Advancements in the field of next generation sequencing lead to the generation of ever-more data, with the challenge often being how to combine and reconcile results from different OMICs studies such as genome, epigenome and transcriptome. Here we provide an overview of the standard processing pipelines for ChIP-seq and RNA-seq as well as common downstream analyses. We describe popular multi-omics data integration approaches used to identify target genes and co-factors, and we discuss how machine learning techniques may predict transcriptional regulators and gene expression.

中文翻译：

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解答：当前的数据整合方法可揭示转录调控机制。

下一代测序领域的进步导致产生越来越多的数据，挑战通常是如何合并和协调来自不同OMIC研究的结果，例如基因组，表观基因组和转录组。在这里，我们概述了ChIP-seq和RNA-seq的标准处理流程以及常见的下游分析。我们描述了用于识别目标基因和辅助因子的流行的多组学数据集成方法，并讨论了机器学习技术如何预测转录调节子和基因表达。