Lab 4: Alignment & Quantification
Choose Your Path
Lab 4 has two options. Complete either Lab 4a or Lab 4b (not both). Both paths produce the same output files needed for Lab 5.
Which approach should I use?
Lab 4a
Genome Alignment (STAR + Salmon)
Choose this if you need:
- BAM files for visualization in IGV
- Read-level mapping information
- Splice junction analysis
- Integration with variant calling
- Full understanding of the traditional RNA-seq workflow
Requirements: ~4 GB RAM (chr11 only), longer processing time
STAR
Salmon
samtools
IGV
Start Lab 4a →
Lab 4b
Pseudo-alignment (Salmon Only)
Choose this if you:
- Only need gene expression counts
- Want faster processing
- Have limited computational resources
- Don't need BAM files
- Want to learn modern lightweight quantification
Requirements: ~2 GB RAM, 10-100x faster than STAR
Salmon
tximport
R
Start Lab 4b →
Comparison Table
| Feature | Lab 4a (STAR + Salmon) | Lab 4b (Salmon Only) |
|---|---|---|
| Processing speed | Slower (alignment step) | 10-100x faster |
| Memory required | ~4 GB (chr11 only) | ~2 GB |
| BAM files | Yes | No |
| IGV visualization | Yes | No |
| Gene counts for DESeq2 | Yes | Yes |
| Splice junction info | Yes | No |
| Best for | Full analysis, visualization | Quick DE analysis |
Recommendation
For this workshop:
If you're new to RNA-seq analysis, we recommend Lab 4a to learn the full workflow including genome alignment and BAM file inspection. The experience of visualizing reads in IGV is valuable for understanding how RNA-seq works.
If you're short on time or already familiar with alignment concepts, Lab 4b will get you to the differential expression analysis faster.