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import java.util.regex.Pattern
include {post_csv_to_json_api as post_species_csv_to_json_api; post_csv_to_json_api as post_abr_csv_to_json_api;
score as score_species; score as score_abr; put_file as upload_fast5; put_file_base as upload_fsummary} from './lib.nf'
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/*
Map reads against local species database
*/
process map_species {
// Only one fork so we _really_ don't run out of memory
maxForks 1
container 'quay.io/biocontainers/minimap2:2.26--he4a0461_1'
input:
path species_fasta
tuple val(prefix), path(reads_fastq)
output:
tuple path("${reads_fastq}_species_raw.paf"), val(prefix), emit: paf
script:
"""
minimap2 --secondary=no -x map-ont "${species_fasta}" "${reads_fastq}" > "${reads_fastq}_species_raw.paf"
"""
}
/*
Take minimap PAF result from map_species and discard matches with low quality
*/
process discard_low_quality_species_scores {
input:
tuple path(species_raw), val(prefix)
output:
tuple path ("${species_raw}_qc.csv"), val(prefix), emit: species_detected_qc
shell:
'''
# discard matches with a quality(PAF column 12) < 60 or length(11) less than 200,
# output the first 12 columns (PAF format) and add a column with mapping_percentage(10/11),
# reduce all spaces to a single one and convert them into ','
# finally sort by species_accession(col 6) and store as csv
awk '$12 > 59 && $12 < 255 && $11 > 199 {print $1,$2,$3,$4,$5,$6,$7,$8,$9,$10,$11,$12,$10/$11}' !{species_raw}\
| sort | sed "s/^\\s\\+//g" | tr ' ' ',' | sort --key=6 --field-separator=, > !{species_raw}_qc.csv
'''
}
/*
Map matched species sequence IDs to species names so they can be included in the result
*/
process lookup_species_names {
input:
tuple path(species_detected_qc), val(prefix)
path species_fasta
output:
path 'species_names.csv', emit: species_names
script:
"""
# TODO: this can probably be done more efficiently.. and also once and be permanently stored until the database changes..
grep '^>' ${species_fasta} > species_meta.txt
looked_up=''
while read line; do
id="\$(echo \${line} | awk -F, '{print \$6}')"
if [[ ! \${looked_up[*]} =~ \${id} ]];then
looked_up=(\${looked_up[@]} \${id})
grep \${id} species_meta.txt
fi;
done < "${species_detected_qc}" | uniq | tr -d '>' | sed "s/,/ -/g" | sed "s/^\\([A-Z,0-9,_.]*\\)\\s/\\1,/g" | sort > species_names.csv
"""
}
/*
Include species names in the table of detected species
*/
process join_species_names {
input:
path species_names
tuple path(species_detected_qc), val(prefix)
output:
tuple path("${species_detected_qc}_with_names.csv"), val(prefix), emit: species_detected
publishDir "${params.publishdir}/${prefix}/"
script:
"""
output_name="${species_detected_qc}_with_names.csv"
echo 'species_accession,query_id,query_length,query_start,query_end,strand,target_length,target_start,'\
'target_end,residue_matches,alignment_block_length,mapping_quality,match_percentage,species_name' > "\${output_name}"
join -1 6 -2 1 -t , "${species_detected_qc}" "${species_names}" >> "\${output_name}"
"""
}
/*
Map reads agains CARD database for antibiotic resistancies
*/
process detect_abr {
container 'quay.io/biocontainers/minimap2:2.26--he4a0461_1'
input:
path card_db
tuple val(prefix), path(reads_fastq)
output:
tuple path("${reads_fastq}_abr_raw.paf"), val(prefix), emit: abr_raw
script:
"""
minimap2 --secondary=no -x map-ont "${card_db}" "${reads_fastq}" > "${reads_fastq}_abr_raw.paf"
"""
}
/*
Take minimap2 PAF result from detect_abr and discard matches with low quality
*/
process discard_low_quality_abr_scores {
input:
tuple path(abr_raw_paf), val(prefix)
output:
tuple path("${abr_raw_paf}_qc.csv"), val(prefix), emit: abr_detected_raw
publishDir "${params.publishdir}/${prefix}"
script:
"""
# discard matches with a quality(PAF column 12) < 60 or length(11) less than 200,
# output the first 12 columns (PAF format) and add a column with mapping_percentage(10/11),
# reduce all spaces to a single one and convert them into ','
# finally sort by species_accession(col 6) and store as csv
echo 'query_id,query_length,query_start,query_end,strand,abr_card_id,target_length,target_start,target_end,'\
'residue_matches,alignment_block_length,mapping_quality,match_percentage' > "${abr_raw_paf}_qc.csv"
awk '\$12 > 59 && \$12 < 255 && \$11 > 199 {print \$1,\$2,\$3,\$4,\$5,\$6,\$7,\$8,\$9,\$10,\$11,\$12,\$10/\$11}' "${abr_raw_paf}"|\
sort | sed "s/^\\s\\+//g" | tr ' ' ',' >> "${abr_raw_paf}_qc.csv"
"""
}
// Create an unique project id from the name of the project directory, and the flowcell name and runIDs extracted from the filename of the given file
def get_project_id_from_fastx(somefile) {
// the project dir is the parent of the directory containing the fastx files
x = somefile.collect()*.toString()
proj_dir = x[x.findIndexOf{ it in params.minknowsubdirs} - 2]
// split the filename into parts, remove the second part (which is always 'fail' or 'pass') and the last part (which contains the file number and extension)
// afterwards join the remaining parts together and then with the name of the project dir to obtain the full project id
filename_parts = somefile.getBaseName().split(params.namedelimiter)
return [proj_dir].plus( filename_parts.minus(filename_parts[1, -1]).join(params.idjoiner) ).join(params.idjoiner)
// Get the same project id from the filename of a final_summary which minkow creates when the run is finished.
// BUT: If the sample uses barcoding, the barcode-id will be part of the id extracted from the fastx files (so they can be separated in the frontend),
// but not part of this id, because there is only one final_summary for the whole run. This has to be handled by the cloud-workflow.
def get_project_id_from_summary(somesummary) {
[somesummary.getParent()[-2]].plus(somesummary.getBaseName().split(params.namedelimiter)[2,3,4]).join(params.idjoiner)
def makeregexpattern(somedir, somepattern) {
// as a function because nextflow otherwise complains about an already used variable
return Pattern.compile( somedir + somepattern )
}
if (params.minknowdir.endsWith(File.separator)) {
minknowdir = params.minknowdir
} else {
minknowdir = params.minknowdir + File.separator
Dorian Lehmenkühler
committed
}
Dorian Lehmenkühler
committed
while (! file(minknowdir).exists()){
println "MinKnow output dir '${minknowdir}' does not exist yet. Checking again in 10 seconds. (Press Ctrl+C three times to cancel)"
sleep(10000)
println "Starting workflow with following parameters:"
println "Reads directory to watch: ${minknowdir + params.fastq_pattern_regex}"
println "FAST5_pass directory to watch: ${minknowdir + params.fast5_pattern_glob}"
println "Spezies database: $params.species"
println "CARD database: $params.card"
println "DB API server: $params.db_api_server"
println "Cloud API server: $params.cloud_api_server"
// Fasta file with genomes of all species relevant to us
species = channel.value(params.species)
// Creates a channel which emits every file newly cerated in the directory params.minknowdir/fastq_pass/
// The channel emits a tuple (with the .map operator) of the file and an unique project id
reads_glob = channel.watchPath(minknowdir+params.fastq_pattern_glob).map{ it -> [ get_project_id_from_fastx(it) , it]}
checkreadsPattern = makeregexpattern(minknowdir, params.fastq_pattern_regex)
reads = reads_glob.filter({ prj, read -> checkreadsPattern.matcher(read.toString()).matches()})
if( !(params.upload_fast5 in params.false_values) ) {
// Additionally watch the created FAST5 files to forward them to the cloud workflow and wait for the final summary
fast5files = channel.watchPath(minknowdir+params.fast5_pattern_glob).map{ it -> [it, get_project_id_from_fastx(it)]}
fsummary = channel.watchPath(minknowdir+params.summary_pattern_glob).map{ it -> [it, get_project_id_from_summary(it)]}
checkfsumPattern = makeregexpattern(minknowdir, params.summary_pattern_regex)
upload_fsummary(fsummary.filter({ sum, prj -> checkfsumPattern.matcher(sum.toString()).matches()}), params.cloud_api_server)
upload_fast5(fast5files,params.cloud_api_server, params.cloud_api_fast5_endpoint)
}
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/*
Species part
*/
// Map reads to species and discard mappings with low quality
map_species(species, reads) | discard_low_quality_species_scores
// Match the detected species ids with their common names
lookup_species_names(discard_low_quality_species_scores.out.species_detected_qc, species)
join_species_names(lookup_species_names.out.species_names, discard_low_quality_species_scores.out.species_detected_qc)
score_species(join_species_names.out.species_detected)
post_species_csv_to_json_api(score_species.out.scored_paflike, params.db_api_server, params.db_api_species_endpoint)
/*
ABR part
*/
// CARD database of ABR
card = channel.value(params.card)
// Detect antibiotic resistances
detect_abr(card, reads) | discard_low_quality_abr_scores
score_abr(discard_low_quality_abr_scores.out.abr_detected_raw)
post_abr_csv_to_json_api(score_abr.out.scored_paflike, params.db_api_server, params.db_api_abr_endpoint)
}
// vim: shiftwidth=4 smarttab expandtab autoindent