mirror of
https://github.com/MillironX/taxprofiler.git
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88 lines
3.9 KiB
Text
88 lines
3.9 KiB
Text
//
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// Check input samplesheet and get read channels
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//
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include { UNTAR } from '../../modules/nf-core/untar/main'
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workflow DB_CHECK {
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take:
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dbsheet // file: /path/to/dbsheet.csv
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main:
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ch_versions = Channel.empty()
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ch_dbs_for_untar = Channel.empty()
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ch_final_dbs = Channel.empty()
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// special check to check _between_ rows, for which we must group rows together
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// note: this will run in parallel to within-row validity, but we can assume this will run faster thus will fail first
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Channel.fromPath(dbsheet)
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.splitCsv ( header:true, sep:',' )
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.map {[it.tool, it.db_name] }
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.groupTuple()
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.map {
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tool, db_name ->
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def unique_names = db_name.unique(false)
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if ( unique_names.size() < db_name.size() ) exit 1, "[nf-core/taxprofiler] ERROR: Each database for a tool must have a unique name, duplicated detected. Tool: ${tool}, Database name: ${unique_names}"
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}
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// normal checks for within-row validity, so can be moved to separate functions
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parsed_samplesheet = Channel.fromPath(dbsheet)
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.splitCsv ( header:true, sep:',' )
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.map {
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validate_db_rows(it)
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create_db_channels(it)
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}
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ch_dbs_for_untar = parsed_samplesheet
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.branch {
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untar: it[1].toString().endsWith(".tar.gz")
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skip: true
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}
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//Filter the channel to run untar on DBs of tools actually using
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ch_input_untar = ch_dbs_for_untar.untar.dump()
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.filter { params.run_kraken2 && it[0]['tool'] == 'kraken2' || params.run_centrifuge && it[0]['tool'] == 'centrifuge' || params.run_bracken && it[0]['tool'] == 'bracken' || params.run_kaiju && it[0]['tool'] == 'kaiju' || params.run_krakenuniq && it [0]['tool'] == 'krakenuniq' || params.run_malt && it[0]['tool'] == 'malt' || params.run_metaphlan3 && it[0]['tool'] == 'metaphlan3' }
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UNTAR (ch_input_untar)
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ch_versions = ch_versions.mix(UNTAR.out.versions.first())
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ch_final_dbs = ch_dbs_for_untar.skip.mix( UNTAR.out.untar )
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emit:
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dbs = ch_final_dbs // channel: [ val(meta), [ db ] ]
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versions = ch_versions // channel: [ versions.yml ]
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}
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def validate_db_rows(LinkedHashMap row){
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// check minimum number of columns
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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}"
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// all columns there
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def expected_headers = ['tool', 'db_name', 'db_params', 'db_path']
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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(", ")}"
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// valid tools specified// TIFNISIH LIST
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def expected_tools = [ "bracken", "centrifuge", "diamond", "kaiju", "kraken2", "krakenuniq", "malt", "metaphlan3", "motus" ]
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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}"
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// detect quotes in params
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if ( row.db_params.contains('"') ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
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if ( row.db_params.contains("'") ) exit 1, "[nf-core/taxprofiler] ERROR: Invalid database db_params entry. No quotes allowed. Error in: ${row}"
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}
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def create_db_channels(LinkedHashMap row) {
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def meta = [:]
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meta.tool = row.tool
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meta.db_name = row.db_name
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meta.db_params = row.db_params
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def array = []
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if (!file(row.db_path, type: 'dir').exists()) {
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exit 1, "ERROR: Please check input samplesheet -> database path could not be found!\n${row.db_path}"
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}
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array = [ meta, file(row.db_path) ]
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return array
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}
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