Streamline Protocol for Bulk-RNA Sequencing: From Data Extraction to Expression Analysis

Next-generation RNA sequencing (RNA-seq) allows researchers to study gene expression across the whole genome. However, its analysis often needs powerful computers and advanced command-line skills, which can be challenging when resources are limited. This protocol provides a simple, start-to-finish RNA-seq data analysis method that is easy to follow, reproducible, and requires minimal local hardware. It uses free tools such as SRA Toolkit, FastQC, Trimmomatic, BWA/HISAT2, Samtools, and Subread, along with Python and R for further analysis using Google Colab. The process includes downloading raw data from NCBI GEO/SRA, checking data quality, trimming adapters and low-quality reads, aligning sequences to reference genomes, converting file formats, counting reads, normalizing to TPM, and creating visualizations such as heatmaps, bar plots, and volcano plots. Differential gene expression is analyzed with pyDESeq2, and functional enrichment is done using g:Profiler. Troubleshooting in RNA-seq generally involves configuring essential tools, resolving path and dependency issues, and ensuring proper handling of paired-end reads during analysis. By running the heavy computational steps on cloud platforms, this workflow makes RNA-seq analysis affordable and accessible to more researchers. © 2026 Wiley Periodicals LLC. Basic Protocol 1: Extracting and processing a high-throughput RNA-seq dataset with the command prompt and Windows Subsystem for Linux Basic Protocol 2: Normalization and visualization of processed RNA-seq dataset with Google Colab and Python 3.