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Merge pull request #258 from nf-core/clone-maps

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refactor: double check maps and validation
This commit is contained in:
Moritz E. Beber 2023-03-11 21:39:49 +01:00 committed by GitHub
commit efa398edab
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5 changed files with 72 additions and 108 deletions
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62
subworkflows/local/db_check.nf
View file

@ -28,22 +28,20 @@ workflow DB_CHECK {
// Normal checks for within-row validity, so can be moved to separate functions
parsed_samplesheet = Channel.fromPath(dbsheet)
.splitCsv ( header:true, sep:',' )
.map {
validate_db_rows(it)
create_db_channels(it)
.map { row ->
validate_db_rows(row)
return [ row.subMap(['tool', 'db_name', 'db_params']), file(row.db_path) ]
}
ch_dbs_for_untar = parsed_samplesheet
.branch {
untar: it[1].toString().endsWith(".tar.gz")
.branch { db_meta, db ->
untar: db.name.endsWith(".tar.gz")
skip: true
}
// Filter the channel to untar only those databases for tools that are selected to be run by the user.
ch_input_untar = ch_dbs_for_untar.untar
.filter {
params["run_${it[0]['tool']}"]
}
.filter { db_meta, db -> params["run_${db_meta.tool}"] }
UNTAR (ch_input_untar)
ch_versions = ch_versions.mix(UNTAR.out.versions.first())
@ -54,41 +52,27 @@ workflow DB_CHECK {
versions = ch_versions // channel: [ versions.yml ]
}
def validate_db_rows(LinkedHashMap row){
def validate_db_rows(LinkedHashMap row) {
// check minimum number of columns
if (row.size() < 4) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database input sheet - malformed row (e.g. missing column). See documentation for more information. Error in: ${row}"
// check minimum number of columns
if (row.size() < 4) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database input sheet - malformed row (e.g. missing column). See documentation for more information. Error in: ${row}"
// all columns there
def expected_headers = ['tool', 'db_name', 'db_params', 'db_path']
if ( !row.keySet().containsAll(expected_headers) ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database input sheet - malformed column names. Please check input TSV. Column names should be: ${expected_keys.join(", ")}"
// all columns there
def expected_headers = ['tool', 'db_name', 'db_params', 'db_path']
if ( !row.keySet().containsAll(expected_headers) ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database input sheet - malformed column names. Please check input TSV. Column names should be: ${expected_headers.join(", ")}"
// valid tools specified
def expected_tools = [ "bracken", "centrifuge", "diamond", "kaiju", "kraken2", "krakenuniq", "malt", "metaphlan3", "motus" ]
if ( !expected_tools.contains(row.tool) ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid tool name. Please see documentation for all supported profilers. Error in: ${row}"
// valid tools specified
def expected_tools = [ "bracken", "centrifuge", "diamond", "kaiju", "kraken2", "krakenuniq", "malt", "metaphlan3", "motus" ]
if ( !expected_tools.contains(row.tool) ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid tool name. Please see documentation for all supported profilers. Error in: ${row}"
// detect quotes in params
if ( row.db_params.contains('"') ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
if ( row.db_params.contains("'") ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
// detect quotes in params
if ( row.db_params.contains('"') ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
if ( row.db_params.contains("'") ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
// check if any form of bracken params, that it must have `;`
if ( row.tool == 'bracken' && row.db_params && !row.db_params.contains(";") ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. Bracken requires a semi-colon if passing parameter. Error in: ${row}"
// check if any form of bracken params, that it must have `;`
if ( row.tool == 'bracken' && row.db_params && !row.db_params.contains(";") ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. Bracken requires a semi-colon if passing parameter. Error in: ${row}"
// ensure that the database directory exists
if (!file(row.db_path, type: 'dir').exists()) exit 1, "ERROR: Please check input samplesheet -> database path could not be found!\n${row.db_path}"
}
def create_db_channels(LinkedHashMap row) {
def meta = [:]
meta.tool = row.tool
meta.db_name = row.db_name
meta.db_params = row.db_params
def array = []
if (!file(row.db_path, type: 'dir').exists()) {
exit 1, "ERROR: Please check input samplesheet -> database path could not be found!\n${row.db_path}"
}
array = [ meta, file(row.db_path) ]
return array
}

47
subworkflows/local/input_check.nf
View file

@ -12,9 +12,9 @@ workflow INPUT_CHECK {
parsed_samplesheet = SAMPLESHEET_CHECK ( samplesheet )
.csv
.splitCsv ( header:true, sep:',' )
.branch {
fasta: it['fasta'] != ''
nanopore: it['instrument_platform'] == 'OXFORD_NANOPORE'
.branch { row ->
fasta: row.fasta != ''
nanopore: row.instrument_platform == 'OXFORD_NANOPORE'
fastq: true
}
@ -37,49 +37,42 @@ workflow INPUT_CHECK {
// Function to get list of [ meta, [ fastq_1, fastq_2 ] ]
def create_fastq_channel(LinkedHashMap row) {
// create meta map
def meta = [:]
meta.id = row.sample
meta.run_accession = row.run_accession
meta.instrument_platform = row.instrument_platform
meta.single_end = row.single_end.toBoolean()
meta.is_fasta = false
def meta = row.subMap(['sample', 'run_accession', 'instrument_platform'])
meta.id = meta.sample
meta.single_end = row.single_end.toBoolean()
meta.is_fasta = false
// add path(s) of the fastq file(s) to the meta map
def fastq_meta = []
if (!file(row.fastq_1).exists()) {
exit 1, "ERROR: Please check input samplesheet -> Read 1 FastQ file does not exist!\n${row.fastq_1}"
}
if (meta.single_end) {
fastq_meta = [ meta, [ file(row.fastq_1) ] ]
return [ meta, [ file(row.fastq_1) ] ]
} else {
if (meta.instrument_platform == 'OXFORD_NANOPORE') {
if (row.fastq_2 != '') {
exit 1, "ERROR: Please check input samplesheet -> For Oxford Nanopore reads Read 2 FastQ should be empty!\n${row.fastq_2}"
}
fastq_meta = [ meta, [ file(row.fastq_1) ] ]
return [ meta, [ file(row.fastq_1) ] ]
} else {
if (!file(row.fastq_2).exists()) {
exit 1, "ERROR: Please check input samplesheet -> Read 2 FastQ file does not exist!\n${row.fastq_2}"
}
fastq_meta = [ meta, [ file(row.fastq_1), file(row.fastq_2) ] ]
return [ meta, [ file(row.fastq_1), file(row.fastq_2) ] ]
}
}
return fastq_meta
}// Function to get list of [ meta, fasta ]
def create_fasta_channel(LinkedHashMap row) {
def meta = [:]
meta.id = row.sample
meta.run_accession = row.run_accession
meta.instrument_platform = row.instrument_platform
meta.single_end = true
meta.is_fasta = true
}
// Function to get list of [ meta, fasta ]
def create_fasta_channel(LinkedHashMap row) {
def meta = row.subMap(['sample', 'run_accession', 'instrument_platform'])
meta.id = meta.sample
meta.single_end = true
meta.is_fasta = true
def array = []
if (!file(row.fasta).exists()) {
exit 1, "ERROR: Please check input samplesheet -> FastA file does not exist!\n${row.fasta}"
}
array = [ meta, [ file(row.fasta) ] ]
return array
return [ meta, [ file(row.fasta) ] ]
}

2
subworkflows/local/longread_hostremoval.nf
View file

@ -46,7 +46,7 @@ workflow LONGREAD_HOSTREMOVAL {
ch_versions = ch_versions.mix( SAMTOOLS_INDEX.out.versions.first() )
bam_bai = MINIMAP2_ALIGN.out.bam
.join(SAMTOOLS_INDEX.out.bai, remainder: true)
.join(SAMTOOLS_INDEX.out.bai)
SAMTOOLS_STATS ( bam_bai, reference )
ch_versions = ch_versions.mix(SAMTOOLS_STATS.out.versions.first())

18
subworkflows/local/longread_preprocessing.nf
View file

@ -20,33 +20,23 @@ workflow LONGREAD_PREPROCESSING {
PORECHOP_PORECHOP ( reads )
ch_processed_reads = PORECHOP_PORECHOP.out.reads
.map {
meta, reads ->
def meta_new = meta.clone()
meta_new['single_end'] = 1
[ meta_new, reads ]
}
.map { meta, reads -> [ meta + [single_end: 1], reads ] }
ch_versions = ch_versions.mix(PORECHOP_PORECHOP.out.versions.first())
ch_multiqc_files = ch_multiqc_files.mix( PORECHOP_PORECHOP.out.log )
} else if ( params.longread_qc_skipadaptertrim && !params.longread_qc_skipqualityfilter) {
ch_processed_reads = FILTLONG ( reads.map{ meta, reads -> [meta, [], reads ]} )
ch_processed_reads = FILTLONG ( reads.map { meta, reads -> [meta, [], reads ] } )
ch_versions = ch_versions.mix(FILTLONG.out.versions.first())
ch_multiqc_files = ch_multiqc_files.mix( FILTLONG.out.log )
} else {
PORECHOP_PORECHOP ( reads )
ch_clipped_reads = PORECHOP_PORECHOP.out.reads
.map {
meta, reads ->
def meta_new = meta.clone()
meta_new['single_end'] = 1
[ meta_new, reads ]
}
.map { meta, reads -> [ meta + [single_end: 1], reads ] }
ch_processed_reads = FILTLONG ( ch_clipped_reads.map{ meta, reads -> [meta, [], reads ]} ).reads
ch_processed_reads = FILTLONG ( ch_clipped_reads.map { meta, reads -> [ meta, [], reads ] } ).reads
ch_versions = ch_versions.mix(PORECHOP_PORECHOP.out.versions.first())
ch_versions = ch_versions.mix(FILTLONG.out.versions.first())

51
subworkflows/local/profiling.nf
View file

@ -35,10 +35,7 @@ workflow PROFILING {
ch_input_for_profiling = reads
.map {
meta, reads ->
def meta_new = meta.clone()
pairtype = meta_new['single_end'] ? '_se' : '_pe'
meta_new['id'] = meta_new['id'] + pairtype
[meta_new, reads]
[meta + [id: "${meta.id}${meta.single_end ? '_se' : '_pe'}"], reads]
}
.combine(databases)
.branch {
@ -68,34 +65,34 @@ workflow PROFILING {
// MALT: We groupTuple to have all samples in one channel for MALT as database
// loading takes a long time, so we only want to run it once per database
ch_input_for_malt = ch_input_for_profiling.malt
.map {
meta, reads, db_meta, db ->
.map {
meta, reads, db_meta, db ->
// Reset entire input meta for MALT to just database name,
// as we don't run run on a per-sample basis due to huge datbaases
// so all samples are in one run and so sample-specific metadata
// unnecessary. Set as database name to prevent `null` job ID and prefix.
def temp_meta = [ id: meta['db_name'] ]
// Reset entire input meta for MALT to just database name,
// as we don't run run on a per-sample basis due to huge datbaases
// so all samples are in one run and so sample-specific metadata
// unnecessary. Set as database name to prevent `null` job ID and prefix.
def temp_meta = [ id: meta['db_name'] ]
// Extend database parameters to specify whether to save alignments or not
def new_db_meta = db_meta.clone()
def sam_format = params.malt_save_reads ? ' --alignments ./ -za false' : ""
new_db_meta['db_params'] = db_meta['db_params'] + sam_format
// Extend database parameters to specify whether to save alignments or not
def new_db_meta = db_meta.clone()
def sam_format = params.malt_save_reads ? ' --alignments ./ -za false' : ""
new_db_meta['db_params'] = db_meta['db_params'] + sam_format
// Combine reduced sample metadata with updated database parameters metadata,
// make sure id is db_name for publishing purposes.
def new_meta = temp_meta + new_db_meta
new_meta['id'] = new_meta['db_name']
// Combine reduced sample metadata with updated database parameters metadata,
// make sure id is db_name for publishing purposes.
def new_meta = temp_meta + new_db_meta
new_meta['id'] = new_meta['db_name']
[ new_meta, reads, db ]
[ new_meta, reads, db ]
}
.groupTuple(by: [0,2])
.multiMap {
it ->
reads: [ it[0], it[1].flatten() ]
db: it[2]
}
}
.groupTuple(by: [0,2])
.multiMap {
meta, reads, db ->
reads: [ meta, reads.flatten() ]
db: db
}
MALT_RUN ( ch_input_for_malt.reads, ch_input_for_malt.db )