In short
RNA-seq measures gene expression across the transcriptome. The analysis path is: quality control, alignment or pseudo-alignment, quantification, then differential-expression and interpretation. You can learn it on public datasets and a laptop.
The workflow
- Quality control of reads (FastQC / MultiQC)
- Trimming low-quality bases/adapters
- Alignment (HISAT2/STAR) or pseudo-alignment (Salmon)
- Quantification to a counts matrix
- Differential expression (DESeq2 / edgeR) and enrichment
Tools to learn (free)
| Step | Tools |
|---|---|
| QC | FastQC, MultiQC |
| Align/quantify | HISAT2, STAR, Salmon |
| Differential expression | DESeq2, edgeR (R) |
| Enrichment/visualisation | clusterProfiler, ggplot2 |
Turn it into a project
Reanalyse a public RNA-seq dataset (GEO/SRA) end to end and report the differentially expressed genes and pathways. That single finished analysis is a strong dissertation component. Manna Biotech offers RNA-seq / transcriptomics training and support, for education and research.
Frequently asked questions
What do I need for RNA-seq analysis?+
Basic Linux, some R, and the workflow understanding. Public datasets let you start immediately.
Is RNA-seq a good MSc project?+
Yes - a public-dataset reanalysis is feasible and builds in-demand skills.
Do I need wet-lab RNA-seq to analyse data?+
No - you can analyse public sequencing data without generating it yourself.
Can Manna Biotech help with RNA-seq?+
Yes - scientist-guided transcriptomics training and analysis support are available.