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Decoding ChIP-seq with a double-binding signal refines binding peaks to single-nucleotides and predicts cooperative interaction

机译:用双结合信号解码ChIP-seq可改善与单核苷酸的结合峰并预测协同相互作用

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摘要

The comprehension of protein and DNA binding in vivo is essential to understand gene regulation. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides a global map of the regulatory binding network. Most ChIP-seq analysis tools focus on identifying binding regions from coverage enrichment. However, less work has been performed to infer the physical and regulatory details inside the enriched regions. This research extends a previous blind-deconvolution approach to develop a post-peak–calling algorithm that improves binding site resolution and predicts cooperative interactions. At the core of our new method is a physically motivated model that characterizes the binding signal as an extreme value distribution. This model suggests a mathematical framework to study physical properties of DNA shearing from the ChIP-seq coverage. The model explains the ChIP-seq coverage with two signals: The first considers DNA fragments with only a single binding event, whereas the second considers fragments with two binding events (a double-binding signal). The model incorporates motif discovery and is able to detect multiple sites in an enriched region with single-nucleotide resolution, high sensitivity, and high specificity. Our method improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate <11% for a set of 47 experimentally validated prokaryotic sites. It also improves resolution of highly enriched regions of large-scale eukaryotic data sets. The double-binding signal provides a novel application in ChIP-seq analysis: the identification of cooperative interaction. Predictions of known cooperative binding sites show a 0.85 area under an ROC curve.
机译:体内蛋白质和DNA结合的理解对于理解基因调控至关重要。染色质免疫沉淀后再测序(ChIP-seq)提供了调节结合网络的全局图。大多数ChIP-seq分析工具都专注于通过覆盖范围丰富来识别结合区域。但是,为推断富集区域内的物理和法规细节所做的工作较少。这项研究扩展了以前的盲解卷积方法,以开发一种峰后调用算法,该算法可以提高结合位点的分辨率并预测协作相互作用。我们新方法的核心是一个物理激励模型,该模型将绑定信号表征为极值分布。该模型提出了一个数学框架,用于研究ChIP-seq覆盖范围内的DNA剪切的物理特性。该模型用两个信号解释了ChIP-seq的覆盖范围:第一个考虑仅具有单个结合事件的DNA片段,而第二个考虑具有两个结合事件的片段(双结合信号)。该模型结合了基序发现,并能够以单核苷酸分辨率,高灵敏度和高特异性检测富集区域中的多个位点。对于47个经过实验验证的原核位点,我们的方法可将峰值呼叫者灵敏度从不足45%提高到94%,假阳性率<11%。它还可以提高大规模真核数据集高度富集区域的分辨率。双重结合信号在ChIP-seq分析中提供了一种新颖的应用:协同相互作用的鉴定。已知的合作结合位点的预测显示ROC曲线下的面积为0.85。

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