Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data.
Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data.
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DOI：
10.1038/nmeth.1246
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发表时间：
2008-09
期刊：
影响因子：
48
通讯作者：
Sidow, Arend
中科院分区：
文献类型：
--
作者：
Valouev, Anton;Johnson, David S.;Sundquist, Andreas;Medina, Catherine;Anton, Elizabeth;Batzoglou, Serafim;Myers, Richard M.;Sidow, Arend
蛋白质复合物和DNA之间的分子相互作用执行必要的基因调控功能。通过染色质免疫沉淀结合大规模平行测序（ChIP-Seq）揭示这种相互作用最近已成为人们强烈兴趣的焦点。我们在这里介绍QuEST（序列标签的定量富集），这是一个基于核密度估计方法的强大统计框架，它利用ChIP-Seq数据来确定蛋白质复合物与DNA接触的位置。使用QuEST，我们发现了数千个人类转录因子SRF，GABP和NRSF的结合位点，平均分辨率约为20个碱基对。对QuEST识别序列的基于MEM的基序分析揭示了SRF辅因子的DNA结合，提供了可以从ChIP-Seq数据获得辅因子结合特异性的证据。通过将QuEST分析与基因本体（GO）注释和表达数据相结合，我们说明了如何推断转录因子的一般功能。
Molecular interactions between protein complexes and DNA carry out essential gene regulatory functions. Uncovering such interactions by means of chromatin-immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) has recently become the focus of intense interest. We here introduce QuEST (Quantitative Enrichment of Sequence Tags), a powerful statistical framework based on the Kernel Density Estimation approach, which utilizes ChIP-Seq data to determine positions where protein complexes come into contact with DNA. Using QuEST, we discovered several thousand binding sites for the human transcription factors SRF, GABP and NRSF at an average resolution of about 20 base-pairs. MEME-based motif analyses on the QuEST-identified sequences revealed DNA binding by cofactors of SRF, providing evidence that cofactor binding specificity can be obtained from ChIP-Seq data. By combining QuEST analyses with gene ontology (GO) annotations and expression data, we illustrate how general functions of transcription factors can be inferred.
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