Linear regression models predicting strength of transcriptional activity of promoters.

Tetsushi Yada, Keigo Yoshida, Masao Morita, Takeaki Taniguchi, Takuma Irie, Yutaka Suzuki
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Abstract

We developed linear regression models which predict strength of transcriptional activity of promoters from their sequences. Intrinsic transcriptional strength data of 451 human promoter sequences in three cell lines (HEK293, MCF7 and 3T3), which were measured by systematic luciferase reporter gene assays, were used to build the models. The models sum up contributions of CG dinucleotide content and transcription factor binding sites (TFBSs) to transcriptional strength. We evaluated prediction accuracies of the models by cross validation tests and found that they have adequate ability for predicting transcriptional strength of promoters in spite of their simple formalization. We also evaluated statistical significance of the contributions and proposed a picture of regulatory code hidden in promoter sequences. That is, CG dinucleotide content and TFBSs mainly determine strength of transcriptional activity under ubiquitous and specific environments, respectively.

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预测启动子转录活性强度的线性回归模型。
我们开发了线性回归模型来预测启动子序列的转录活性强度。利用系统荧光素酶报告基因法测定的451个人启动子序列在HEK293、MCF7和3T3三种细胞系中的内在转录强度数据构建模型。这些模型总结了CG二核苷酸含量和转录因子结合位点(TFBSs)对转录强度的贡献。我们通过交叉验证测试评估了模型的预测准确性,发现尽管它们的形式化简单,但它们具有足够的预测启动子转录强度的能力。我们还评估了贡献的统计显著性,并提出了隐藏在启动子序列中的调控代码的图片。即CG二核苷酸的含量和TFBSs分别主要决定了泛在环境和特定环境下的转录活性强弱。
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