A Practical Guide to RNA-Sequencing Analysis: From Raw Reads to Biological Insights

Audience:

Early-career bioinformaticians, researchers, and graduate students

Goal:

Equip learners with conceptual and practical understanding of the RNA-seq
workflow – from experimental design to interpretation.

Course Description

• This session provides a practical and structured overview of RNA sequencing (RNA-seq) analysis – one of the most widely used approaches for studying gene expression.
• You’ll explore the complete computational workflow, from raw sequencing reads to biological interpretation, learning how to perform quality control, alignment, differential expression, and pathway analysis.
• By the end, you’ll have a clear understanding of how to move from FASTQ files to functional insights, supported by examples, visuals, and standard tools used in bioinformatics research.

Learning Outcomes:

By the end of this session, learners will be able to:

• Understand the concept and applications of RNA-seq in biomedical research.
• Describe the computational workflow, including QC, alignment/quantification, and differential expression.
• Interpret key visual outputs such as volcano plots, heatmaps, and PCA plots.
• Recognize best practices and pitfalls in RNA-seq data analysis.
• Connect gene-level changes to biological pathways for meaningful insight.

Course Structure

Module 1: Introduction to RNA-seq

• What is RNA-seq?
• Key applications: disease research, drug targets, biomarker discovery, personalized medicine.
• Example: cancer vs. normal expression profiling

Module 2: RNA-seq Workflow Overview

• Experimental design: replicates, controls, metadata importance.
• Sample preparation: RNA extraction, library construction, sequencing.
• Output format: structure of FASTQ files and read qualities.

Module 3: Computational Pipeline

Step 1: Quality Control

• Tools: FastQC, MultiQC.
• Key metrics: base quality, adapter contamination.
• Why QC matters (bad reads = false results).
• FastQC report

Step 2: Alignment or Quantification

Traditional alignment: HISAT2, STAR.

• Pseudoalignment: Salmon, Kallisto.
• Mapping logic – how reads align to a reference.

Step 3: Count Matrix

Constructing a raw count table.

• Example matrix: “Control” vs “Treatment” expression counts.

Step 4: Differential Expression Analysis

• Tools: DESeq2, edgeR.
• Fold-change and adjusted p-values explained.
• Visual outputs:
  • Volcano plot (significant vs non-significant genes)
  • Heatmap (expression clustering)
  • PCA plot (sample separation)
•  

Module 4: Biological Interpretation

• Functional enrichment: GO, KEGG, Reactome.
• Interpreting biological meaning from DEGs.
• Case study: upregulated pathways in a cancer dataset.
• Plots

Module 5: Challenges & Best Practices

• Technical considerations: batch effects, normalization, noise.
• Importance of replicates and metadata completeness.
• Reproducibility and workflow documentation.

Module 6: Recap and Resources

• Summary pipeline: FASTQ → QC → Alignment/Quantification → Counts → DE → Pathways.
• Public datasets: GEO, TCGA, ENA.
• Online tutorials and reference materials.

Key Takeaways

• RNA-seq transforms raw sequencing data into meaningful biological knowledge.
• Understanding QC, normalization, and statistical rigor ensures reliable results.
• Visualizations (volcano, heatmap, PCA) make expression data interpretable.
• Bioinformatics isn’t just computation – it’s a path from data to discovery.