Learner Profiles
This lesson is designed for researchers who want to perform a complete RNA-seq analysis using standard tools and reproducible workflows. Learners are typically graduate students, postdocs, research staff, or faculty working with sequencing-based gene expression data.
Background and Experience
- Familiarity with basic biological concepts including genes, transcripts and expression
- Some exposure to the command line (navigating directories, running commands)
- Little or no prior experience with RNA-seq data analysis
- No requirement for programming experience beyond running provided scripts
Motivations
- Analyze RNA-seq data from their own experiments
- Learn standard QC, alignment, quantification and differential expression workflows
- Understand how to interpret results and generate publication-ready figures
- Gain exposure to reproducible analysis approaches on HPC systems
Needs and Goals
- Know how to evaluate read quality
- Learn how to align reads and generate count matrices
- Perform differential expression analysis using established statistical frameworks
- Understand normalization, modeling assumptions and interpretation of outputs
- Learn best practices for organizing projects and making workflows reproducible