Super admin . 29th Oct, 2025 11:25 AM
In the rapidly evolving landscape of genomics, RNA-Seq data analysis has become an indispensable tool for understanding gene regulation and cellular function. Whether you're investigating cancer biomarkers, studying stress responses, or exploring developmental pathways, RNA-Seq provides a powerful way to decode transcriptional activity across biological systems.
This 15-day crash course is designed for students, researchers, and professionals who want to master differential gene expression (DGE) analysis — the cornerstone of modern transcriptomics. Through a blend of theory and hands-on sessions, you’ll gain both the conceptual clarity and computational skills needed to analyze next-generation sequencing (NGS) data confidently.
What You’ll Learn
Over two intensive weeks, participants will explore the complete workflow:
Day 1–3: Understanding RNA-Seq technology, data formats (FASTQ, GTF, FPKM/TPM), and experimental design.
Day 4–6: Quality control and preprocessing using FastQC and Trimmomatic.
Day 7–9: Alignment and quantification with HISAT2, STAR, and featureCounts.
Day 10–12: Performing DGE analysis using DESeq2 or edgeR in R to identify up- and down-regulated genes.
Day 13–15: Functional interpretation using Ensembl, DAVID, and Reactome, along with advanced data visualization using ggplot2.
Why This Course Matters
Employers in biotech and academia increasingly value scientists who can interpret large-scale transcriptomic data. Mastering RNA-Seq analysis bridges the gap between wet-lab biology and computational bioinformatics, giving you a competitive edge in research and industry.
By the end of this course, you’ll not only understand how to process and analyze RNA-Seq datasets but also be able to present your findings with scientific confidence — ready to contribute to cutting-edge genomics and precision medicine research.
In just 15 days, you’ll transform your curiosity into capability — and take a decisive step toward becoming a job-ready bioinformatics professional.
