Analysis of differential gene expression from RNA-seq data has become a standard for several research areas. The steps for the computational analysis 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 the differential expression analysis pipeline, addressing its steps and the respective objectives, the principal methods available in each step, and their properties, therefore introducing an organized overview to this context. 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. In addition, a timeline of the computational methods for DEG is shown and discussed, and the relationships existing between the most important computational tools are presented by an interaction network. A discussion on the challenges and gaps in DEG analysis is also highlighted in this review. This paper will serve as a tutorial for new entrants into the field and help established users update their analysis pipelines.

从 RNA-seq 数据分析差异基因表达已成为多个研究领域的标准。计算分析的步骤包括许多数据类型和文件格式，以及可以单独应用或作为管道一起应用的各种计算工具。本文回顾了差异表达分析流程，阐述了其步骤和各自的目标、每个步骤中可用的主要方法及其属性，因此对这一背景进行了有组织的概述。本综述旨在考虑计算方法，主要解决 RNA 测序数据 (RNA-seq) 差异表达基因 (DEG) 分析所涉及的方面。此外，还显示并讨论了 DEG 计算方法的时间表，最重要的计算工具之间存在的关系由交互网络呈现。本综述还重点讨论了 DEG 分析中的挑战和差距。本文将作为该领域新手的教程，并帮助已建立的用户更新他们的分析管道。