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Exploring the Link Between Gene Expression and Protein Binding by Integrating mRNA Microarray and ChIP-Seq Data

机译:通过整合mRNA微阵列和芯片-SEQ数据来探索基因表达与蛋白质结合的联系

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ChIP-sequencing experiments are routinely used to study genome-wide chromatin marks. Due to the high-cost and complexity associated with this technology, it is of great interest to investigate whether the low-cost option of microarray experiments can be used in combination with ChIP-seq experiments. Most integrative analyses do not consider important features of ChIP-seq data, such as spatial dependencies and ChIP-efficiencies. In this paper, we address these issues by applying a Markov random field model to ChIP-seq data on the protein Brd4, for which both ChIP-seq and microarray data are available on the same biological conditions. We investigate the correlation between the enrichment probabilities around transcription start sites, estimated by the Markov model, and microarray gene expression values. Our preliminary results suggest that binding of the protein is associated with lower gene expression, but differential binding across different conditions does not show an association with differential expression of the associated genes.
机译:芯片测序实验通常用于研究基因组染色质标记。由于与该技术相关的高成本和复杂性,研究了微阵列实验的低成本选项是否可以与芯片-SEQ实验组合使用的兴趣。大多数综合分析不考虑芯片-SEQ数据的重要特征,例如空间依赖性和芯片效率。在本文中,我们通过将Markov随机现场模型应用于蛋白质BRD4上的芯片-SEQ数据来解决这些问题,芯片-SEQ和微阵列数据都可以在相同的生物条件下获得。我们研究了转录开始网站周围的富集概率与Markov模型的估计和微阵列基因表达值之间的相关性。我们的初步结果表明蛋白质的结合与较低的基因表达相关,但不同条件的差异结合不显示与相关基因的差异表达相关联。

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