Add subworkflow for gridss somatic structural variant calling

[imsarath]

This PR adds bam_tumor_normal_somatic_structural_variant_calling_gridss, a subworkflow that calls somatic structural variants from a tumor/normal BAM pair. It chains the full GRIDSS workflow - optional targeted fragment extraction, per-sample preprocess, joint assembly and call, then gridss_somatic_filter. It emits the unfiltered joint VCF plus the all-somatic and high-confidence somatic call sets.

PR checklist

Closes #4498

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  • For modules:
    • nf-core modules test <MODULE> --profile docker
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  • For subworkflows:
    • nf-core subworkflows test <SUBWORKFLOW> --profile docker
    • nf-core subworkflows test <SUBWORKFLOW> --profile singularity
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[SPPearce]

Can you format the main.nf with each module argument on its own line too please

[SPPearce]

Try this (untested):

Suggested change

	{ assert snapshot(
	path(workflow.out.gridss_vcf[0][1]).vcf.summary,
	workflow.out.all_somatic_vcf.collect { meta, vcf -> [ meta, file(vcf).name ] },
	workflow.out.high_confidence_somatic_vcf.collect { meta, vcf -> [ meta, file(vcf).name ] }
	).match() }
	{ assert snapshot(sanitizeOutput(workflow.out, variantsMD5Keys: ["gridss_vcf", "all_somatic_vcf", "high_confidence_somatic_vcf"]).match() }

[SPPearce]

A couple of comments.
You should be able to calculate is_targeted based on whether ch_target_bed exists?
Should the input tuple be flatter, rather than [tumour_bam, normal_bam] as a nested tuple.
Should the target bed be part of the input tuple? I could see scenarios where you are only interested in certain variants per sample.
What happens if you have more than one tumour sample per normal sample, can the subworkflow deal with that?

[imsarath]

Sorry for the slow reply, and thanks for the detailed review!

1. is_targeted from ch_target_bed - I did try that first, but I couldn't get it to work. The problem is that is_targeted drives an if block that decides which processes get included in the DAG, and that decision happens at workflow-composition time - the channel contents aren't available yet, so an empty vs. non-empty ch_target_bed can't be inspected there. A boolean was the simplest thing that worked. Happy to switch if there's a better pattern.
2. Flatter input tuple - Is there a specific advantage to the flat form? I used the nested one because GRIDSS_ASSEMBLE/CALL take the paired BAMs as a list anyway. Easy to change if flat is the convention.
3. Target BED in the input tuple - Agreed, makes sense. Note that GRIDSS_EXTRACTOVERLAPPINGFRAGMENTS currently takes the BED as a separate channel, so that module would need updating too.
4. Multiple tumours per normal - I hadn't considered it; the focus was one tumour/one normal, so it won't handle that today. We can add it, but it'd need a nested structure for the tumour BAMs/BAIs, which conflicts with point 2.