  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 down and up-regulated chromosome regions using RNA-Seq data. The
methodology is based on a recent work developed to detect up-regulated 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 regulation
status, robust with respect to prior specifications and provides tools for a
global or local search of under and overexpressed chromosome regions.
