Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this context, we define a hidden Markov process that underlies all individuals jointly in order to detect and to classify genomics regions in different states (typically, deletion, normal or amplification). Structural variations from different individuals may be dependent. It is the case in agronomy where varietal selection program exists and species share a common phylogenetic past. We propose to take into account these dependencies inthe HMM model. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM), implemented in the CHMM R package. A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.

中文翻译：

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应用于拷贝数变异联合检测的耦合隐马尔可夫模型的变分推理

隐马尔可夫模型为检测基因组学中的拷贝数变异 (CNV) 提供了一个自然的统计框架。在这种情况下，我们定义了一个隐藏马尔可夫过程，该过程共同构成所有个体的基础，以便检测和分类处于不同状态（通常是删除、正常或扩增）的基因组区域。来自不同个体的结构变化可能是依赖的。在农学中，存在品种选择程序并且物种具有共同的系统发育历史。我们建议在 HMM 模型中考虑这些依赖关系。在处理大量序列时，最大似然推断（经典地使用 EM 算法执行）变得难以处理。因此，我们提出了一种基于变分方法 (VEM) 的近似推理算法，该算法在 CHMM R 包中实现。