94 lines
3.5 KiB
Markdown
94 lines
3.5 KiB
Markdown
### Does the workflow support both cDNA and direct RNA?
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Yes. Use `--direct_rna` for direct RNA data. cDNA is the default mode.
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### Do I need both `--ref_genome` and `--ref_annotation` in fixed-annotation mode?
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Yes. `bambu` still uses the genome together with the imported annotation.
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### Does the workflow create one shared transcriptome or one per sample?
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Both. The joint cohort model is the primary result for reporting and DE/DTU, and
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the workflow also emits independent per-sample transcriptomes.
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### Can I run DE/DTU without transcript discovery?
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Yes. Use `--transcriptome_mode fixed_annotation` together with `--de_analysis`.
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### What changed from the previous workflow version?
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The current workflow uses `bambu`, optional `SQANTI3`, `DESeq2`, and `DEXSeq`.
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The main transcriptome result is now one shared `bambu` model built from all
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samples together, with separate per-sample `bambu` outputs published alongside
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it. The most important differences are summarised below.
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#### Why do the results now mention `bambu` and `SQANTI3` instead of StringTie or GffCompare?
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The workflow now uses a different set of transcript analysis tools. It builds
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its transcript models with `bambu`, optionally classifies them with `SQANTI3`,
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and performs DE/DTU from the cohort `bambu` outputs.
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#### Why is `--ref_annotation` now required even in fixed-annotation mode?
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`bambu` still requires the annotation together with the genome in both
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`discover` and `fixed_annotation` modes. Fixed-annotation mode means
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annotation-driven quantification, not annotation-only execution.
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#### Can I still use `--ref_transcriptome`?
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No. `--ref_transcriptome` has been removed from the workflow interface.
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Short old-to-new example:
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```text
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Previous workflow version:
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--transcriptome_source precomputed --ref_transcriptome transcripts.fa
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Current workflow:
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--transcriptome_mode fixed_annotation \
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--ref_genome genome.fa \
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--ref_annotation annotation.gtf
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```
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#### Why do I now get both cohort and per-sample transcriptomes?
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The workflow now treats both as important outputs. The shared cohort model is
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the main transcriptome used for reporting and optional DE/DTU, while the
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per-sample transcriptomes are provided for looking at each sample separately.
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#### Why are DE results under `de_analysis/<contrast>/`?
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The workflow now writes one subdirectory per comparison instead of publishing
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one flat DE result set. This makes the output folder clearer when more than one
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comparison is present.
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Short old-to-new example:
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```text
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Previous workflow version:
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de_analysis/results_dge.tsv
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Current workflow:
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de_analysis/<contrast>/results_dge.tsv
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```
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#### Can I still run fixed-annotation quantification without transcript discovery?
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Yes. Use `--transcriptome_mode fixed_annotation` together with `--ref_genome`,
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`--ref_annotation`, and any optional DE/DTU settings.
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#### What should I expect to differ in report contents and output filenames?
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Expect the report and output folder to emphasise:
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+ optional `pychopper` preprocessing outputs under `ingress_results/<alias>/`
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+ the joint cohort `bambu` transcriptome under `cohort/`
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+ the per-sample `bambu` transcriptomes under `samples/<alias>/`
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+ optional `SQANTI3` results under cohort and per-sample directories
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+ contrast-specific DE/DTU outputs under `de_analysis/<contrast>/`
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If your question is not answered here, please report issues or suggestions on
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the [GitHub issues](https://github.com/epi2me-labs/wf-transcriptomes/issues)
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page or start a discussion on the
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[community](https://community.nanoporetech.com/).
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