Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites.
Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites.
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通过整合基因表达和 DNase I 超敏感位点了解转录因子调控。
DOI：
10.1155/2015/757530
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发表时间：
2015
影响因子：
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通讯作者：
Wang Y
中科院分区：
文献类型：
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作者：
Wang G;Wang F;Huang Q;Li Y;Liu Y;Wang Y
转录因子是与DNA序列结合以调节基因转录的蛋白质。转录因子结合位点是由一个或多个转录因子特异性结合的短DNA序列（5 - 20bp长）。转录因子结合位点的鉴定及其功能的预测仍然是计算生物学中具有挑战性的问题。本研究通过整合TRANSFAC数据库中已知的DNase I超敏位点和位置权重矩阵，鉴定出基因调控区的转录因子结合位点。基于宫颈癌HeLaS3细胞和HeLaS3 -ifnα4h细胞（干扰素治疗HeLaS3细胞4小时）的整体基因表达模式，我们提出了一种基于模型的计算方法来预测一组可能导致这种差异基因表达的转录因子。值得注意的是，IRF、IRF-2、IRF-9、IRF-1和IRF-3、ICSBP等10个预测功能因子中有6个属于干扰素调节因子家族，并在干扰素治疗后上调基因表达水平。另一个因子ISGF-3也是由干扰素诱导的转录激活因子。使用不同的转录因子结合位点选择标准，我们的模型预测结果是一致的。我们的模型展示了计算鉴定基因调控中功能性转录因子的潜力。
Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5–20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.