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本文引用的文献

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MYBS: a comprehensive web server for mining transcription factor binding sites in yeast.MYBS:一个用于挖掘酵母中转录因子结合位点的综合网络服务器。
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W221-6. doi: 10.1093/nar/gkm379. Epub 2007 May 30.
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Detection of generic spaced motifs using submotif pattern mining.使用子基序模式挖掘检测通用间隔基序
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SPACER: identification of cis-regulatory elements with non-contiguous critical residues.间隔序列:具有非连续关键残基的顺式调控元件的鉴定
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Discovering motifs in ranked lists of DNA sequences.在DNA序列排名列表中发现基序。
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Method for identifying transcription factor binding sites in yeast.鉴定酵母中转录因子结合位点的方法。
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An improved map of conserved regulatory sites for Saccharomyces cerevisiae.酿酒酵母保守调控位点的改进图谱。
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Limitations and potentials of current motif discovery algorithms.当前基序发现算法的局限性与潜力。
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Mining ChIP-chip data for transcription factor and cofactor binding sites.挖掘芯片级免疫沉淀(ChIP-chip)数据以寻找转录因子和辅因子结合位点。
Bioinformatics. 2005 Jun;21 Suppl 1:i403-12. doi: 10.1093/bioinformatics/bti1043.
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Identification of functional transcription factor binding sites using closely related Saccharomyces species.利用近缘酿酒酵母物种鉴定功能性转录因子结合位点
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A boosting approach for motif modeling using ChIP-chip data.一种使用芯片杂交(ChIP-chip)数据进行基序建模的增强方法。
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发现酵母转录因子的间隔结合位点。

Discovering gapped binding sites of yeast transcription factors.

作者信息

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

机构信息

Department of Bio-Industrial Mechatronics Engineering, Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan.

出版信息

Proc Natl Acad Sci U S A. 2008 Feb 19;105(7):2527-32. doi: 10.1073/pnas.0712188105. Epub 2008 Feb 13.

DOI:10.1073/pnas.0712188105
PMID:18272477
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2268170/
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.

摘要

一个有间隔的转录因子结合位点(TFBS)包含一个或多个高度简并的位置。发现有间隔的基序很困难,因为在基序中允许高度简并的位置会极大地扩大搜索空间并使发现过程复杂化。在此,我们提出一种发现TFBS的方法,特别是有间隔的基序。我们使用芯片免疫沉淀(ChIP-chip)数据来判断转录因子与假定靶启动子的结合强度,并使用相关物种的直系同源序列来判断预测的TFBS的进化保守程度。候选基序通过生长紧凑的基序块以及连接两个候选块来构建,在它们之间允许0至15个简并位置。对所得模式区分靶基因和非靶基因的能力进行统计评估。然后,提出一种基于位置的排序程序,通过收集位置一致性来增强真实基序的信号。对32个已知酵母TFBS的实证测试表明,该方法在识别有间隔的基序方面高度准确,优于当前方法,并且在无间隔的基序上也表现良好。对另外54个转录因子的预测成功发现了11个有间隔和38个无间隔的基序,这些基序得到了文献的支持。我们的方法在预测经实验验证的TFBS方面实现了高灵敏度和特异性。