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Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells

机译:模拟小鼠胚胎干细胞中转录因子结合和组蛋白修饰与基因表达水平的相对关系

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Transcription factor (TF) binding and histone modification (HM) are important for the precise control of gene expression. Hence, we constructed statistical models to relate these to gene expression levels in mouse embryonic stem cells. While both TF binding and HMs are highly ‘predictive' of gene expression levels (in a statistical, but perhaps not strictly mechanistic, sense), we find they show distinct differences in the spatial patterning of their predictive strength: TF binding achieved the highest predictive power in a small DNA region centered at the transcription start sites of genes, while the HMs exhibited high predictive powers across a wide region around genes. Intriguingly, our results suggest that TF binding and HMs are redundant in strict statistical sense for predicting gene expression. We also show that our TF and HM models are cell line specific; specifically, TF binding and HM are more predictive of gene expression in the same cell line, and the differential gene expression between cell lines is predictable by differential HMs. Finally, we found that the models trained solely on protein-coding genes are predictive of expression levels of microRNAs, suggesting that their regulation by TFs and HMs may share a similar mechanism to that for protein-coding genes.
机译:转录因子(TF)结合和组蛋白修饰(HM)对于精确控制基因表达很重要。因此,我们构建了统计模型,将这些模型与小鼠胚胎干细胞中的基因表达水平相关联。尽管TF结合和HMs都高度“预测”基因表达水平(从统计学的角度来看,但严格来说不是严格的机制),但我们发现它们在预测强度的空间模式上显示出明显的差异:TF结合实现了最高的预测在以基因转录起始位点为中心的小DNA区域中,强分子具有强大的预测能力,而HM在整个基因周围的宽泛区域都具有很高的预测能力。有趣的是,我们的结果表明,在严格的统计意义上,TF结合和HM在预测基因表达方面是多余的。我们还表明,我们的TF和HM模型是特定于细胞系的;具体而言,TF结合和HM更能预测同一细胞系中的基因表达,而细胞系之间的差异基因表达可通过差异HM预测。最后,我们发现仅在蛋白质编码基因上训练的模型可预测microRNA的表达水平,这表明它们受TF和HM调控的机制可能与蛋白质编码基因相似。

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