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Merge branch 'dev' into allow-targz-motus

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James A. Fellows Yates 2023-01-24 15:20:29 +01:00 committed by GitHub
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4 changed files with 7 additions and 3 deletions
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2
conf/modules.config
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@ -230,7 +230,7 @@ process {
path: { "${params.outdir}/minimap2/index" },
mode: params.publish_dir_mode,
enabled: params.save_hostremoval_index,
pattern: 'minimap2'
pattern: '*.mmi'
]
}

2
docs/usage.md
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@ -64,6 +64,8 @@ ERR3201952,ERR3201952,OXFORD_NANOPORE,/<path>/<to>/fastq/ERR3201952.fastq.gz,,
> ⚠️ Input FASTQ and FASTA files _must_ be gzipped
> ⚠️ While one can include both short-read and long-read data in one run, we recommend that you split these across _two_ pipeline runs and database sheets (see below). This will allow classification optimisation for each data type, and make MultiQC run-reports more readable (due to run statistics having vary large number differences).
| Column | Description |
| --------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `sample` | Unique sample name [required]. |

1
subworkflows/local/db_check.nf
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@ -44,6 +44,7 @@ workflow DB_CHECK {
.filter {
params["run_${it[0]['tool']}"]
}
UNTAR (ch_input_untar)
ch_versions = ch_versions.mix(UNTAR.out.versions.first())
ch_final_dbs = ch_dbs_for_untar.skip.mix( UNTAR.out.untar )

5
subworkflows/local/profiling.nf
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@ -203,6 +203,8 @@ workflow PROFILING {
.filter{
if (it[0].is_fasta) log.warn "[nf-core/taxprofiler] MetaPhlAn3 currently does not accept FASTA files as input. Skipping MetaPhlAn3 for sample ${it[0].id}."
!it[0].is_fasta
if (it[0].instrument_platform == 'OXFORD_NANOPORE') log.warn "[nf-core/taxprofiler] MetaPhlAn3 has not been evaluated for Nanopore data. Skipping MetaPhlAn3 for sample ${it[0].id}."
!it[0].instrument_platform == 'OXFORD_NANOPORE'
}
.multiMap {
it ->
@ -277,14 +279,13 @@ workflow PROFILING {
[[id: db_meta.db_name, single_end: meta.single_end], reads, db_meta, db]
}
.groupTuple(by: [0,2,3])
.dump(tag: "krakenuniq_premultimap")
.multiMap {
single_meta, reads, db_meta, db ->
reads: [ single_meta + db_meta, reads.flatten() ]
db: db
}
// Hardcode to _always_ produce the report file (which is our basic otput, and goes into)
KRAKENUNIQ_PRELOADEDKRAKENUNIQ ( ch_input_for_krakenuniq.reads.dump(tag: "krakenuniq_input"), ch_input_for_krakenuniq.db.dump(tag: "krakenuniq_db"), params.krakenuniq_ram_chunk_size, params.krakenuniq_save_reads, true, params.krakenuniq_save_readclassifications )
KRAKENUNIQ_PRELOADEDKRAKENUNIQ ( ch_input_for_krakenuniq.reads, ch_input_for_krakenuniq.db, params.krakenuniq_ram_chunk_size, params.krakenuniq_save_reads, true, params.krakenuniq_save_readclassifications )
ch_multiqc_files = ch_multiqc_files.mix( KRAKENUNIQ_PRELOADEDKRAKENUNIQ.out.report )
ch_versions = ch_versions.mix( KRAKENUNIQ_PRELOADEDKRAKENUNIQ.out.versions.first() )
ch_raw_classifications = ch_raw_classifications.mix( KRAKENUNIQ_PRELOADEDKRAKENUNIQ.out.classified_assignment )