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pcm_dev_2.nf
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#!/usr/bin/env nextflow
workflow.onComplete = {
// any workflow property can be used here
println "Pipeline complete"
println "Command line: $workflow.commandLine"
}
workflow.onError = {
println "Oops .. something went wrong"
}
// General parameters
params.candidates = ""
params.evotarmd = "${baseDir}/bin/evotar.rmd"
params.in = "${baseDir}/example/example_proteome.faa"
params.out = "${baseDir}/example/res"
params.modelling = "${params.out}/modelling"
//params.cpu = 2
//params.cpu_candidates = 4
params.database = "${baseDir}/database/"
//params.database = "/usr/local/bin/database/"
params.universal_model = "${params.database}/universal_model2.tsv"
//params.universal_model = "/usr/local/bin/database/universal_model.csv"
params.cleaned_pdb = "${params.database}/cleaned_pdb/"
//params.proq = "$HOME/soft/ProQv1.2/"
params.proq = "/usr/local/bin/"
//params.psipred = "$HOME/soft/psipred/"
params.psipred = "/usr/local/bin/"
//params.mypmfs = "${baseDir}/bin/"
params.mypmfs = "/usr/local/bin/"
params.modelling_quality = "fast"
params.model = 6
params.template = 3
params.hfinder_evalue = 1E-5
//params.tmalign_dir = "${baseDir}/soft/"
params.tmalign_dir = "/usr/local/bin/"
//params.mammoth_dir = "${baseDir}/soft/"
params.mammoth_dir = "/usr/local/bin/"
params.family = "aac2,aac3_1,aac3_2,aac6,ant,aph,arnm,blaa,blab1,blab3,blac,blad,dfra,erm,fos,ldt,mcr,qnr,sul,tetM,tetX,van"
filter = params.family
tab = filter.tokenize( ',' )
params.trained_data = "${baseDir}/trained_data/"
params.predout = "${params.out}/prediction_output.tsv"
params.matrixout = "${params.out}/pcm_result.tsv"
params.predhtmlout = "${params.out}/result.html"
myDir = file(params.out)
myDir.mkdirs()
modDir = file(params.modelling)
modDir.mkdirs()
multifastaChannel = Channel
.fromPath("${params.in}")
.ifEmpty { exit 1, "Missing parameter: ${params.in}" }
params.help=false
def usage() {
println("pcm.nf --in <fasta_file> --out <output_dir> --cpus <nb_cpus> -w <temp_work_dir>")
println("--in Multifasta file containing protein sequence (default ${params.in}).")
println("--out Output directory (default ${params.out}). ")
println("--candidates Table providing a set of family and sequence to test by pcm (in the tsv format: family_name\\tsequence_name)")
// println("--cpu Number of cpus for homology modeling processing (default ${params.cpu})")
// println("--cpu_candidates Number of cpus for candidates search (default ${params.cpu_candidates})")
println("--family Select the family to consider (default aac2,aac3_1,aac3_2,aac6,ant,aph,arnm,blaa,blab1,blab3,blac,blad,dfra,erm,fos,ldt,mcr,qnr,sul,tetM,tetX,van, serpin)")
println("--hfinder_evalue E-value threshold to search candidates (default ${params.hfinder_evalue})")
println("--modelling_quality Level of quality of the homology modelling fast, normal or max (default ${params.modelling_quality})")
println("--model Number of model calculated (default ${params.model})")
println("--template Number of template for modelling (default ${params.template})")
}
if(params.help){
usage()
exit(1)
}
familyChannel = Channel
.from(
["aac2", "142", "236", "${params.database}/aac2/aac2_ref.faa", "${params.database}/aac2/aac2.hmm"],
["aac3_1", "122", "203","${params.database}/aac3_1/aac3_1_ref.faa","${params.database}/aac3_1/aac3_1.hmm"],
["aac3_2", "205", "341","${params.database}/aac3_2/aac3_2_ref.faa","${params.database}/aac3_2/aac3_2.hmm"],
["aac6", "123", "205","${params.database}/aac6/aac6_ref.faa","${params.database}/aac6/aac6.hmm"],
["ant", "194", "323","${params.database}/ant/ant_ref.faa","${params.database}/ant/ant.hmm"],
["aph", "206", "344","${params.database}/aph/aph_ref.faa","${params.database}/aph/aph.hmm"],
["arnm", "190", "316","${params.database}/arnm/arnm_ref.faa","${params.database}/arnm/arnm.hmm"],
["blaa", "219", "365","${params.database}/blaa/blaa_ref.faa","${params.database}/blaa/blaa.hmm"],
["blab1", "191", "318","${params.database}/blab1/blab1_ref.faa","${params.database}/blab1/blab1.hmm"],
["blab3", "215", "359","${params.database}/blab3/blab3_ref.faa","${params.database}/blab3/blab3.hmm"],
["blac", "290", "483","${params.database}/blac/blac_ref.faa","${params.database}/blac/blac.hmm"],
["blad", "203", "338","${params.database}/blad/blad_ref.faa","${params.database}/blad/blad.hmm"],
["dfra", "114", "236","${params.database}/dfra/dfra_ref.faa","${params.database}/dfra/dfra.hmm"],
["erm", "203", "338","${params.database}/erm/erm_ref.faa","${params.database}/erm/erm.hmm"],
["fos", "105", "175","${params.database}/fos/fos_ref.faa","${params.database}/fos/fos.hmm"],
["ldt", "257", "429","${params.database}/ldt/ldt_ref.faa","${params.database}/ldt/ldt.hmm"],
["mcr", "172", "578","${params.database}/mcr/mcr_ref.faa","${params.database}/mcr/mcr.hmm"],
["qnr", "164", "274","${params.database}/qnr/qnr_ref.faa","${params.database}/qnr/qnr.hmm"],
["sul", "209", "349","${params.database}/sul/sul_ref.faa","${params.database}/sul/sul.hmm"],
["tetM", "475", "791","${params.database}/tetM/tetM_ref.faa","${params.database}/tetM/tetM.hmm"],
["tetX", "287", "478","${params.database}/tetX/tetX_ref.faa","${params.database}/tetX/tetX.hmm"],
["van", "260", "433","${params.database}/van/van_ref.faa","${params.database}/van/van.hmm"],
["serpin", "337", "563","${params.database}/serpin/serpin_ref.faa", "${params.database}/serpin/serpin_ref.hmm"],
["lytr", "75", "125","${params.database}/lytr/lytr_ref.faa", "${params.database}/lytr/lytr_ref.hmm"]
)
.filter{ it[0] in tab}
process convert_names {
publishDir "$myDir/", mode: 'copy'
input:
file(fasta) from multifastaChannel
output:
file("conv_name.fasta") into multifastaconvChannel
file("association.tsv") into assoconvChannel
script:
"""
convert_names.py -i ${fasta} -o conv_name.fasta -a association.tsv
"""
}
if (params.candidates){
selectedChannel = Channel.fromPath("${params.candidates}")
.ifEmpty { exit 1, "Cannot find candidate list file: ${params.candidates}" }
.splitCsv(sep: "\t")
.groupTuple()
.map{it -> [it[0], it[1]] }
// index
process extract_candidates {
tag "${fam[0]}"
input:
each fam from selectedChannel
file(fasta) from multifastaconvChannel
output:
set val("${fam[0]}"), file("splitted/*.fasta") into fastaChannel mode flatten
script:
//File newFile = new File("${workflow.workDir}/${fam[0]}.txt")
//newFile.withWriter{ out -> fam[1].each {out.println it} }
"""
#grab_catalogue_sequence.py -i ${workflow.workDir}/${fam[0]}.txt -d ${fasta} -o ${fam[0]}.fasta
grab_catalogue_sequence.py -l "${fam[1]}" -d ${fasta} -o ${fam[0]}.fasta
extract_sequence.py ${fam[0]}.fasta splitted/
"""
}
}
else{
// index
process index_query {
tag "${fasta.baseName}"
if(params.modelling_quality == "fast"){
// cpus params.cpu_candidates
}
input:
file(fasta) from multifastaconvChannel
output:
set file(fasta), file("*") into multifastaindexedChannel
shell:
"""
if [ "!{params.modelling_quality}" == "fast" ]
then
mmseqs createdb !{fasta} targetDB
mmseqs createindex targetDB tmp --threads !{task.cpus}
rm -rf tmp
else
makeblastdb -in !{fasta} -dbtype prot
fi
"""
}
process search_distant_homologuous {
tag "${fam[0]}"
publishDir "$myDir/candidates/", mode: 'copy'
errorStrategy 'retry'
maxRetries 2
// cpus params.cpu_candidates
input:
set file(fasta), file(index) from multifastaindexedChannel
each fam from familyChannel
output:
set val("${fam[0]}"), file("*_candidates/*.fasta") optional true into candidatesChannel
file("*_candidates/*.tsv") optional true into summaryChannel
shell:
"""
mkdir !{fam[0]}_candidates/ tmp
if [ "!{params.modelling_quality}" == "fast" ]
then
hfinder.py -q !{fam[3]} -d !{fasta} -db targetDB -tmp tmp -s mmseqs -e !{params.hfinder_evalue} -lmin !{fam[1]} -lmax !{fam[2]} -b extract fastcheck -r !{fam[0]}_candidates/ -n 1 -t !{task.cpus}
else
hfinder.py -q !{fam[3]} -qm !{fam[4]} -d ${fasta} -s blastp hmmsearch ssearch -e !{params.hfinder_evalue} -lmin !{fam[1]} -lmax !{fam[2]} -b extract cumulative check -r !{fam[0]}_candidates/ -n 1 -t !{task.cpus}
fi
if [ -f "!{fam[0]}_candidates/all_protein_homology.fasta" ]
then
mv !{fam[0]}_candidates/all_protein_homology.fasta !{fam[0]}_candidates/!{fam[0]}_candidates.fasta
mv !{fam[0]}_candidates/all_hit_length.tsv !{fam[0]}_candidates/!{fam[0]}_candidates_hit_length.tsv
mv !{fam[0]}_candidates/all_hit_properties.tsv !{fam[0]}_candidates/!{fam[0]}_candidates_hit_properties.tsv
fi
"""
}
process fastaExtract {
tag "${fasta}"
input:
set fam, file(fasta) from candidatesChannel
output:
set fam, file("splitted/*.fasta") into fastaChannel mode flatten
shell:
"""
extract_sequence.py !{fasta} splitted/
"""
}
}
process homology_modelling {
tag "${fasta.baseName}:${fam}"
publishDir "$myDir/modelling/${fam}_candidates/", mode: 'copy'
errorStrategy 'finish'
// cpus params.cpu
// label 'modelling'
input:
set fam, file(fasta) from fastaChannel
output:
set fam, file(fasta), file("ref/*/*.horiz"), file("best_model_ref/*.pdb"), file("ref/*/result_proq_*"), file("ref/*/result_mypmfs_*"), file("ref/*/modeller_summary_*.csv"), file("tneg/*/*.horiz"), file("best_model_tneg/*.pdb"), file("tneg/*/result_proq_*"), file("tneg/*/result_mypmfs_*"), file("tneg/*/modeller_summary_*.csv") optional true into modelChannel
file("*/*/*.svg") optional true into imgChannel
file("*/*/*.pdb") optional true into allPDBChannel
shell:
"""
#runpsipred !{fasta} !{task.cpus}
mkdir -p ref/!{fasta.baseName}/ best_model_ref/ tneg/!{fasta.baseName}/ best_model_tneg/
# ref -d \$(pwd)/!{fasta.baseName}.horiz
modeller_script_singularity.py -l model check -s proq_standalone mypmfs -f !{fasta} -r ref/!{fasta.baseName}/ -pd !{params.database}/!{fam}/!{fam}_ref_pdb.faa -pr !{params.cleaned_pdb} -k !{params.proq} !{params.mypmfs} -j !{params.psipred}/ -q !{params.modelling_quality} -n !{params.model} -nb !{params.template} -t !{task.cpus} -pi blastp -smypmfs !{params.trained_data}
# tneg
modeller_script_singularity.py -l model check -s proq_standalone mypmfs -f !{fasta} -r tneg/!{fasta.baseName}/ -pd !{params.database}/!{fam}/!{fam}_tneg_pdb.faa -pr !{params.cleaned_pdb} -k !{params.proq} !{params.mypmfs} -j !{params.psipred}/ -q !{params.modelling_quality} -n !{params.model} -nb !{params.template} -t !{task.cpus} -pi blastp -smypmfs !{params.trained_data}
# Get the best model for ref
summary_ref=\$(ls -1 ref/!{fasta.baseName}//modeller_summary_*.csv 2>/dev/null |head -1)
# Check ref file
if [ -f "\$summary_ref" ]
then
cp ref/!{fasta.baseName}//\$(sed -n 2p \$summary_ref |cut -f 1) best_model_ref/
else
echo "\$summary_ref file is missing"
fi
# Get the best model for tneg
summary_tneg=\$(ls -1 tneg/!{fasta.baseName}//modeller_summary_*.csv 2>/dev/null |head -1)
# Check tneg file
if [ -f "\$summary_tneg" ]
then
cp tneg/!{fasta.baseName}//\$(sed -n 2p \$summary_tneg |cut -f 1) best_model_tneg/
else
echo "No negative simulation were obtained, we create empty files for nextflow"
touch tneg/!{fasta.baseName}/empty_neg.horiz best_model_tneg/empty_neg.pdb tneg/!{fasta.baseName}/result_mypmfs_empty_neg.csv tneg/!{fasta.baseName}/modeller_summary_empty_neg.csv
fi
"""
}
process prosa_check {
tag "${fasta.baseName}:${fam}"
publishDir "$myDir/modelling/${fam}_candidates/", mode: 'copyNoFollow', pattern: "*/*/result_prosa_*"
errorStrategy 'retry'
maxRetries 10
// label 'modelling'
input:
set val(fam), file(fasta), horiz_ref, best_pdb_ref, proq_ref, mypmfs_ref, summary_ref, horiz_tneg, best_pdb_tneg, proq_tneg, mypmfs_tneg, summary_tneg from modelChannel
output:
set val(fam), file(fasta), best_pdb_ref, proq_ref, mypmfs_ref, file("ref/*/result_prosa_*"), summary_ref, best_pdb_tneg, proq_tneg, mypmfs_tneg, file("tneg/*/result_prosa_*"), summary_tneg into extractChannel
set val(fam), file(fasta), best_pdb_ref, best_pdb_tneg into structuralAlignmentChannel
shell:
"""
mkdir -p ref/!{fasta.baseName}/ tneg/!{fasta.baseName}/
modeller_script_singularity.py -s prosa -l check -sm !{summary_ref} -d !{horiz_ref}
if [ !{horiz_tneg} != "empty_neg.horiz" ]
then
modeller_script_singularity.py -s prosa -l check -sm !{summary_tneg} -d !{horiz_tneg}
else
touch tneg/!{fasta.baseName}/result_prosa_empty_neg.csv
fi
cp \$(dirname !{summary_ref})/result_prosa_* ref/!{fasta.baseName}/
cp \$(dirname !{summary_tneg})/result_prosa_* tneg/!{fasta.baseName}/
"""
}
process extract_result {
tag "${fasta.baseName}:${fam}"
errorStrategy 'retry'
maxRetries 2
input:
set val(fam), file(fasta), best_pdb_ref, proq_ref, mypmfs_ref, prosa_ref, summary_ref, best_pdb_tneg, proq_tneg, mypmfs_tneg, prosa_tneg, summary_tneg from extractChannel
output:
file("res_ref_summary.tsv") into refSummaryChannel
file("res_tneg_summary.tsv") into tnegSummaryChannel
shell:
"""
# Reference
best_model_ref=\$(tail -n +2 !{summary_ref} |head -1 |cut -s -f1|sed -e "s/\\r//g"|sed "s/.pdb//g")
dope_ref=\$(tail -n +2 !{summary_ref} |head -1 |cut -s -f3|sed -e 's/\\r//g')
molpdf_ref=\$(tail -n +2 !{summary_ref} |head -1 |cut -s -f2|sed -e 's/\\r//g')
normalized_dope_ref=\$(tail -n +2 !{summary_ref} |head -1 |cut -s -f4|sed -e 's/\\r//g')
GA341_score_ref=\$(tail -n +2 !{summary_ref} |head -1 |cut -s -f5|sed -e 's/\\r//g')
zscore_ref=\$(tail -n +2 !{prosa_ref} |head -1 |awk '{print \$2}'|sed -e 's/\\r//g' )
maxsub_ref=\$(tail -n +2 !{proq_ref} |head -1 |awk '{print \$2}'| sed -e 's/\\r//g' )
lgscore_ref=\$(tail -n +2 !{proq_ref} |head -1 |awk '{print \$3}'| sed -e 's/\\r//g')
pseudo_energy_ref=\$(tail -n +2 !{mypmfs_ref} |head -1 |awk '{print \$2}'| sed -e 's/\\r//g')
echo -e "\$best_model_ref\t!{fam}\t\$molpdf_ref\t\$dope_ref\t\$normalized_dope_ref\t\$GA341_score_ref\t\$zscore_ref\t\$maxsub_ref\t\$lgscore_ref\t\$pseudo_energy_ref" > res_ref_summary.tsv
# Negative
if [ !{summary_tneg} != "tneg/!{fasta.baseName}/modeller_summary_empty_neg.csv" ]
then
best_model_tneg=\$(tail -n +2 !{summary_tneg} |head -1 |cut -s -f1|sed -e "s/\\r//g"|sed "s/.pdb//g")
dope_tneg=\$(tail -n +2 !{summary_tneg} |head -1 |cut -s -f3|sed -e 's/\\r//g')
molpdf_tneg=\$(tail -n +2 !{summary_tneg} |head -1 |cut -s -f2|sed -e 's/\\r//g')
normalized_dope_tneg=\$(tail -n +2 !{summary_tneg} |head -1 |cut -s -f4|sed -e 's/\\r//g')
GA341_score_tneg=\$(tail -n +2 !{summary_tneg} |head -1 |cut -s -f5|sed -e 's/\\r//g')
zscore_tneg=\$(tail -n +2 !{prosa_tneg} |head -1 |awk '{print \$2}'|sed -e 's/\\r//g' )
maxsub_tneg=\$(tail -n +2 !{proq_tneg} |head -1 |awk '{print \$2}'| sed -e 's/\\r//g' )
lgscore_tneg=\$(tail -n +2 !{proq_tneg} |head -1 |awk '{print \$3}'| sed -e 's/\\r//g')
pseudo_energy_tneg=\$(tail -n +2 !{mypmfs_tneg} |head -1 |awk '{print \$2}'| sed -e 's/\\r//g')
echo -e "\$best_model_tneg\t!{fam}\t\$molpdf_tneg\t\$dope_tneg\t\$normalized_dope_tneg\t\$GA341_score_tneg\t\$zscore_tneg\t\$maxsub_tneg\t\$lgscore_tneg\t\$pseudo_energy_tneg" > res_tneg_summary.tsv
else
echo -e "\$best_model_tneg\t!{fam}\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0\t0.0" > res_tneg_summary.tsv
fi
"""
}
// run compute_structural_alignment.sh
process structural_alignment {
tag "${fasta.baseName}:${fam}"
publishDir "$myDir/modelling/${fam}_candidates/", mode: 'copyNoFollow', pattern: '*/struct_matrix_*.tsv'
errorStrategy 'retry'
maxRetries 10
// cpus params.cpu
input:
set val(fam), file(fasta), best_pdb_ref, best_pdb_tneg from structuralAlignmentChannel
output:
//file("*/struct_matrix_*.tsv") into structAliChannel
file("*_candidates_ref_vs_ref/struct_matrix_mammoth.tsv") into refMammothChannel
file("*_candidates_ref_vs_ref/struct_matrix_TMalign.tsv") into refTMalignChannel
file("*_candidates_tneg_vs_tneg/struct_matrix_mammoth.tsv") into tnegMammothChannel
file("*_candidates_tneg_vs_tneg/struct_matrix_TMalign.tsv") into tnegTMalignChannel
shell:
"""
mkdir !{fam}_candidates_ref_vs_ref/ !{fam}_candidates_tneg_vs_tneg/
PDBRMSD.py -l !{fam} -q !{best_pdb_ref} -t ${params.database}/!{fam}/ref_pdb/ -s TMalign mammoth -p !{params.tmalign_dir} !{params.mammoth_dir} -r !{fam}_candidates_ref_vs_ref/ -b 1 -n !{task.cpus}
PDBRMSD.py -l !{fam} -q !{best_pdb_tneg} -t ${params.database}/!{fam}/tneg_pdb/ -s TMalign mammoth -p !{params.tmalign_dir} !{params.mammoth_dir} -r !{fam}_candidates_tneg_vs_tneg/ -b 1 -n !{task.cpus}
"""
}
modelling_by_ref = refSummaryChannel.collectFile(name: 'candidates_by_ref.tsv')
modelling_by_ref.into { modelling_by_ref_tosave; modelling_by_ref_toprocess}
modelling_by_ref_tosave.subscribe { it.copyTo(modDir) }
modelling_by_tneg = tnegSummaryChannel.collectFile(name: 'candidates_by_tneg.tsv')
modelling_by_tneg.into { modelling_by_tneg_tosave; modelling_by_tneg_toprocess}
modelling_by_tneg_tosave.subscribe { it.copyTo(modDir) }
mammoth_by_ref = refMammothChannel.collectFile(name: 'mammoth_by_ref.tsv')
mammoth_by_ref.into { mammoth_by_ref_tosave; mammoth_by_ref_toprocess}
mammoth_by_ref_tosave.subscribe { it.copyTo(modDir) }
tmalign_by_ref = refTMalignChannel.collectFile(name: 'TMalign_by_ref.tsv')
tmalign_by_ref.into { tmalign_by_ref_tosave; tmalign_by_ref_toprocess}
tmalign_by_ref_tosave.subscribe { it.copyTo(modDir) }
mammoth_by_tneg = tnegMammothChannel.collectFile(name: 'mammoth_by_tneg.tsv')
mammoth_by_tneg.into { mammoth_by_tneg_tosave; mammoth_by_tneg_toprocess}
mammoth_by_tneg_tosave.subscribe { it.copyTo(modDir) }
tmalign_by_tneg = tnegTMalignChannel.collectFile(name: 'TMalign_by_tneg.tsv')
tmalign_by_tneg.into { tmalign_by_tneg_tosave; tmalign_by_tneg_toprocess}
tmalign_by_tneg_tosave.subscribe { it.copyTo(modDir) }
process build_matrix {
input:
file(mamref) from mammoth_by_ref_toprocess
file(mamtneg) from mammoth_by_tneg_toprocess
file(tmref) from tmalign_by_ref_toprocess
file(tmtneg) from tmalign_by_tneg_toprocess
file(modref) from modelling_by_ref_toprocess
file(modtneg) from modelling_by_tneg_toprocess
file(association) from assoconvChannel
output:
file("pcm_result.tsv") into matrixChannel
file("pcm_result.tsv") into matrixoutChannel
shell:
"""
compute_matrix.py -sr !{modref} -sn !{modtneg} -ar !{mamref} !{tmref} -an !{mamtneg} !{tmtneg} -n !{association} -o pcm_result.tsv
"""
}
process lineartest {
cache false
input:
file(matrix) from matrixChannel
output:
file("result.html") into predhtmloutChannel
file("prediction_output.tsv") into predoutChannel
shell:
"""
#!/usr/bin/env Rscript
library(rmarkdown)
#x <- read.csv2("!{params.universal_model}")[,c(3:4,6:13)]
x <- read.csv2("!{params.universal_model}", sep="\\t", dec = ".")[,c(3:4,6:14)]
y <- read.csv2("!{params.universal_model}", sep="\\t", dec = ".")[,2]
pcm <- read.delim("!{matrix}")
predout <- paste0(getwd(), "/prediction_output.tsv")
rmarkdown::render("!{params.evotarmd}", output_file ="result.html", output_dir=getwd(), params=list(x,y, pcm, predout),
output_format="html_document")
"""
}
predhtmloutChannel.subscribe { it.copyTo("${params.predhtmlout}") }
predoutChannel.subscribe { it.copyTo("${params.predout}") }
matrixoutChannel.subscribe { it.copyTo("${params.matrixout}") }
println "Project : $workflow.projectDir"
println "Cmd line: $workflow.commandLine"