1. RNA-seq Overview
Context, audience, and workflow summary.
Start here if you want the high-level picture before diving into the video-specific material.
Active Tutorial
RNA-seq Analysis Tutorial
Learn RNA-seq from concept to hands-on analysis with step-by-step commands, workflow guidance, and practical troubleshooting notes.
Part 1 Available
Part 2 Available
Part 3 Coming Soon
2. Part 1
Introduction and workflow orientation.
Conceptual introduction to RNA-seq, experimental logic, and the computational pipeline structure.
3. Part 2
Installation, FASTQ to quantification.
The main hands-on page with workflow notes from environment setup through `featureCounts`.
4. Commands and Resources
Copyable blocks for each stage.
Includes the original STAR workflow for reference and the recommended HISAT2-based workflow for the current tutorial run.
5. Troubleshooting Notes
Short issue and fix notes.
Use this page for common practical problems including path issues, index mismatches, and platform-specific setup problems.
6. Part 3
Normalization, DESeq2, and interpretation.
Reserved for the downstream analysis stage, including normalization, differential expression, and visualization.
RNA-seq Overview
RNA-seq is a sequencing-based method used to measure transcript abundance and explore gene expression patterns across biological conditions. In practical work, the analysis starts with raw sequence reads and moves through quality control, preprocessing, alignment or pseudoalignment, quantification, and finally downstream statistical interpretation.
This tutorial series is designed for learners who want both conceptual understanding and a runnable command-line workflow. The first two parts focus on the upstream analysis path from FASTQ files to gene-level counts.
Expected audience
- Students entering transcriptomics for the first time
- Researchers who want a practical RNA-seq workflow map
- Learners following the Bioinformatics with BB video series
- Users who need a concise reference for setup and commands
What you will learn
- How the RNA-seq workflow is structured from raw reads to counts
- Which tools are used at each stage and why
- How to organize commands for reproducible terminal work
- Where tool switching may be necessary in real local environments
Workflow summary
- Raw data and QC
- Read trimming and cleanup
- Reference genome and annotation preparation
- Alignment and BAM processing
- Gene-level quantification with `featureCounts`
Tools covered
- Conda
- FastQC
- Cutadapt
- HISAT2
- SAMtools
- Subread `featureCounts`
- STAR as reference workflow context