name: seqwish_induce description: Induce a variation graph in GFA format from alignments in PAF format keywords: - induce - paf - gfa - graph - variation graph tools: - seqwish: description: | seqwish implements a lossless conversion from pairwise alignments between sequences to a variation graph encoding the sequences and their alignments. homepage: https://github.com/ekg/seqwish documentation: https://github.com/ekg/seqwish params: - outdir: type: string description: | The pipeline's output directory. By default, the module will output files into `$params.outdir/` - publish_dir_mode: type: string description: | Value for the Nextflow `publishDir` mode parameter. Available: symlink, rellink, link, copy, copyNoFollow, move. - enable_conda: type: boolean description: | Run the module with Conda using the software specified via the `conda` directive - singularity_pull_docker_container: type: boolean description: | Instead of directly downloading Singularity images for use with Singularity, force the workflow to pull and convert Docker containers instead. input: - meta: type: map description: | Groovy Map containing sample information e.g. [ id:'test', single_end:false ] - paf: type: file description: PAF file of alignments pattern: "*.{paf,paf.gz}" - fasta: type: file description: FASTA file used to generate alignments pattern: "*.{fa,fa.gz,fasta,fasta.gz}" output: - meta: type: map description: | Groovy Map containing sample information e.g. [ id:'test', single_end:false ] - gfa: type: file description: Variation graph in GFA1 format pattern: "*.{gfa}" - version: type: file description: File containing software version pattern: "*.{version.txt}" authors: - "@heuermh"