wf-transcriptomes-v202/docs/02_introduction.md

This workflow analyses Oxford Nanopore long-read transcript sequencing data. It uses bambu to build and quantify transcript models, SQANTI3 for transcript classification and QC, can optionally run DESeq2 and DEXSeq for differential analysis, and will run modkit for base modification pileups on aligned reads if relevant tags are present.

The workflow supports:

• Transcript identification from either cDNA or direct RNA reads
• Transcript discovery guided by a supplied genome and annotation
• Quantification against a supplied reference annotation
• Transcript classification and QC with SQANTI3
• Differential gene expression with DESeq2
• Differential transcript usage with DEXSeq
• Base modification pileups with modkit

The main transcriptome result is a shared bambu model built from all samples together. The workflow also produces separate per-sample transcriptomes, so each sample has its own GTF, FASTA, count tables, and SQANTI3 summary alongside the shared results.

Schematic depicting wf-transcriptomes workflow.

For users familiar with earlier transcriptome workflows, the main change is that transcript discovery, quantification, and optional differential analysis now use the shared bambu outputs rather than the older StringTie/GffCompare/Salmon-based approach. The rest of this README explains the current workflow in plain terms, while the FAQ and Troubleshooting sections call out the main differences from the previous workflow version. For bambu implementation details, see the bambu GitHub repository. For SQANTI3 classification categories, see the SQANTI3 isoform classification documentation.