RNA-Seq examines global gene expression to provide insights into cellular 
processes, and it can be particularly informative when comparing contrasting 
physiological states or strains. Although relatively routine in many 
laboratories, there are many steps involved in performing a transcriptomics 
experiment to ensure representative and high-quality results are generated for 
analysis. In this chapter, we present the application of widely used 
bioinformatic methodologies to assess, trim, and filter RNA-seq reads for 
quality using FastQC and Trim Galore, respectively. High-quality reads are 
mapped using Bowtie2 and differentially expressed genes across different groups 
were estimated using the DEseq2 R-Bioconductor package. In addition, we describe 
the various steps to perform the sample-wise data quality assessment by 
generating exploratory plots through the DESeq2 package. Simple steps to 
calculate the significant differentially expressed genes, up- and down-regulated 
genes, and exporting the data and images are also included. A Venn diagram is a 
useful method to compare the differentially expressed genes across various 
comparisons and steps to generate the Venn diagram from DESeq2 results are 
provided. Finally, the output from DESeq2 is compared to published results from 
EdgeR. The Clostridium autoethanogenum data are published and publicly 
available.