This workflow analyses Oxford Nanopore long-read transcript sequencing data. It uses [`bambu`](https://bioconductor.org/packages/bambu/) to build and quantify transcript models, [`SQANTI3`](https://github.com/ConesaLab/SQANTI3) for transcript classification and QC, can optionally run [`DESeq2`](https://bioconductor.org/packages/DESeq2/) and [`DEXSeq`](https://bioconductor.org/packages/DEXSeq/) for differential analysis, and will run [`modkit`](https://github.com/nanoporetech/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.
wf-transcriptomes overview schematic.
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](https://github.com/GoekeLab/bambu). For `SQANTI3` classification categories, see the [`SQANTI3` isoform classification documentation](https://github.com/ConesaLab/SQANTI3/wiki/).