Computational methods for differentially expressed gene analysis from RNA-Seq: an overview

TL;DR

This paper reviews the computational pipelines and methods used for differential gene expression analysis from RNA-Seq data, highlighting their evolution, relationships, and current challenges in the field.

Contribution

It provides an organized overview of the steps, methods, and properties involved in RNA-Seq DEG analysis, including a timeline and interaction network of tools.

Findings

Overview of differential expression analysis pipeline

Timeline of computational methods evolution

Discussion of challenges and gaps in DEG analysis

Abstract

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.