hsiung Li; Bor-Sen Chen
Journal:  BMC Bioinformatics       Date:  2007-06-08       Impact factor: 3.169

10.  Inferring transcriptional compensation interactions in yeast via stepwise structure equation modeling.

Authors:  Grace S Shieh; Chung-Ming Chen; Ching-Yun Yu; Juiling Huang; Woei-Fuh Wang; Yi-Chen Lo
Journal:  BMC Bioinformatics       Date:  2008-03-03       Impact factor: 3.169

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