The number of studies dealing with RNA-Seq data analysis has experienced a fast increase in the past years making this type of gene expression a strong competitor to the DNA microarrays. This paper proposes a Bayesian model to detect low and highly-expressed chromosome regions using RNA-Seq data. The methodology is based on a recent work designed to detect highly-expressed (overexpressed) regions in the context of microarray data. A hidden Markov model is developed by considering a mixture of Gaussian distributions with ordered means in a way that first and last mixture components are supposed to accommodate the under and overexpressed genes, respectively. The model is flexible enough to efficiently deal with the highly irregular spaced configuration of the data by assuming a hierarchical Markov dependence structure. The analysis of four cancer data sets (breast, lung, ovarian and uterus) is presented. Results indicate that the proposed model is selective in determining the expression status, robust with respect to prior specifications and provides tools for a global or local search of under and overexpressed chromosome regions.

在过去的几年中，涉及RNA-Seq数据分析的研究数量迅速增加，这使得这种类型的基因表达成为DNA微阵列的有力竞争者。本文提出了一种使用RNA-Seq数据检测低表达和高表达染色体区域的贝叶斯模型。该方法基于最近的一项工作，该工作旨在检测微阵列数据中高度表达（过度表达）的区域。通过考虑高斯分布与有序均值的混合来开发隐马尔可夫模型，以使第一个和最后一个混合成分分别适应欠表达和过表达的基因。该模型具有足够的灵活性，可以通过假定分层的马尔可夫依赖性结构来有效处理高度不规则的数据空间配置。介绍了四个癌症数据集（乳腺癌，肺癌，卵巢癌和子宫癌）的分析结果。结果表明，所提出的模型在确定表达状态方面具有选择性，相对于先前的规范具有鲁棒性，并且为全局或局部搜索欠表达和过度表达的染色体区域提供了工具。