  The analysis of differential gene expression from RNA-Seq data has become a
standard for several research areas mainly involving bioinformatics. The steps
for the computational analysis of these data include many data types and file
formats, and a wide variety of computational tools that can be applied alone or
together as pipelines. This paper presents a review of differential expression
analysis pipeline, addressing its steps and the respective objectives, the
principal methods available in each step and their properties, bringing an
overview in an organized way in this context. In particular, this review aims
to address mainly the aspects involved in the differentially expressed gene
(DEG) analysis from RNA sequencing data (RNA-Seq), considering the
computational methods and its properties. In addition, a timeline of the
evolution of computational methods for DEG is presented and discussed, as well
as the relationships existing between the main computational tools are
presented by an interaction network. A discussion on the challenges and gaps in
DEG analysis is also highlighted in this review.
