Noncoding DNA regions have central roles in human biology, evolution, and disease. ChromHMM helps to annotate the noncoding genome using epigenomic information across one or multiple cell types. It combines multiple genome-wide epigenomic maps, and uses combinatorial and spatial mark patterns to infer a complete annotation for each cell type. ChromHMM learns chromatin-state signatures using a multivariate hidden Markov model (HMM) that explicitly models the combinatorial presence or absence of each mark. ChromHMM uses these signatures to generate a genome-wide annotation for each cell type by calculating the most probable state for each genomic segment. ChromHMM provides an automated enrichment analysis of the resulting annotations to facilitate the functional interpretations of each chromatin state. ChromHMM is distinguished by its modeling emphasis on combinations of marks, its tight integration with downstream functional enrichment analyses, its speed, and its ease of use. Chromatin states are learned, annotations are produced, and enrichments are computed within 1 d.

中文翻译：

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利用ChromHMM进行染色质状态发现和基因组注释。

非编码DNA区域在人类生物学，进化和疾病中起着核心作用。ChromHMM使用一种或多种细胞类型的表观基因组信息帮助注释非编码基因组。它结合了多个全基因组表观基因组图谱，并使用组合和空间标记模式来推断每种细胞类型的完整注释。ChromHMM使用多元隐马尔可夫模型（HMM）学习染色质状态签名，该模型显式地对每个标记的组合存在与否进行建模。ChromHMM使用这些签名通过计算每个基因组片段的最可能状态，为每种细胞类型生成全基因组注释。ChromHMM提供了对所得注释的自动富集分析，以促进每种染色质状态的功能性解释。ChromHMM的特色在于其对标记组合的建模重点，与下游功能富集分析的紧密集成，速度和易用性。了解染色质状态，产生注释，并在1 d内计算富集。