首页> 外文会议>International Symposium on Knowledge and Systems Sciences(KSS2004); 20041110-12; Ishikawa(JP) >Computational Discovery of Yeast Transcriptional Regulatory Modules from DNA Sequences and Gene Expression Data
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Computational Discovery of Yeast Transcriptional Regulatory Modules from DNA Sequences and Gene Expression Data

机译:从DNA序列和基因表达数据的酵母转录调控模块的计算发现。

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In eukaryotes, gene expression is controlled by various transcription factors that bind to the promoter regions. Transcription factors may act positively, negatively or not at all. Different combinations of them may also activate or repress gene expression, and form regulatory networks of transcription. Uncovering such regulatory networks is a central challenge in genomic biology. In this study, we developed a data mining method for finding two informative kinds of transcriptional regulatory modules (TRMs) so called closed inf-TRMs and closed sup-TRMs from factor DNA-binding sites that are in turn predicted by transfac database. Our method is based on closed itemset mining that takes account the event: some factors always together bind to a set of genes and control their expression. These two kinds of TRMs will be further revised based on gene expression profiles data to find high confident TRMs. When being applied to yeast data, our method produced results that are consistent with the previous studies. Furthermore, the result TRMs are compact, concise and comprehensive to identify and interpret the transcriptional role of regulator combinations on sets of genes.
机译:在真核生物中,基因表达受到与启动子区域结合的各种转录因子的控制。转录因子可发挥积极,消极作用或根本不起作用。它们的不同组合也可以激活或抑制基因表达,并形成转录调控网络。发现这样的调控网络是基因组生物学的主要挑战。在这项研究中,我们开发了一种数据挖掘方法,可从转基因数据库预测的因子DNA结合位点中找到两种信息类型的转录调节模块(TRM),即所谓的封闭inf-TRM和封闭sup-TRM。我们的方法基于考虑事件的封闭项目集挖掘:某些因素总是一起绑定到一组基因并控制它们的表达。将根据基因表达谱数据对这两种TRM进行进一步修订,以找到高可信度的TRM。当应用于酵母数据时,我们的方法产生的结果与以前的研究一致。此外,结果TRM是紧凑,简洁和全面的,可识别和解释调节剂组合在基因集上的转录作用。

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