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elixir_proteomics_QC.nf
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#!/usr/bin/env nextflow
/*
* Copyright (c) 2018, Centre for Genomic Regulation (CRG) and the authors.
*
*/
/*
* Elixir Proteomics QC pipeline by Bioinformatics Core & Proteomics Core @ CRG
*
* @authors
* Luca Cozzuto <[email protected]>
* Toni Hermoso <[email protected]>
* Roger Olivella <[email protected]>
*
*/
params.help = false
params.resume = false
/*
* PIPELINE
*/
version = 0.1
log.info """elixir_proteomics_QC - N F ~ version ${version}
========================================
┌─┐┬ ┬─┐ ┬┬┬─┐ ┌─┐┬─┐┌─┐┌┬┐┌─┐┌─┐┌┬┐┬┌─┐┌─┐ ╔═╗ ╔═╗
├┤ │ │┌┴┬┘│├┬┘ ├─┘├┬┘│ │ │ ├┤ │ ││││││ └─┐ ║═╬╗║
└─┘┴─┘┴┴ └─┴┴└─────┴ ┴└─└─┘ ┴ └─┘└─┘┴ ┴┴└─┘└─┘────╚═╝╚╚═╝
========================================
zipfiles (input files) : ${params.zipfiles}
watch (YES or NO) : ${params.watch}
qconfig (config file) : ${params.qconfig}
fasta_tab (tsv file) : ${params.fasta_tab}
out_folder (output folder) : ${params.out_folder}
email for notification : ${params.email}
"""
if (params.help) {
log.info 'This is the QCloud pipeline'
log.info '\n'
exit 1
}
if (params.resume) exit 1, "Are you making the classical --resume typo? Be careful!!!! ;)"
// Data folders
workflowsFolder = "$baseDir/workflows/"
fasta_folder = "$baseDir/fasta"
blastdb_folder = "$baseDir/blastdb"
CSV_folder = "$baseDir/csv"
fastaconfig = file(params.fasta_tab)
if( !fastaconfig.exists() ) { error "Cannot find any fasta tab file!!!"}
// Output folder
out_folder = file(params.out_folder)
if( !out_folder.exists() ) { out_folder.mkdir()}
// Files needed
srmCSV = file("${CSV_folder}/qtrap_bsa.traml")
peptideCSV = file("${CSV_folder}/knime_peptides_final.csv")
peptideCSV_C4L = file("${CSV_folder}/knime_peptides_qc4l.csv")
MSGFPlus = file("$baseDir/bin/MSGFPlus.jar")
//peptideCSVs
def peptideCSVs = [:]
peptideCSVs["QC01"] = "peptide.csv"
peptideCSVs["QC02"] = "peptide.csv"
peptideCSVs["QC03"] = "peptide_C4L.csv"
checkFiles([srmCSV, peptideCSV, peptideCSV_C4L])
/*
* check for workflow existence
*/
// QC01/QC02 WF
shotgunWF = file("${workflowsFolder}/module_workflow_shotgun.knwf")
// QCS1/QCS2 WF
srmWF = file("${workflowsFolder}/module_workflow_srm.knwf")
// QC03 WFs
shotgun_qc4l_cidWF = file("${workflowsFolder}/module_workflow_qc4l_cid.knwf")
shotgun_qc4l_hcdWF = file("${workflowsFolder}/module_workflow_qc4l_hcd.knwf")
shotgun_qc4l_etcidWF = file("${workflowsFolder}/module_workflow_qc4l_etcid.knwf")
shotgun_qc4l_ethcdWF = file("${workflowsFolder}/module_workflow_qc4l_ethcd.knwf")
// Common WFs
chekPeptidesWF = file("${workflowsFolder}/module_check_peptides.knwf")
api_connectionWF = file("${workflowsFolder}/module_api_conn.knwf")
// Check presence of knime's workflow files
checkFiles([shotgunWF, srmWF, chekPeptidesWF,api_connectionWF, shotgun_qc4l_cidWF, shotgun_qc4l_hcdWF, shotgun_qc4l_etcidWF, shotgun_qc4l_ethcdWF])
// Check presence of knime's workflow files
baseQCPath = "${workflowsFolder}/module_parameter_QC_"
def dbindex = new DBindexes()
def Correspondence = dbindex.getCorrespondence()
def ontology = dbindex.getOntology()
checkWFFiles(baseQCPath, Correspondence.keySet())
/*
* Create a channel for mzlfiles files; Temporary for testing purposes only
*/
// Below handles original_id from processing of samples: 181112_Q_QC1F_01_01_9d9d9d1b-9d9d-4f1a-9d27-9d2f7635059d_QC01_0d97b132db1ecedc3b5fdbddec6fba72.zip
if (params.watch=="YES") {
Channel
//.fromPath( params.zipfiles )
.watchPath( params.zipfiles )
.map {
file = it
id = it.getName()
ext = params.zipfiles.tokenize( '/' )
pieces = id.tokenize( '_' )
checksum = pieces[-1].replace(".raw.zip", "")
[pieces[-2], checksum, file]
}.set { zipfiles }
} else {
Channel
.fromPath( params.zipfiles )
.map {
file = it
id = it.getName()
ext = params.zipfiles.tokenize( '/' )
pieces = id.tokenize( '_' )
checksum = pieces[-1].replace(".raw.zip", "")
[pieces[-2], checksum, file]
}.set { zipfiles }
}
/*
* Create a channel for fasta files description
*/
Channel
.from(fastaconfig.readLines())
.map { line ->
list = line.split("\t")
genome_id = list[0]
internal_db = list[1]
fasta_file_name = list[2]
fasta_path = file("${fasta_folder}/${fasta_file_name}")
[genome_id, fasta_file_name, internal_db, fasta_path]
}
.into{ fasta_desc; blastdb_desc }
/*
* Read the config file and get genome and workflow information
*/
qconfig = file(params.qconfig)
if( !qconfig.exists() ) { error "Cannot find any qconfig tab file!!!"}
Channel
.from(qconfig.readLines())
.map { line ->
list = line.split("\t")
internal_code = list[0]
genome = list[1]
workflow_type = list[2]
[internal_code, genome, workflow_type]
}
.set{qconfig_desc}
/*
* Run makeblastdb on fasta data
*/
process makeblastdb {
storeDir blastdb_folder
afterScript("chmod 777 ${blastdb_folder}")
tag { genome_id }
input:
set genome_id, fasta_file, internal_dbfile, file(fasta_path) from fasta_desc
output:
set genome_id, internal_dbfile, file ("*") into blastdbs, blastdbs_d
script:
"""
if [ `echo ${fasta_file} | grep 'gz'` ]; then zcat ${fasta_file} > ${internal_dbfile}; else ln -s ${fasta_file} ${internal_dbfile}; fi
makeblastdb -dbtype prot -in ${internal_dbfile} -out ${internal_dbfile}
"""
}
/*
* Run msconvert on raw data. In case QC0S add a parameter
*/
process thermofilerawparser {
label 'thermoconvert'
publishDir "${out_folder}/${qcode}_${checksum}", mode: 'copy', pattern: "*.mzML"
tag { "${qcode}_${checksum}" }
input:
set qcode, checksum, file(zipfile) from zipfiles
output:
set val("${qcode}_${checksum}"), qcode, checksum, file("${qcode}_${checksum}.mzML") into mzmlfiles_for_correction
script:
def filename = zipfile.getBaseName()
"""
unzip ${zipfile}
ThermoRawFileParser -i=${filename} -f=1 -m=0 -o ./
"""
}
/*
* Run batch correction on mzl and eventually unzip the input file
* We remove the string xmlns="http://psi.hupo.org/ms/mzml" since it can causes problem with some executions
*/
process correctMzml {
tag { sample_id }
input:
set sample_id, qcode, checksum, file(mzML_file) from (mzmlfiles_for_correction)
output:
set qcode, sample_id, checksum, file("${sample_id}.ok.mzML") into corrected_mzmlfiles_for_second_step
"""
if [ `echo ${mzML_file} | grep 'gz'` ]; then zcat ${mzML_file} > ${sample_id}.mzML; \
sed s@'xmlns=\"http://psi.hupo.org/ms/mzml\"'@@g ${sample_id}.mzML > ${sample_id}.ok.mzML; \
else sed s@'xmlns=\"http://psi.hupo.org/ms/mzml\"'@@g ${mzML_file} > ${sample_id}.ok.mzML; fi
"""
}
/*
* Combine different channels (blast dbs, corrected mzml files) for obtaining the required input
* for the next steps
*/
input_pipe_withcode_reordered = corrected_mzmlfiles_for_second_step.combine(qconfig_desc,by: 0).map{
qc_id, sample_id, checksum, file, genome, analysis -> [genome, qc_id, sample_id, file, analysis, checksum]
}
input_pipe_complete_first_step = input_pipe_withcode_reordered.combine(blastdbs, by: 0)
input_pipe_complete_first_step
.into{ input_pipe_complete_first_step_for_srm; input_pipe_complete_first_step_for_shotgun; input_pipe_complete_first_step_for_shotgun_qc4l_cid; input_pipe_complete_first_step_for_shotgun_qc4l_hcd; input_pipe_complete_first_step_for_shotgun_qc4l_etcid; input_pipe_complete_first_step_for_shotgun_qc4l_ethcd ; debug }
/*
* Run shotgun on raw data (In case QC01 // QC02).
* Choose blast_db and fasta file depending on species
* choose genome depending on QC code in the file name // description etc .
*/
process run_shotgun {
publishDir "${out_folder}/${sample_id}", mode: 'copy', pattern: "*.qcml"
publishDir "${out_folder}/${sample_id}", mode: 'copy', pattern: "*.featureXML"
tag { sample_id }
label 'big_mem'
afterScript "$baseDir/bin/fixQcml.sh"
input:
set genome_id, internal_code, sample_id, file(mzML_file), analysis_type, checksum, fasta_file, file ("*") from input_pipe_complete_first_step_for_shotgun
file(workflowfile) from shotgunWF
when:
analysis_type == 'shotgun'
output:
set sample_id, internal_code, analysis_type, checksum, file("${sample_id}.featureXML") into shot_featureXMLfiles_for_calc_peptide_area, shot_featureXMLfiles_for_calc_mass_accuracy, shot_featureXMLfiles_for_calc_median_fwhm
set sample_id, internal_code, analysis_type, checksum, file(mzML_file) into shot_mzML_file_for_MedianITMS1, shot_mzML_file_for_MedianITMS2, shot_mzML_file_for_tic
set sample_id, internal_code, analysis_type, checksum, file(mzML_file), file(fasta_file) into shot_mzML_file_for_check
set sample_id, internal_code, analysis_type, checksum, file("${sample_id}.qcml") into qcmlfiles_for_MS2_spectral_count, qcmlfiles_for_tot_num_uniq_peptides, qcmlfiles_for_tot_num_uniq_proteins, qcmlfiles_for_tot_num_psm
script:
def outfiles = "${sample_id}.featureXML ${sample_id}.qcml ${sample_id}.idXML"
def knime = new Knime(wf:workflowfile, empty_out_file:outfiles, mem:"${task.memory.mega-5000}m", mzml:mzML_file, oqcml:"${sample_id}.qcml", ofeatxml:"${sample_id}.featureXML", oidxml:"${sample_id}.idXML", fasta:fasta_file, psq:"${fasta_file}.psq")
knime.launch()
}
/*
* Run srm on raw data (In case QC01 // QC02)
* Choose blast_db and fasta file depending on species
* choose genome depending on QC code in the file name // description etc .
*/
process run_srm {
tag { sample_id }
label 'big_mem'
input:
set genome_id, internal_code, sample_id, file(mzML_file), analysis_type, checksum, fasta_file, file ("*") from input_pipe_complete_first_step_for_srm
file(workflowfile) from srmWF
file(srmCSV)
when:
analysis_type == 'srm'
output:
set sample_id, internal_code, analysis_type, checksum, file("${sample_id}.featureXML") into srm_featureXMLfiles_for_calc_peptide_area, srm_featureXMLfiles_for_calc_mass_accuracy, srm_featureXMLfiles_for_calc_median_fwhm
set sample_id, internal_code, analysis_type, checksum, file(mzML_file) into srm_mzML_file_for_MedianITMS1, srm_mzML_file_for_MedianITMS2, srm_mzML_file_for_check
script:
def outfile = "${sample_id}.featureXML"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", mzml:mzML_file, ofeatxml:"${sample_id}.featureXML", srmCSV:srmCSV)
knime.launch()
}
/*
* Run shotgun_qc4l_cid on raw data (In case QC03)
* Choose blast_db and fasta file depending on species
* choose genome depending on QC code in the file name // description etc .
*/
process shotgun_qc4l_cid {
tag { sample_id }
label 'big_mem'
afterScript "$baseDir/bin/fixQcml.sh"
input:
set genome_id, internal_code, sample_id, file(mzML_file), analysis_type, checksum, fasta_file, file ("*") from input_pipe_complete_first_step_for_shotgun_qc4l_cid
file(workflowfile) from shotgun_qc4l_cidWF
when:
analysis_type == 'shotgun_qc4l'
output:
set val("${sample_id}_cid"), internal_code, val("shotgun_qc4l_cid"), checksum, file("${sample_id}.featureXML") into shot_qc4l_cid_featureXMLfiles_for_calc_peptide_area, shot_qc4l_cid_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_cid_featureXMLfiles_for_calc_median_fwhm
set val("${sample_id}_cid"), internal_code, val("shotgun_qc4l_cid"), checksum, file(mzML_file) into shot_qc4l_cid_mzML_file_for_MedianITMS1, shot_qc4l_cid_mzML_file_for_MedianITMS2, shot_qc4l_cid_mzML_file_for_check, shot_qc4l_cid_mzML_file_for_tic
set val("${sample_id}_cid"), internal_code, val("shotgun_qc4l_cid"), checksum, file("${sample_id}.qcml") into shot_qc4l_cid_qcmlfiles_for_MS2_spectral_count, shot_qc4l_cid_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_cid_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_cid_qcmlfiles_for_tot_num_psm
script:
def outfiles = "${sample_id}.featureXML ${sample_id}.qcml ${sample_id}.idXML"
def knime = new Knime(wf:workflowfile, empty_out_file:outfiles, mem:"${task.memory.mega-5000}m", mzml:mzML_file, oqcml:"${sample_id}.qcml", ofeatxml:"${sample_id}.featureXML", oidxml:"${sample_id}.idXML", fasta:fasta_file, psq:"${fasta_file}.psq")
knime.launch()
}
/*
* Run shotgun_qc4l_hcd on raw data (In case QC03)
* Choose blast_db and fasta file depending on species
* choose genome depending on QC code in the file name // description etc .
*/
process shotgun_qc4l_hcd {
tag { sample_id }
label 'big_mem'
afterScript "$baseDir/bin/fixQcml.sh"
input:
set genome_id, internal_code, sample_id, file(mzML_file), analysis_type, checksum, fasta_file, file ("*") from input_pipe_complete_first_step_for_shotgun_qc4l_hcd
file(workflowfile) from shotgun_qc4l_hcdWF
when:
analysis_type == 'shotgun_qc4l'
output:
set val("${sample_id}_hcd"), internal_code, val("shotgun_qc4l_hcd"), checksum, file("${sample_id}.featureXML") into shot_qc4l_hcd_featureXMLfiles_for_calc_peptide_area, shot_qc4l_hcd_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_hcd_featureXMLfiles_for_calc_median_fwhm
set val("${sample_id}_hcd"), internal_code, val("shotgun_qc4l_hcd"), checksum, file(mzML_file) into shot_qc4l_hcd_mzML_file_for_MedianITMS1, shot_qc4l_hcd_mzML_file_for_MedianITMS2, shot_qc4l_hcd_mzML_file_for_check, shot_qc4l_hcd_mzML_file_for_tic
set val("${sample_id}_hcd"), internal_code, val("shotgun_qc4l_hcd"), checksum, file("${sample_id}.qcml") into shot_qc4l_hcd_qcmlfiles_for_MS2_spectral_count, shot_qc4l_hcd_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_hcd_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_hcd_qcmlfiles_for_tot_num_psm
script:
def outfiles = "${sample_id}.featureXML ${sample_id}.qcml ${sample_id}.idXML"
def knime = new Knime(wf:workflowfile, empty_out_file:outfiles, mem:"${task.memory.mega-5000}m", mzml:mzML_file, oqcml:"${sample_id}.qcml", ofeatxml:"${sample_id}.featureXML", oidxml:"${sample_id}.idXML", fasta:fasta_file, psq:"${fasta_file}.psq")
knime.launch()
}
/*
* Run shotgun_qc4l_etcid on raw data (In case QC03)
* Choose blast_db and fasta file depending on species
* choose genome depending on QC code in the file name // description etc .
*/
process shotgun_qc4l_etcid {
tag { sample_id }
label 'big_mem'
afterScript "$baseDir/bin/fixQcml.sh"
input:
set genome_id, internal_code, sample_id, file(mzML_file), analysis_type, checksum, fasta_file, file ("*") from input_pipe_complete_first_step_for_shotgun_qc4l_etcid
file(workflowfile) from shotgun_qc4l_etcidWF
when:
analysis_type == 'shotgun_qc4l'
output:
set val("${sample_id}_etcid"), internal_code, val("shotgun_qc4l_etcid"), checksum, file("${sample_id}.featureXML") into shot_qc4l_etcid_featureXMLfiles_for_calc_peptide_area, shot_qc4l_etcid_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_etcid_featureXMLfiles_for_calc_median_fwhm
set val("${sample_id}_etcid"), internal_code, val("shotgun_qc4l_etcid"), checksum, file(mzML_file) into shot_qc4l_etcid_mzML_file_for_MedianITMS1, shot_qc4l_etcid_mzML_file_for_MedianITMS2, shot_qc4l_etcid_mzML_file_for_check, shot_qc4l_etcid_mzML_file_for_tic
set val("${sample_id}_etcid"), internal_code, val("shotgun_qc4l_etcid"), checksum, file("${sample_id}.qcml") into shot_qc4l_etcid_qcmlfiles_for_MS2_spectral_count, shot_qc4l_etcid_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_etcid_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_etcid_qcmlfiles_for_tot_num_psm
script:
def outfiles = "${sample_id}.featureXML ${sample_id}.qcml ${sample_id}.idXML"
def knime = new Knime(wf:workflowfile, empty_out_file:outfiles, mem:"${task.memory.mega-5000}m", mzml:mzML_file, oqcml:"${sample_id}.qcml", ofeatxml:"${sample_id}.featureXML", oidxml:"${sample_id}.idXML", fasta:fasta_file, psq:"${fasta_file}.psq")
knime.launch()
}
/*
* Run shotgun_qc4l_ethcd on raw data (In case QC03)
* Choose blast_db and fasta file depending on species
* choose genome depending on QC code in the file name // description etc .
*/
process shotgun_qc4l_ethcd {
tag { sample_id }
label 'big_mem'
afterScript "$baseDir/bin/fixQcml.sh"
input:
set genome_id, internal_code, sample_id, file(mzML_file), analysis_type, checksum, fasta_file, file ("*") from input_pipe_complete_first_step_for_shotgun_qc4l_ethcd
file(workflowfile) from shotgun_qc4l_ethcdWF
when:
analysis_type == 'shotgun_qc4l'
output:
set val("${sample_id}_ethcd"), internal_code, val("shotgun_qc4l_ethcd"), checksum, file("${sample_id}.featureXML") into shot_qc4l_ethcd_featureXMLfiles_for_calc_peptide_area, shot_qc4l_ethcd_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_ethcd_featureXMLfiles_for_calc_median_fwhm
set val("${sample_id}_ethcd"), internal_code, val("shotgun_qc4l_ethcd"), checksum, file(mzML_file) into shot_qc4l_ethcd_mzML_file_for_MedianITMS1, shot_qc4l_ethcd_mzML_file_for_MedianITMS2, shot_qc4l_ethcd_mzML_file_for_check, shot_qc4l_ethcd_mzML_file_for_tic
set val("${sample_id}_ethcd"), internal_code, val("shotgun_qc4l_ethcd"), checksum, file("${sample_id}.qcml") into shot_qc4l_ethcd_qcmlfiles_for_MS2_spectral_count, shot_qc4l_ethcd_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_ethcd_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_ethcd_qcmlfiles_for_tot_num_psm
script:
def outfiles = "${sample_id}.featureXML ${sample_id}.qcml ${sample_id}.idXML"
def knime = new Knime(wf:workflowfile, empty_out_file:outfiles, mem:"${task.memory.mega-5000}m", mzml:mzML_file, oqcml:"${sample_id}.qcml", ofeatxml:"${sample_id}.featureXML", oidxml:"${sample_id}.idXML", fasta:fasta_file, psq:"${fasta_file}.psq")
knime.launch()
}
/*
* Run calculation of MS2 spectral count
*/
process calc_MS2_spectral_count {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, val(analysis_type), checksum, file(qcmlfile) from qcmlfiles_for_MS2_spectral_count.mix(shot_qc4l_cid_qcmlfiles_for_MS2_spectral_count, shot_qc4l_hcd_qcmlfiles_for_MS2_spectral_count, shot_qc4l_etcid_qcmlfiles_for_MS2_spectral_count, shot_qc4l_ethcd_qcmlfiles_for_MS2_spectral_count)
file(workflowfile) from getWFFile(baseQCPath, "MS2specCount")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['MS2specCount'][analysis_type]}.json") into ms2_spectral_for_delivery
script:
def analysis_id = Correspondence['MS2specCount'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['MS2specCount'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", qcml:qcmlfile, qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of total number of unique identified peptides
*/
process calc_tot_num_uniq_peptides {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(qcmlfile) from qcmlfiles_for_tot_num_uniq_peptides.mix(shot_qc4l_cid_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_hcd_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_etcid_qcmlfiles_for_tot_num_uniq_peptides, shot_qc4l_ethcd_qcmlfiles_for_tot_num_uniq_peptides)
file(workflowfile) from getWFFile(baseQCPath, "totNumOfUniPep")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['totNumOfUniPep'][analysis_type]}.json") into uni_peptides_for_delivery
script:
def analysis_id = Correspondence['totNumOfUniPep'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['totNumOfUniPep'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", qcml:qcmlfile, qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of total number of uniquely identified proteins
*/
process calc_tot_num_uniq_proteins {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(qcmlfile) from qcmlfiles_for_tot_num_uniq_proteins.mix(shot_qc4l_cid_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_hcd_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_etcid_qcmlfiles_for_tot_num_uniq_proteins, shot_qc4l_ethcd_qcmlfiles_for_tot_num_uniq_proteins)
file(workflowfile) from getWFFile(baseQCPath, "totNumOfUniProt")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['totNumOfUniProt'][analysis_type]}.json") into uni_prots_for_delivery
script:
def analysis_id = Correspondence['totNumOfUniProt'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['totNumOfUniProt'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", qcml:qcmlfile, qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of total number of Peptide Spectral Matches
*/
process calc_tot_num_psm {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(qcmlfile) from qcmlfiles_for_tot_num_psm.mix(shot_qc4l_cid_qcmlfiles_for_tot_num_psm, shot_qc4l_hcd_qcmlfiles_for_tot_num_psm, shot_qc4l_etcid_qcmlfiles_for_tot_num_psm, shot_qc4l_ethcd_qcmlfiles_for_tot_num_psm)
file(workflowfile) from getWFFile(baseQCPath, "totNumOfPsm")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['totNumOfPsm'][analysis_type]}.json") into tot_psm_for_delivery
script:
def analysis_id = Correspondence['totNumOfPsm'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['totNumOfPsm'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", qcml:qcmlfile, qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of median IT MS1
*/
process calc_median_IT_MS1 {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(mzml_file) from shot_mzML_file_for_MedianITMS1.mix(srm_mzML_file_for_MedianITMS1, shot_qc4l_cid_mzML_file_for_MedianITMS1, shot_qc4l_hcd_mzML_file_for_MedianITMS1, shot_qc4l_etcid_mzML_file_for_MedianITMS1, shot_qc4l_ethcd_mzML_file_for_MedianITMS1)
file(workflowfile) from getWFFile(baseQCPath, "medianITMS1")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['medianITMS1'][analysis_type]}.json") into median_itms1_for_delivery
script:
def analysis_id = Correspondence['medianITMS1'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['medianITMS1'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", mzml:mzml_file, qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of median IT MS2
*/
process calc_median_IT_MS2 {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(mzml_file) from shot_mzML_file_for_MedianITMS2.mix(srm_mzML_file_for_MedianITMS2, shot_qc4l_cid_mzML_file_for_MedianITMS2, shot_qc4l_hcd_mzML_file_for_MedianITMS2, shot_qc4l_etcid_mzML_file_for_MedianITMS2, shot_qc4l_ethcd_mzML_file_for_MedianITMS2)
file(workflowfile) from getWFFile(baseQCPath, "medianITMS2")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['medianITMS2'][analysis_type]}.json") into median_itms2_for_delivery
script:
def analysis_id = Correspondence['medianITMS2'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['medianITMS2'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mem:"${task.memory.mega-5000}m", mzml:mzml_file, qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of peptide area (only QC1 and QC2)
*/
process calc_peptide_area {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, val(internal_code), analysis_type, checksum, file(featxml_file) from shot_featureXMLfiles_for_calc_peptide_area.mix(srm_featureXMLfiles_for_calc_peptide_area)
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
file(workflowfile) from getWFFile(baseQCPath, "pepArea")
output:
set sample_id, internal_code, checksum, val("${Correspondence['pepArea'][analysis_type]}"), file("${sample_id}_QC_${Correspondence['pepArea'][analysis_type]}.json") into pep_area_for_check
script:
def analysis_id = Correspondence['pepArea'][analysis_type]
def ontology_id = ontology[analysis_id]
def csvfile = peptideCSVs[internal_code]
def outfile = "${sample_id}_QC_${Correspondence['pepArea'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, csvpep:csvfile, stype:internal_code, featxml:featxml_file, mem:"${task.memory.mega-5000}m", qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of peptide area (Only QC3 // hcd)
*/
process calc_peptide_area_c4l {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(featxml_file) from shot_qc4l_hcd_featureXMLfiles_for_calc_peptide_area
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
file(workflowfile) from getWFFile(baseQCPath, "pepArea_qc4l")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['pepArea_qc4l'][analysis_type]}.json") into pep_c4l_for_delivery
script:
def analysis_id = Correspondence['pepArea_qc4l'][analysis_type]
def ontology_id = ontology[analysis_id]
def csvfile = peptideCSVs[internal_code]
def outfile = "${sample_id}_QC_${Correspondence['pepArea_qc4l'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, csvpep:csvfile, stype:internal_code, featxml:featxml_file, mem:"${task.memory.mega-5000}m", qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}", extrapars:'-workflow.variable=delta_mass,10,double -workflow.variable=delta_rt,250,double -workflow.variable=charge,2,double -workflow.variable=threshold_area,1000000,double')
knime.launch()
}
/*
* Run calculation of peptide area (Only QC3 // others) WORKAROUND
*/
process calc_peptide_area_c4l_fake {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(featxml_file) from shot_qc4l_cid_featureXMLfiles_for_calc_peptide_area.mix(shot_qc4l_etcid_featureXMLfiles_for_calc_peptide_area, shot_qc4l_ethcd_featureXMLfiles_for_calc_peptide_area)
output:
set sample_id, val(null) into pep_c4l_for_delivery_fake
script:
"""
echo "this is a workaround because of a nextflow problem"
"""
}
/*
* Run calculation of Sum of all Total Ion Current per RT
*/
process calc_tic {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, val(analysis_type), checksum, file(mzmlfile) from shot_mzML_file_for_tic.mix(shot_qc4l_cid_mzML_file_for_tic, shot_qc4l_hcd_mzML_file_for_tic, shot_qc4l_etcid_mzML_file_for_tic, shot_qc4l_ethcd_mzML_file_for_tic)
file(workflowfile) from getWFFile(baseQCPath, "tic")
output:
set sample_id, file("${sample_id}_QC_${Correspondence['tic'][analysis_type]}.json") into tic_for_delivery
script:
def analysis_id = Correspondence['tic'][analysis_type]
def ontology_id = ontology[analysis_id]
def outfile = "${sample_id}_QC_${Correspondence['tic'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, mzml:mzmlfile, stype:internal_code, mem:"${task.memory.mega-5000}m", qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of Mass accuracy
*/
process calc_mass_accuracy {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(featxml_file) from shot_featureXMLfiles_for_calc_mass_accuracy.mix(srm_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_cid_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_hcd_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_etcid_featureXMLfiles_for_calc_mass_accuracy, shot_qc4l_ethcd_featureXMLfiles_for_calc_mass_accuracy)
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
file(workflowfile) from getWFFile(baseQCPath, "massAccuracy")
output:
set sample_id, internal_code, checksum, val("${Correspondence['massAccuracy'][analysis_type]}"), file("${sample_id}_QC_${Correspondence['massAccuracy'][analysis_type]}.json") into mass_json_for_check
script:
def analysis_id = Correspondence['massAccuracy'][analysis_type]
def ontology_id = ontology[analysis_id]
def csvfile = peptideCSVs[internal_code]
def outfile = "${sample_id}_QC_${Correspondence['massAccuracy'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, csvpep:csvfile, stype:internal_code, featxml:featxml_file, mem:"${task.memory.mega-5000}m", qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Run calculation of Median Fwhm
*/
process calc_median_fwhm {
tag { "${sample_id}-${analysis_type}" }
input:
set sample_id, internal_code, analysis_type, checksum, file(featxml_file) from shot_featureXMLfiles_for_calc_median_fwhm.mix(srm_featureXMLfiles_for_calc_median_fwhm, shot_qc4l_cid_featureXMLfiles_for_calc_median_fwhm, shot_qc4l_hcd_featureXMLfiles_for_calc_median_fwhm, shot_qc4l_etcid_featureXMLfiles_for_calc_median_fwhm, shot_qc4l_ethcd_featureXMLfiles_for_calc_median_fwhm)
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
file(workflowfile) from getWFFile(baseQCPath, "medianFwhm")
output:
set sample_id, internal_code, checksum, val("${Correspondence['medianFwhm'][analysis_type]}"), file("${sample_id}_QC_${Correspondence['medianFwhm'][analysis_type]}.json") into median_fwhm_for_check
script:
def analysis_id = Correspondence['medianFwhm'][analysis_type]
def ontology_id = ontology[analysis_id]
def csvfile = peptideCSVs[internal_code]
def outfile = "${sample_id}_QC_${Correspondence['medianFwhm'][analysis_type]}.json"
def knime = new Knime(wf:workflowfile, empty_out_file:outfile, csvpep:csvfile, stype:internal_code, featxml:featxml_file, mem:"${task.memory.mega-5000}m", qccv:"QC_${analysis_id}", qccvp:"QC_${ontology_id}", chksum:checksum, ojid:"${sample_id}")
knime.launch()
}
/*
* Check petide results
*/
process check_peptides {
tag { "${sample_id}-${process_id}-json_file" }
beforeScript("mkdir out")
input:
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
set sample_id, internal_code, checksum, process_id, file(json_file) from pep_area_for_check
file(workflowfile) from chekPeptidesWF
output:
set sample_id, file("out/${json_file}") into pep_checked_for_delivery
script:
def csvfile = peptideCSVs[internal_code]
def outfile = "out/${json_file}"
def knime = new Knime(qccv:"QC_${process_id}", empty_out_file:outfile, wf:workflowfile, chksum:checksum, csvpep:csvfile, stype:internal_code, ijfile:json_file, mem:"${task.memory.mega-5000}m", ofolder:"./out", ojfile:"${json_file}")
knime.launch()
}
/*
* Check mass results
*/
process check_mass {
tag { sample_id }
beforeScript("mkdir out")
input:
set sample_id, internal_code, checksum, process_id, file(json_file) from mass_json_for_check
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
file(workflowfile) from chekPeptidesWF
output:
set sample_id, file("out/${json_file}") into mass_checked_for_delivery
script:
def csvfile = peptideCSVs[internal_code]
def outfile = "out/${json_file}"
def knime = new Knime(qccv:"QC_${process_id}", empty_out_file:outfile, wf:workflowfile, chksum:checksum, csvpep:csvfile, stype:internal_code, ijfile:json_file, mem:"${task.memory.mega-5000}m", ofolder:"./out", ojfile:"${json_file}")
knime.launch()
}
/*
* Check fwhm results
*/
process check_fwhm {
tag { sample_id }
beforeScript("mkdir out")
input:
set sample_id, internal_code, checksum, process_id, file(json_file) from median_fwhm_for_check
file ("peptide.csv") from file (peptideCSV)
file ("peptide_C4L.csv") from file (peptideCSV_C4L)
file(workflowfile) from chekPeptidesWF
output:
set sample_id, file("out/${json_file}") into median_checked_for_delivery
script:
def csvfile = peptideCSVs[internal_code]
def outfile = "out/${json_file}"
def knime = new Knime(qccv:"QC_${process_id}", empty_out_file:outfile, wf:workflowfile, chksum:checksum, csvpep:csvfile, stype:internal_code, ijfile:json_file, mem:"${task.memory.mega-5000}m", ofolder:"./out", ojfile:"${json_file}")
knime.launch()
}
process MSGFPlus {
tag { sample_id }
publishDir "${out_folder}/${sample_id}", mode: 'copy', pattern: "*.mzid"
input:
file(MSGFPlus)
set sample_id, internal_id, analysis_type, checksum, file(mzML_file), file(fasta) from shot_mzML_file_for_check.mix(srm_mzML_file_for_check, shot_qc4l_cid_mzML_file_for_check, shot_qc4l_hcd_mzML_file_for_check, shot_qc4l_etcid_mzML_file_for_check, shot_qc4l_ethcd_mzML_file_for_check)
output:
set sample_id, internal_id, analysis_type, checksum, file("${sample_id}.mzid") into mZML_params_for_mapping
script:
"""
java -jar ${MSGFPlus} -s ${mzML_file} -o ${sample_id}.mzid -d ${fasta}
"""
}
/*
* Reshaping channels
*/
// mix peptide channels (from QC01, QC02 and QC03 to have for each id a number of results)
pep_c4l_all = pep_c4l_for_delivery_fake.mix(pep_c4l_for_delivery, pep_checked_for_delivery)
// joins channels common to any analysis in a single channel
ms2_spectral_for_delivery.join(tic_for_delivery).join(tot_psm_for_delivery).join(uni_peptides_for_delivery).join(uni_prots_for_delivery).join(median_itms2_for_delivery).join(mass_checked_for_delivery).join(median_checked_for_delivery).join(median_itms1_for_delivery).join(pep_c4l_all).into{jointJsons; jointJsonsAA}
// separate this channel depending on QC01-QC02/ QC03
queueQC12 = Channel.create()
queueQC03 = Channel.create()
jointJsons.choice( queueQC03, queueQC12 ) { a -> a =~ /QC03/ ? 0 : 1 }
// group the outputs of QC03 depending on the original id
queueQC03Grouped = queueQC03.map{
def rawids = it[0].tokenize( '_' );
def orid = "${rawids[0]}_${rawids[1]}_${rawids[2]}";
def l = [orid];
l.addAll([it.drop(1)]);
return l
}.groupTuple(size:4)
queueQC03ToBeSent = queueQC03Grouped.map{
def id = [it[0]]
id.addAll([it.drop(1).flatten()]);
return id
}
// reshape the QC01-QC02 channel
queueQC12ToBeSent = queueQC12.map{
def rawids = it[0].tokenize( '_' );
def orid = "${rawids[0]}_${rawids[1]}";
def l = [orid];
l.addAll([it.drop(1)]);
return l
}
// mix the QC01-QC02 and QC03 again
jsonToBeSent = queueQC12ToBeSent.mix(queueQC03ToBeSent)
// reshape the mZML params channel for the submission
mZML_params_for_mapping.map{
def rawids = it[0].tokenize( '_' )
def sample_id = "${rawids[0]}_${rawids[1]}"
[sample_id , it[1], it[3], it[4] ]
}.unique().into {mZML_params_for_delivery; cicco}
/*
* collect Results
*/
process collectResults {
tag { sample_id }
input:
set sample_id, internal_code, checksum, file(mzidfile), file("*") from mZML_params_for_delivery.join(jsonToBeSent)
output:
set sample_id, file("${sample_id}.json") into json_to_be_converted
script:
"""
json_merger.sh \$PWD ${sample_id}.json
"""
}
process convertResults {
tag { sample_id }
label 'mzqcconvert'
publishDir "${out_folder}/${sample_id}", mode: 'copy'
input:
set sample_id, file(json) from json_to_be_converted
output:
file("${sample_id}.mzQC")
script:
"""
qcloud_mzqc_conversion_v0.1.py -input ${json} -output ${sample_id}.mzQC
"""
}
workflow.onComplete {
println "Pipeline ELIXIR@Proteomics QC PIPELINE!"
println "Started at $workflow.start"
println "Finished at $workflow.complete"
println "Time elapsed: $workflow.duration"
println "Execution status: ${ workflow.success ? 'OK' : 'failed' }"
}
/*
* Functions
*/
def public getWFFile(filePrefix, WF_ID) {
return file("${filePrefix}${WF_ID}.knwf")
}
def public checkWFFiles(filePrefix, WF_vals) {
def WF_IDs = WF_vals.toList().unique()
WF_IDs.each() {
knwfFIle = getWFFile(filePrefix, it)
checkFile(knwfFIle)
}
}
def public checkFiles(filePaths) {
for (filePath in filePaths) {
checkFile(filePath)
}
}
def public checkFile(filePath) {
if (!filePath.exists()) {
error "Cannot find any ${filePath} file!!!"
}
}