Epigenomics, the study of the human genome and its interactions with proteins and other cellular elements, has become of significant interest in recent years. Such interactions have been shown to regulate essential cellular functions and are associated with multiple complex diseases. Therefore, understanding how these interactions may change across conditions is central in biomedical research. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) is one of several techniques to detect local changes in epigenomic activity (peaks). However, existing methods for differential peak calling are not optimized for the diversity in ChIP-seq signal profiles, are limited to the analysis of two conditions, or cannot classify specific patterns of differential change when multiple patterns exist. To address these limitations, we present a flexible and efficient method for the detection of differential epigenomic activity across multiple conditions. We utilize data from the ENCODE Consortium and show that the presented method, epigraHMM, exhibits superior performance to current tools and it is among the fastest algorithms available, while allowing the classification of combinatorial patterns of differential epigenomic activity and the characterization of chromatin regulatory states.

中文翻译：

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多种条件下表观基因组变化的高效检测和分类


表观基因组学是对人类基因组及其与蛋白质和其他细胞元件相互作用的研究，近年来引起了人们的极大兴趣。这种相互作用已被证明可以调节重要的细胞功能，并与多种复杂疾病相关。因此，了解这些相互作用如何随条件而变化是生物医学研究的核心。染色质免疫沉淀随后进行大规模并行测序 (ChIP-seq) 是检测表观基因组活性（峰）局部变化的几种技术之一。然而，现有的差异峰检出方法并未针对 ChIP-seq 信号谱的多样性进行优化，仅限于分析两种条件，或者当存在多种模式时无法对差异变化的特定模式进行分类。为了解决这些限制，我们提出了一种灵活有效的方法来检测多种条件下的差异表观基因组活性。我们利用来自 ENCODE 联盟的数据，表明所提出的方法 epigraHMM 比当前工具表现出优越的性能，并且它是可用的最快算法之一，同时允许对差异表观基因组活性的组合模式进行分类并表征染色质调控状态。