A Guide to Basic RNA Sequencing Data Processing and Transcriptomic Analysis

Abstract

RNA sequencing (RNA-Seq) has transformed transcriptomic research, enabling researchers to perform large-scale inspection of mRNA levels in living cells. With the growing applicability of this technique to many scientific investigations, the analysis of next-generation sequencing (NGS) data becomes an important yet challenging task, especially for researchers without a bioinformatics background. This protocol offers a beginner-friendly step-by-step guide to analyze NGS data (starting from raw .fastq files), providing the required codes with an explanation of the different steps and software used. We outline a computational workflow that includes quality control, trimming of reads, read alignment to the genome, and gene quantification, ultimately enabling researchers to identify differentially expressed genes and gain insights on mRNA levels. Multiple approaches to visualize this data using statistical and graphical tools in R are also described, allowing the generation of heatmaps and volcano plots to represent genes and gene sets of interest. Key features • Provides a beginner-friendly protocol for RNA-Seq analysis to obtain insights into gene expression. • Pipeline starts with raw .fastq files and involves analysis in command line/terminal and R (via RStudio). • Yields a variety of output files that represent mRNA levels amongst different samples. Output files include count files, heatmaps, ordered lists of DEGs, and volcano plots.