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

Abdullah Al Mohit1, Niher Ranjan Das2, Arushi Jain1

  • 1Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology, South Asian University, New Delhi, India.

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|February 4, 2026
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Summary
This summary is machine-generated.

This protocol simplifies RNA sequencing (RNA-seq) data analysis using free tools and cloud computing. It makes gene expression studies accessible to researchers with limited hardware and technical expertise.

Keywords:
RNA sequencing (RNA‐seq)cloud‐based bioinformaticsdifferential expressiongene expression analysis

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation RNA sequencing (RNA-seq) is a powerful tool for genome-wide gene expression analysis.
  • Traditional RNA-seq analysis requires significant computational resources and advanced bioinformatics skills.
  • Limited access to hardware and expertise hinders widespread adoption of RNA-seq.

Purpose of the Study:

  • To present a simplified, start-to-finish protocol for RNA-seq data analysis.
  • To enable researchers with limited resources to perform comprehensive gene expression studies.
  • To make RNA-seq analysis reproducible and accessible using free tools and cloud platforms.

Main Methods:

  • Utilizes free bioinformatics tools (SRA Toolkit, FastQC, Trimmomatic, BWA/HISAT2, Samtools, Subread) and cloud-based platforms (Google Colab).
  • Covers the entire workflow: data download, quality control, read trimming, alignment, read counting, normalization (TPM), and visualization.
  • Integrates Python and R for differential gene expression analysis (pyDESeq2) and functional enrichment (g:Profiler).

Main Results:

  • A user-friendly, reproducible protocol for RNA-seq data analysis is established.
  • The workflow successfully processes raw sequencing data into normalized expression values and visualizations.
  • Differential gene expression and functional enrichment analyses are performed efficiently.

Conclusions:

  • This protocol significantly lowers the barrier to entry for RNA-seq data analysis.
  • It empowers researchers with limited computational resources to conduct advanced gene expression studies.
  • The method enhances the accessibility and affordability of RNA-seq analysis in research settings.