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main.nf
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#!/usr/bin/env nextflow
/*
========================================================================================
samba
========================================================================================
samba Analysis Pipeline.
#### Homepage / Documentation
https://github.com/ifremer-bioinformatics/samba
----------------------------------------------------------------------------------------
*/
def helpMessage() {
// Add to this help message with new command line parameters
log.info SeBiMERHeader()
log.info"""
Usage:
The typical command for running the pipeline after filling the conf/custom.config file is as follows:
nextflow run main.nf -profile <docker,singularity,conda>,custom
Mandatory arguments:
--excel_sample_file [file] Path to XLS input file containing two sheets named "manifest" and "metadata".
or
--input_metadata [file] Path to input file with project samples metadata (csv format).
--input_manifest [file] Path to input file with samples reads files paths (csv format).
-profile [str] Configuration profile to use. Can use multiple (comma separated).
Available: conda, docker, singularity, test, custom.
Generic:
--singleEnd [bool] Set to true to specify that the inputs are single-end reads.
--longreads [bool] Set to true to specify that the inputs are long reads (Nanopore/Pacbio) (default = false for illumina short reads).
--compress_result [bool] Zip the final result directory (default = true)
Other options
--outdir [path] The output directory where the results will be saved.
-w/--work-dir The temporary directory where intermediate data will be saved.
--email [email] Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits
--email_on_fail [email] Same as --email, except only send mail if the workflow is not successful
-name [str] Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.
--projectName [str] Name of the project being analyzed.
Data integrity:
--data_integrity_enable [bool] Data integrity checking step. Set to false to deactivate this step. (default = true)
--primer_filter [str] Percentage of primers supposed to be found in raw reads (default : 70).
Raw reads cleaning:
--cutadapt_enable [bool] Primer removal process. Set to false to deactivate this step. (default = true)
--primerF [str] Forward primer (to be used in Cutadapt cleaning step).
--primerR [str] Reverse primer (to be used in Cutadapt cleaning step).
--errorRate [str] Cutadapt error rate allowed to match primers (default : 0.1).
--overlap [str] Cutadapt overlaping length between primer and read (default : 18 for test dataset, must be changed for user dataset).
ASVs inference:
--FtrimLeft [str] The number of nucleotides to remove from the start of each forward read (default : 0 = no trimming).
--RtrimLeft [str] The number of nucleotides to remove from the start of each reverse read (default : 0 = no trimming).
--FtruncLen [str] Truncate forward reads after FtruncLen bases. Reads shorter than this are discarded (default : 0 = no trimming).
--RtruncLen [str] Truncate reverse reads after RtruncLen bases. Reads shorter than this are discarded (default : 0 = no trimming).
--FmaxEE [str] Forward reads with higher than maxEE "expected errors" will be discarded (default = 2).
--RmaxEE [str] Reverse with higher than maxEE "expected errors" will be discarded (default = 2).
--minQ [str] Truncate reads at the first instance of a quality score less than or equal to minQ (default = 2).
--chimeras [str] Chimera detection method : default = "consensus". Set to "pooled" if the samples in the sequence table are all pooled together for bimera identification.
Merge ASVs tables:
--dada2merge [bool] Set to true to merge DADA2 ASVs tables.
--merge_tabledir [path] Path to the directory containing the ASVs tables to merge (this directory must contain only the ASVs tables to merge).
--merge_repseqsdir [path] Path to the directory containing the representative sequences to merge (this directory must constain only the representative sequences to merge).
Distribution based-clustering:
--dbotu3_enable [bool] Distribution based-clustering step. Set to false to deactivate this step. (default = true)
--gen_crit [str] dbotu3 Genetic criterion (default = 0.1).
--abund_crit [str] dbotu3 Abundance criterion (default = 10).
--pval_crit [str] dbotu3 P-value criterion (default = 0.0005).
Taxonomic assignation:
--extract_db [bool] Set to true to extract specific region from reference database (default = false)
--seqs_db [file] Path to reference database (required if extract_db = true).
--taxo_db [file] Path to taxonomic reference database (required if extract_db = true).
--database [file] Path to preformatted QIIME2 format database (required if extract_db = false).
--confidence [str] Confidence threshold for limiting taxonomic depth. Set to "disable" to disable confidence calculation, or 0 to calculate confidence but not apply it to limit the taxonomic depth of the assignments (default = 0.9).
Taxonomy filtering:
--filtering_tax_enable [bool] Set to true to filter asv table and sequences based on taxonomic assignation (default = false)
--tax_to_exclude [str] List of taxa you want to exclude (comma-separated list).
--tax_to_include [str] List of taxa you want to include (comma-separated list).
Decontamination:
--microDecon_enable [bool] Sample decontamination step. Set to true to activate this step. (default = false)
--control_list [str] Comma separated list of control samples (e.g : "sample1,sample4,sample7") (required if microDecon_enable = true).
--nb_controls [str] Number of control sample listed (required if microDecon_enable = true).
--nb_samples [str] Number of samples that are not control samples (required if microDecon_enable = true).
Predict functionnal abundance:
--picrust2_enable [bool] Set to true to enable functionnal prediction step. (default = false)
--picrust_var [str] According to your metadata file, list the column names corresponding to the variables to group samples for functional predictions (comma-separated list).
--method [str] HSP method of your choice. (default = 'mp' ) The most accurate prediction methode. Faster method: 'pic'.
--nsti [str] Max NSTI value accepted. (default = 2) NSTI cut-off of 2 should eliminate junk sequences.
Differential abundance testing:
--ancom_var [str] According to your metadata file, list the column names corresponding to the variables to group samples for ANCOM analysis (comma-separated list).
Samples removing:
--remove_sample [bool] Set to true to enable samples removing. (default = false)
--sample_to_remove [str] List of samples you to remove (comma-separated list).
Statistics:
--stats_alpha_enable [bool] Set to false to deactivate Alpha diversity statistics step. (default = true)
--stats_beta_enable [bool] Set to false to deactivate Beta diversity statistics steps. (default = true)
--stats_desc_comp_enable [bool] Set to false to deactivate Descriptive comparisons steps. (default = true)
--kingdom [str] Kingdom to be displayed in barplots.
--taxa_nb [str] Number of taxa to be displayed in barplots.
--alpha_div_group [str] According to your metadata file, list the column names corresponding to the variables to group samples for Alpha diversity (comma-separated list).
--beta_div_var [str] According to your metadata file, select the column name corresponding to the variable of interest for Beta diversity (comma-separated list).
--desc_comp_crit [str] According to your metadata file, select the column name corresponding to the variable of interest for Descriptive comparisons graphs (comma-separated list).
--hc_method [str] Hierarchical clustering method (default = 'ward.D2').
--stats_only [bool] Perform only statistical analysis (ASV table and newick tree required). Set to true to activate. (default = false)
--inasv_table [file] if stats_only is activated, set the path to your own ASV table in tsv format.
--innewick [file] if stats_only is activated, set the path to your own phylogenetic tree in newick format.
Final analysis report:
--report_enable [bool] Set to false to deactivate report creation. (default = true)
Long reads options:
--lr_type [str] Long reads technology. For pacbio, [map-pb] and for nanopore, [map-ont]
--lr_tax_fna [file] Path to reference database indexed with Minimap2 (required).
--lr_taxo_flat [file] Path to taxonomic reference file (required).
--lr_rank [int] Minimal rank level to keep a hit as assigned [5]. 1:Kingdom, 2:Phylum, 3:Class, 4:Order, 5:Family, 6:Genus, 7:Species
""".stripIndent()
}
// Show help message
if (params.help) {
helpMessage()
exit 0
}
/*
* SET UP CONFIGURATION VARIABLES
*/
import java.text.SimpleDateFormat
def d = new Date()
def dtFormat = "dd/MM/yyyy"
def date = d.format(dtFormat, TimeZone.getTimeZone('GMT+2'))
// Has the run name been specified by the user?
// this has the bonus effect of catching both -name and --name
custom_runName = params.name
if (!(workflow.runName ==~ /[a-z]+_[a-z]+/)) {
custom_runName = workflow.runName
}
//Copy config files to output directory for each run
paramsfile = file("$baseDir/conf/base.config", checkIfExists: true)
paramsfile.copyTo("$params.outdir/conf/base.config")
if (workflow.profile.contains('shortreadstest')) {
testparamsfile = file("$baseDir/conf/shortreadstest.config", checkIfExists: true)
testparamsfile.copyTo("$params.outdir/conf/shortreadstest.config")
}
if (workflow.profile.contains('longreadstest')) {
testparamsfile = file("$baseDir/conf/longreadstest.config", checkIfExists: true)
testparamsfile.copyTo("$params.outdir/conf/longreadstest.config")
}
if (workflow.profile.contains('custom')) {
customparamsfile = file("$baseDir/conf/custom.config", checkIfExists: true)
customparamsfile.copyTo("$params.outdir/conf/custom.config")
}
// If XLS is used as input file
if (!params.excel_sample_file.isEmpty()) {
excel_sample_file_ch = Channel.fromPath(params.excel_sample_file, checkIfExists:true)
}
//If only running the stats processes of the pipeline
if (params.stats_only) {
if (!params.inasv_table || params.inasv_table.isEmpty()) {
log.error "Parameter --inasv_table cannot be null or empty. Set the path to the ASV count table"
exit 1
} else {
inasv_table_ch = Channel.fromPath(params.inasv_table, checkIfExists:true)
.set { tsv_only }
}
if (!params.innewick || params.innewick.isEmpty()) {
log.error "Parameter --innewick cannot be null or empty. Set the path to the newick tree"
exit 1
} else {
newick_ch = Channel.fromPath(params.innewick, checkIfExists:true)
.set { newick_only }
}
//Force to false other processes options
params.dada2merge = false
params.data_integrity_enable = false
params.cutadapt_enable = false
params.dbotu3_enable = false
params.filtering_tax_enable = false
params.microDecon_enable = false
params.picrust2_enable = false
} else {
params.inasv_table = ""
params.innewick = ""
inasv_table_ch = Channel.empty()
newick_ch = Channel.empty()
}
//If pipeline runs for merging dada2 data
if (params.dada2merge) {
if (!params.merge_tabledir || params.merge_tabledir.isEmpty()) {
log.error "Parameter --merge_tabledir cannot be null or empty. Set the path to the folder with ASV tables to merge"
exit 1
} else {
dada2merge_tabledir_ch = Channel.fromPath(params.merge_tabledir, checkIfExists:true)
}
if (!params.merge_repseqsdir || params.merge_repseqsdir.isEmpty()) {
log.error "Parameter --merge_repseqsdir cannot be null or empty. Set the path to the folder with ASV sequences to merge"
exit 1
} else {
dada2merge_repseqsdir_ch = Channel.fromPath(params.merge_repseqsdir, checkIfExists:true)
}
//Force to false other processes options
params.data_integrity_enable = false
params.cutadapt_enable = false
params.dbotu3_enable = false
params.filtering_tax_enable = false
params.microDecon_enable = false
params.picrust2_enable = false
params.stats_only = false
} else {
params.merge_tabledir = ""
params.merge_repseqsdir = ""
dada2merge_tabledir_ch = Channel.empty()
dada2merge_repseqsdir_ch = Channel.empty()
}
//If taxonomy step require to extract primer regions
if (!params.longreads && !params.stats_only) {
if (params.extract_db) {
if (!params.seqs_db || params.seqs_db.isEmpty()) {
log.error "Parameter --seqs_db cannot be null or empty. Set the path to the reference sequences"
exit 1
}
if (!params.taxo_db || params.taxo_db.isEmpty()) {
log.error "Parameter --taxo_db cannot be null or empty. Set the path to the reference taxonomy"
exit 1
}
} else {
if (!params.database || params.database.isEmpty()) {
log.error "Parameter --database cannot be null or empty. Set the path to the reference database"
}
}
}
if (params.microDecon_enable && !params.control_list) {
log.error "ERROR : A comma separated list of control samples (--control_list) must be set."
exit 1
}
if (params.longreads) {
//Force to false other processes options
params.data_integrity_enable = false
params.cutadapt_enable = false
params.qiime2 = false
params.dada2merge = false
params.dbotu3_enable = false
params.filtering_tax_enable = false
params.microDecon_enable = false
params.ancom_enable = false
params.asv = false
params.picrust2_enable = false
// Initialized variables not used in long reads analysis
params.primerF = ""
params.primerR = ""
params.errorRate = ""
params.overlap = ""
params.FtrimLeft = ""
params.RtrimLeft = ""
params.FtruncLen = ""
params.RtruncLen = ""
params.FmaxEE = ""
params.RmaxEE = ""
params.minQ = ""
params.chimeras = ""
params.database = ""
params.seqs_db = ""
params.taxo_db = ""
params.tax_to_exclude = ""
params.nb_controls = ""
params.nb_samples = ""
params.remove_sample = false
params.sample_to_remove = ""
params.ancom_var = ""
} else {
// Initialized variables not used in short reads analysis
params.lr_rank = ""
params.lr_type = ""
params.lr_tax_fna = ""
params.lr_taxo_flat = ""
}
if (!params.microDecon_enable) {
params.nb_controls = "0"
params.nb_samples = ""
params.control_list = "none"
}
if (!params.stats_alpha_enable) {
params.alpha_div_group = ""
params.kingdom = ""
params.taxa_nb = ""
}
if (!params.stats_beta_enable) {
params.beta_div_var = ""
params.hc_method = ""
}
if (!params.stats_desc_comp_enable) {
params.desc_comp_crit = ""
params.desc_comp_tax_level = ""
}
if (!params.filtering_tax_enable) {
params.tax_to_exclude = ""
}
if (!params.remove_sample) {
params.sample_to_remove = ""
}
if (!params.extract_db) {
params.seqs_db = ""
params.taxo_db = ""
}
if (!params.picrust2_enable) {
params.picrust_var = ""
params.method = ""
params.nsti = ""
}
/*
* PIPELINE INFO
*/
// Header log info
log.info SeBiMERHeader()
def summary = [:]
if (workflow.revision) summary['Pipeline Release'] = workflow.revision
summary['Run Name'] = custom_runName ?: workflow.runName
summary['Project Name'] = params.projectName
if (!params.excel_sample_file.isEmpty()) {
summary['Sample File'] = params.excel_sample_file
} else {
summary['Manifest File'] = params.input_manifest
summary['Metadata File'] = params.input_metadata
}
summary['Data Type'] = params.singleEnd ? 'Single-End' : 'Paired-End'
if (workflow.containerEngine) summary['Container'] = "$workflow.containerEngine"
summary['Output dir'] = params.outdir
summary['Launch dir'] = workflow.launchDir
summary['Working dir'] = workflow.workDir
summary['Script dir'] = workflow.projectDir
summary['User'] = workflow.userName
summary['Config Profile'] = workflow.profile
if (params.stats_only) summary['Stats only'] = "Pipeline running only statistics processes"
if (params.data_integrity_enable) summary['Data integrity'] = "Data integrity checking process enabled"
if (params.cutadapt_enable) summary['Primer removal'] = "Primer removal process enabled"
if (params.dbotu3_enable) summary['Clustering'] = "Distribution based-clustering process enabled"
if (params.filtering_tax_enable) summary['Tax filtering'] = "Filtering ASV table and sequences based on taxonomic assignation"
if (params.microDecon_enable) summary['Decontamination'] = "Sample decontamination process enabled"
if (params.dada2merge) summary['Dada2 merge'] = "Dada2 merge process enabled"
if (params.picrust2_enable) summary['Prediction'] = "Functionnal prediction process enabled"
if (params.ancom_enable) summary['Ancom'] = "Differential abundance ANCOM analysis process enabled"
if (params.stats_alpha_enable) summary['Alpha div'] = "Alpha diversity indexes process enabled"
if (params.stats_beta_enable) summary['Beta div'] = "Beta diversity statistics processes enabled"
if (params.stats_desc_comp_enable) summary["Desc comp"] = "Descriptive comparisons statistics process enabled"
if (params.longreads) summary["Long reads"] = "Going to run long reads processes"
if (params.email || params.email_on_fail) {
summary['E-mail Address'] = params.email
summary['E-mail on failure'] = params.email_on_fail
}
log.info summary.collect { k,v -> "${k.padRight(18)}: $v" }.join("\n")
log.info "-\033[2m--------------------------------------------------\033[0m-"
// Check the hostnames against configured profiles
checkHostname()
Channel.from(summary.collect{ [it.key, it.value] })
.map { k,v -> "<dt>$k</dt><dd><samp>${v ?: '<span style=\"color:#999999;\">N/A</a>'}</samp></dd>" }
.reduce { a, b -> return [a, b].join("\n ") }
.map { x -> """
id: 'samba-summary'
description: " - this information is collected when the pipeline is started."
section_name: 'samba Workflow Summary'
section_href: 'https://github.com/ifremer-bioinformatics/samba'
plot_type: 'html'
data: |
<dl class=\"dl-horizontal\">
$x
</dl>
""".stripIndent() }
.set { ch_workflow_summary }
// Check the hostnames against configured profiles
checkHostname()
/*
* STEP 0 - Get test data if running in test profile
*/
if (workflow.profile.contains('test')) {
process get_test_data {
label 'internet_access'
output :
file 'data_is_ready' into ready_excel2tsv, ready_integrity, ready_import, ready_lr
file 'manifest' into testmanifest
file 'metadata' into testmetadata
when :
!params.stats_only && !params.dada2merge
script :
def datatype = params.longreads ? "longreads" : "shortreads"
"""
get_test_data.sh ${baseDir} 'data_is_ready' ${datatype} 'manifest' 'metadata' &> get_test_data.log 2>&1
"""
}
if (!params.stats_only || !params.dada2merge) {
if (params.longreads) {
testmetadata.set { metadata4stats }
} else {
testmanifest.into { manifest ; manifest4integrity }
testmetadata.into { metadata4dada2 ; metadata4dbotu3 ; metadata_filtering_tax ; metadata4stats ; metadata4integrity ; metadata4picrust2 ; metadata4ancom }
}
}
} else {
data_ready = Channel.value("none")
data_ready.into { ready_excel2tsv ; ready_integrity ; ready_import ; ready_lr}
if (params.excel_sample_file.isEmpty()) {
if (!params.stats_only || !params.dada2merge) {
// Check if input metadata file exits
if (!params.input_metadata || params.input_metadata.isEmpty()) {
log.error "Parameter --input_metadata cannot be null or empty. Set the path to the Metadata file."
exit 1
} else {
Channel.fromPath(params.input_metadata, checkIfExists:true)
.into { metadata4dada2 ; metadata4dbotu3 ; metadata_filtering_tax ; metadata4stats ; metadata4integrity ; metadata4picrust2 ; metadata4ancom }
}
if (!params.input_manifest || params.input_manifest.isEmpty()) {
log.error "Parameter --input_manifest cannot be null or empty. Set the path to the Manifest file."
exit 1
} else {
Channel.fromPath(params.input_manifest, checkIfExists:true)
.into { manifest ; manifest4integrity }
}
} else {
Channel.empty().into { manifest ; manifest4integrity }
}
}
}
/*
* STEP 1 - Checking data integrity
*/
if (!params.longreads) {
if (!params.excel_sample_file.isEmpty()) {
/* Format Excel file to tsv */
process excel2tsv {
label 'biopython_env'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'q2_manifest.sort.tsv'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'q2_metadata.sort.tsv'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'excel2tsv.log'
input :
file xls from excel_sample_file_ch
file ready from ready_excel2tsv
output :
file "q2_metadata.sort.tsv" into metadata_xls
file "q2_manifest.sort.tsv" into manifest_xls
file "excel2tsv.log"
when :
!params.stats_only && !params.dada2merge
script :
def datatype = params.singleEnd ? "single" : "paired"
"""
excel2tsv.py -x ${xls} -s ${datatype} &> excel2tsv.log 2>&1
"""
}
}
integrity_manifest = params.excel_sample_file.isEmpty() ? manifest4integrity : manifest_xls
integrity_metadata = params.excel_sample_file.isEmpty() ? metadata4integrity : metadata_xls
if (params.data_integrity_enable) {
/* Check data integrity */
process data_integrity {
label 'biopython_env'
publishDir "${params.outdir}/${params.data_integrity_dirname}", mode: 'copy', pattern: 'data_integrity.txt'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'data_integrity.txt'
publishDir "${params.outdir}/${params.data_integrity_dirname}", mode: 'copy', pattern: '*.sort'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: '*.sort'
input :
file manifest from integrity_manifest
file metadata from integrity_metadata
file ready from ready_integrity
output :
file 'data_integrity.txt'
file "q2_metadata.sort.tsv" into metadata_sort
file "q2_manifest.sort.tsv" into manifest_sort
when :
params.data_integrity_enable && !params.stats_only && !params.dada2merge
script :
def datatype = params.singleEnd ? "single" : "paired"
"""
data_integrity.py -e ${metadata} -a ${manifest} -p ${params.primer_filter} -s ${datatype} -t ${task.cpus} -c ${params.control_list} &> data_integrity.log 2>&1
"""
}
metadata_sort.into { metadata4dada2 ; metadata4dbotu3 ; metadata_filtering_tax ; metadata4stats ; metadata4picrust2 ; metadata4ancom }
manifest_sort.set { manifest }
} else {
metadata_xls.into { metadata4dada2 ; metadata4dbotu3 ; metadata_filtering_tax ; metadata4stats ; metadata4picrust2 ; metadata4ancom }
manifest_xls.set { manifest }
}
/*
* STEP 2 - Import metabarcoding data into QIIME2 object
*/
process q2_import {
label 'qiime2_env1cpu'
publishDir "${params.outdir}/${params.import_dirname}", mode: 'copy', pattern: 'data.qz*'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: '*_output'
publishDir "${params.outdir}/${params.report_dirname}/version", mode: 'copy', pattern: 'v_*.txt'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_import -> "cmd/${task.process}_complete.sh" }
input :
file q2_manifest from manifest
file ready from ready_import
output :
file 'data.qza' into imported_data
file 'data.qzv' into imported_visu
file 'import_output' into imported_output
file 'completecmd' into complete_cmd_import
file 'v_qiime2.txt' into qiime2_version
when :
!params.stats_only && !params.dada2merge
script :
"""
q2_import.sh ${params.singleEnd} ${q2_manifest} data.qza data.qzv import_output completecmd &> q2_import.log 2>&1
qiime --version|grep 'q2cli'|cut -d' ' -f3 > v_qiime2.txt
"""
}
/*
* STEP 3 - Trim metabarcode data with cutadapt
*/
if (params.cutadapt_enable) {
process q2_cutadapt {
label 'qiime2_env'
publishDir "${params.outdir}/${params.trimmed_dirname}", mode: 'copy', pattern: 'data*.qz*'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: '*_output'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_cutadapt -> "cmd/${task.process}_complete.sh" }
input :
file imported_data from imported_data
output :
file 'data_trimmed.qza' into trimmed_data
file 'data_trimmed.qzv' into trimmed_visu
file 'trimmed_output' into trimmed_output
file 'completecmd' into complete_cmd_cutadapt
when :
params.cutadapt_enable && !params.stats_only && !params.dada2merge
script :
"""
q2_cutadapt.sh ${params.singleEnd} ${task.cpus} ${imported_data} ${params.primerF} ${params.primerR} ${params.errorRate} ${params.overlap} data_trimmed.qza data_trimmed.qzv trimmed_output completecmd &> q2_cutadapt.log 2>&1
"""
}
}
dada2_input = params.cutadapt_enable ? trimmed_data : imported_data
/*
* STEP 4 - Dada2 ASVs inference
*/
process q2_dada2 {
label 'qiime2_env'
publishDir "${params.outdir}/${params.dada2_dirname}", mode: 'copy', pattern: '*.qz*'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: '*_output'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_dada2 -> "cmd/${task.process}_complete.sh" }
input :
file dada2_input from dada2_input
file metadata from metadata4dada2
output :
file 'rep_seqs.qza' into dada2_seqs_dbotu3, dada2_seqs_taxo, dada2_seqs_filtering_tax, dada2_seqs_decontam, dada2_seqs_phylo, dada2_seqs_picrust2, dada2_seqs_ancom
file 'rep_seqs.qzv' into visu_repseps
file 'table.qza' into dada2_table_dbotu3, dada2_table_filtering_tax, dada2_table_picrust2, dada2_table_ancom
file 'table.qzv' into visu_table
file 'stats.qza' into stats_table
file 'stats.qzv' into visu_stats
file 'dada2_output' into dada2_output
file 'completecmd' into complete_cmd_dada2
when :
!params.stats_only && !params.dada2merge
script :
"""
q2_dada2.sh ${params.singleEnd} ${dada2_input} ${metadata} rep_seqs.qza rep_seqs.qzv table.qza table.qzv stats.qza stats.qzv dada2_output ${params.FtrimLeft} ${params.RtrimLeft} ${params.FtruncLen} ${params.RtruncLen} ${params.FmaxEE} ${params.RmaxEE} ${params.minQ} ${params.chimeras} ${task.cpus} completecmd &> q2_dada2.log 2>&1
"""
}
/*
* STEP 5 - Distribution based-clustering with dbotu3
*/
if (params.dbotu3_enable) {
/* Run dbotu3 */
process q2_dbotu3 {
label 'qiime2_env'
publishDir "${params.outdir}/${params.dbotu3_dirname}", mode: 'copy', pattern: '*.qz*'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: '*_output'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_dbotu3 -> "cmd/${task.process}_complete.sh" }
input :
file table from dada2_table_dbotu3
file seqs from dada2_seqs_dbotu3
file metadata from metadata4dbotu3
output :
file 'dbotu3_details.txt' into dbotu3_details
file 'dbotu3_seqs.qza' into dbotu3_seqs_decontam, dbotu3_seqs_taxo, dbotu3_seqs_filtering_tax, dbotu3_seqs_phylo, dbotu3_seqs_picrust2, dbotu3_seqs_ancom
file 'dbotu3_seqs.qzv' into dbotu3_seqs_visu
file 'dbotu3_table.qza' into dbotu3_table, dbotu3_table_filtering_tax, dbotu3_table_picrust2, dbotu3_table_ancom
file 'dbotu3_table.qzv' into dbotu3_table_visu
file 'dbotu3_output' into dbotu3_output
file 'completecmd' into complete_cmd_dbotu3
when :
!params.stats_only && !params.dada2merge && params.dbotu3_enable
script :
"""
q2_dbotu3.sh ${table} ${seqs} ${metadata} dbotu3_details.txt dbotu3_seqs.qza dbotu3_seqs.qzv dbotu3_table.qza dbotu3_table.qzv dbotu3_output ${params.gen_crit} ${params.abund_crit} ${params.pval_crit} completecmd &> q2_dbotu3.log 2>&1
"""
}
}
/*
* STEP 6 - Use Dada2 merge to merge ASVs tables and sequences
*/
if (params.dada2merge) {
Channel.fromPath(params.input_metadata, checkIfExists:true)
.set { metadata_merge_ch }
process q2_dada2_merge {
label 'qiime2_env'
publishDir "${params.outdir}/${params.dada2_dirname}/merged", mode: 'copy', pattern: '*.qza'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'dada2_output'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_dada2merge -> "cmd/${task.process}_complete.sh" }
input :
path table_dir from dada2merge_tabledir_ch
path seq_dir from dada2merge_repseqsdir_ch
path metadata_merge from metadata_merge_ch
output :
file 'merged_table.qza' into merge_table_picrust2, merge_table_ancom
file 'merged_seq.qza' into merge_seqs_taxo, merge_seqs_phylo, merge_seqs_picrust2, merge_seqs_ancom
file 'dada2_output' into dada2merge_output
file 'completecmd' into complete_cmd_dada2merge
when :
params.dada2merge
script :
"""
q2_merge.sh ${table_dir} ${seq_dir} merged_table.qza merged_seq.qza ${metadata_merge} merged_table.qzv dada2_output merged_seq.qzv completecmd &> q2_merge.log 2>&1
"""
}
}
outputA = params.dada2merge ? dada2merge_output : dada2_output
output_ch = params.dbotu3_enable ? dbotu3_output : outputA
output_ch.into { taxonomy_output ; decontam_output }
seqs_taxoA = params.dada2merge ? merge_seqs_taxo : dada2_seqs_taxo
seqs_taxo = params.dbotu3_enable ? dbotu3_seqs_taxo : seqs_taxoA
/*
* STEP 7 - Run taxonomy assignment
*/
process q2_taxonomy {
label 'qiime2_highRAM'
publishDir "${params.outdir}/${params.taxo_dirname}", mode: 'copy', pattern: '*.qz*'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'taxo_output'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'ASV_taxonomy.tsv'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'ASV_table*'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_taxo -> "cmd/${task.process}_complete.sh" }
input :
file repseqs_taxo from seqs_taxo
file summary_output from taxonomy_output
output :
file 'taxonomy.qza' into data_taxonomy_filtering_tax, data_taxonomy_ancom
file 'taxonomy.qzv' into visu_taxonomy
file 'ASV_taxonomy.tsv' into taxonomy_tsv, taxonomy_tsv_filtering_tax
file 'taxo_output' into taxo_output
file 'ASV_table_with_taxonomy.biom' into biom
file 'ASV_table_with_taxonomy.tsv' into biom_tsv, biom_tsv_decontam
file 'taxonomic_database.qza' optional true into trained_database
file 'seqs_db_amplicons.qza' optional true into seqs_db_filtered
file 'completecmd' into complete_cmd_taxo
when :
!params.stats_only
script :
"""
q2_taxo.sh ${task.cpus} ${params.extract_db} ${params.primerF} ${params.primerR} ${params.confidence} ${repseqs_taxo} taxonomy.qza taxonomy.qzv taxo_output ASV_taxonomy.tsv ${summary_output} ASV_table_with_taxonomy.biom ASV_table_with_taxonomy.tsv taxonomic_database.qza seqs_db_amplicons.qza completecmd tmpdir ${params.database} ${params.seqs_db} ${params.taxo_db} &> q2_taxo.log 2>&1
"""
}
asv_table = params.dbotu3_enable ? dbotu3_table_filtering_tax : dada2_table_filtering_tax
asv_seq = params.dbotu3_enable ? dbotu3_seqs_filtering_tax : dada2_seqs_filtering_tax
/*
* STEP 8 - Filtering ASVs based on taxonomy assignment
*/
if (params.filtering_tax_enable) {
process q2_filtering_tax {
label 'qiime2_env'
publishDir "${params.outdir}/${params.tax_filtering_dirname}", mode: 'copy', pattern: '*.qz*'
publishDir "${params.outdir}/${params.report_dirname}/tax_filtering", mode: 'copy', pattern: 'tax_filtered_table_with_tax.*'
publishDir "${params.outdir}/${params.report_dirname}/tax_filtering", mode: 'copy', pattern: 'tax_filtered_output'
publishDir "${params.outdir}/${params.report_dirname}/version", mode: 'copy', pattern: 'v_*.txt'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_filtering_tax -> "cmd/${task.process}_complete.sh" }
input :
file asv_table from asv_table
file asv_tax from data_taxonomy_filtering_tax
file asv_seq from asv_seq
file asv_tax_tsv from taxonomy_tsv_filtering_tax
file metadata from metadata_filtering_tax
output :
file 'tax_filtered_table.qza' into tax_filtered_table, tax_filtered_table_picrust2, tax_filtered_table_ancom
file 'tax_filtered_seq.qza' into tax_filtered_seq, tax_filtered_seq_picrust2
file 'tax_filtered_table.qzv' into tax_filtered_table_visu
file 'tax_filtered_output' into tax_filtered_output
file 'tax_filtered_seq.qzv' into tax_filtered_seq_visu
file 'tax_filtered_table_with_tax.biom' into tax_filtered_table_biom
file 'tax_filtered_table_with_tax.tsv' into tax_filtered_table_tsv, tax_filtered_table_tsv_stats
file 'completecmd' into complete_cmd_filtering_tax
when :
!params.stats_only
script :
"""
q2_filtering_tax.sh ${asv_table} ${asv_tax} ${params.tax_to_exclude} ${params.tax_to_include} tax_filtered_table.qza ${asv_seq} tax_filtered_seq.qza tax_filtered_table.qzv ${metadata} tax_filtered_output tax_filtered_seq.qzv ${asv_tax_tsv} tax_filtered_table_with_tax.biom tax_filtered_table_with_tax.tsv completecmd &> q2_filtering_tax.log 2>&1
"""
}
}
microDecon_table = params.filtering_tax_enable ? tax_filtered_table_tsv : biom_tsv_decontam
/*
* STEP 9 - Decontaminate samples with MicroDecon
*/
if (params.microDecon_enable) {
process microDecon_step1 {
label 'microdecon_env'
publishDir "${params.outdir}/${params.microDecon_dirname}", mode: 'copy', pattern: 'decontaminated_ASV_table.tsv'
publishDir "${params.outdir}/${params.microDecon_dirname}", mode: 'copy', pattern: 'abundance_removed.txt'
publishDir "${params.outdir}/${params.microDecon_dirname}", mode: 'copy', pattern: 'ASV_removed.txt'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern: 'decontaminated_ASV_table.tsv'
publishDir "${params.outdir}/${params.report_dirname}/microDecon", mode: 'copy', pattern: 'decontaminated_ASV_table.tsv'
publishDir "${params.outdir}/${params.report_dirname}/microDecon", mode: 'copy', pattern: 'abundance_removed.txt'
publishDir "${params.outdir}/${params.report_dirname}/microDecon", mode: 'copy', pattern: 'ASV_removed.txt'
publishDir "${params.outdir}/${params.report_dirname}/version", mode: 'copy', pattern: 'v_*.txt'
publishDir "${params.outdir}/${params.report_dirname}", mode: 'copy', pattern : 'completecmd', saveAs : { complete_cmd_microDecon -> "cmd/${task.process}_complete.sh" }
input :
file microDecon_table from microDecon_table
output :
file 'decontaminated_ASV_table.tsv' into decontam_table, decontam_table_step2, decontam_table_step3
file 'abundance_removed.txt' into abund_removed
file 'ASV_removed.txt' into ASV_removed
file 'completecmd' into complete_cmd_microDecon
file 'v_microdecon.txt' into microdecon_version
when :
!params.stats_only && !params.dada2merge && params.microDecon_enable
shell :
"""
sed '1d' ${microDecon_table} > microDecon_table
sed -i 's/#OTU ID/ASV_ID/g' microDecon_table
sed -i "s/'//g" microDecon_table
microDecon.R microDecon_table ${params.control_list} ${params.nb_controls} ${params.nb_samples} decontaminated_ASV_table.tsv abundance_removed.txt ASV_removed.txt &> microDecon.log 2>&1
cp ${baseDir}/bin/microDecon.R completecmd &>> microDecon.log 2>&1
Rscript -e "write(x=as.character(packageVersion('microDecon')), file='v_microdecon.txt')"
"""
}
process microDecon_step2 {
label 'qiime2_env'
publishDir "${params.outdir}/${params.microDecon_dirname}", mode: 'copy', pattern: 'decontaminated_ASV_table.qza'
publishDir "${params.outdir}/${params.report_dirname}/microDecon", mode: 'copy', pattern: 'decontaminated_ASV_table.qza'
input :
file table4microDecon from decontam_table_step2
output :
file 'decontaminated_ASV_table.qza' into decontam_table_qza, decontam_table_picrust2, decontam_table_ancom
when :
!params.stats_only && !params.dada2merge && params.microDecon_enable
shell :
"""
biom convert -i ${table4microDecon} -o decontaminated_ASV_table.biom --to-hdf5 --table-type="OTU table" --process-obs-metadata taxonomy
qiime tools import --input-path decontaminated_ASV_table.biom --type 'FeatureTable[Frequency]' --input-format BIOMV210Format --output-path decontaminated_ASV_table.qza
"""
}
/* Extract non-contaminated ASV ID */
process microDecon_step3 {
label 'seqtk_env'
publishDir "${params.outdir}/${params.microDecon_dirname}", mode: 'copy', pattern: 'decontaminated_ASV_ID.txt'
publishDir "${params.outdir}/${params.microDecon_dirname}", mode: 'copy', pattern: 'decontaminated_ASV.fasta'
publishDir "${params.outdir}/${params.report_dirname}/microDecon", mode: 'copy', pattern: 'decontaminated_ASV.fasta'
input :
file decontam_table from decontam_table_step3
file dada2_output from decontam_output
output :
file 'decontaminated_ASV_ID.txt' into decontam_ASV_ID
file 'decontaminated_ASV.fasta' into decontam_ASV_fasta
when :
!params.stats_only && !params.dada2merge && params.microDecon_enable
shell :
"""
cut -d \$'\t' -f1 ${decontam_table} | sed '1d' > decontaminated_ASV_ID.txt
seqtk subseq ${dada2_output}/sequences.fasta decontaminated_ASV_ID.txt > decontaminated_ASV.fasta
"""
}
/* Reformat decontaminated sequence to QIIME2 format */
process microDecon_step4 {
label 'qiime2_env'
publishDir "${params.outdir}/${params.phylogeny_dirname}", mode: 'copy', pattern: '*.qza'
input :
file ASV_fasta from decontam_ASV_fasta
output :
file 'decontam_seqs.qza' into decontam_seqs_qza, decontam_seqs_phylo, decontam_seqs_picrust2, decontam_seqs_ancom
when :
!params.stats_only && !params.dada2merge && params.microDecon_enable
shell :
"""
qiime tools import --input-path ${ASV_fasta} --output-path decontam_seqs.qza --type 'FeatureData[Sequence]'
"""
}
}
} else {
if (workflow.profile.contains('test')) {
longreadsmanifest = testmanifest.splitCsv(header: true, sep:'\t')
.map { row -> tuple( row."sample-id", file(row."absolute-filepath")) }
longreadstofasta = testmanifest.splitCsv(header: true, sep:'\t')
.map { row -> file(row."absolute-filepath") }
} else {
Channel.fromPath(params.input_manifest, checkIfExists:true).into { manifest_lr ; manifest_lr2fasta }
longreadsmanifest = manifest_lr.splitCsv(header: true, sep:'\t')
.map { row -> tuple( row."sample-id", file(row."absolute-filepath")) }
longreadstofasta = manifest_lr2fasta.splitCsv(header: true, sep:'\t')
.map { row -> file(row."absolute-filepath") }
}
/* _______________________________________________________________ */
/* Long reads part */
/* _______________________________________________________________ */
process lr_mapping {
label 'lr_mapping_env'
publishDir "${params.outdir}/${params.lr_mapping_dirname}", mode: 'copy', pattern: '*.bam'
publishDir "${params.outdir}/${params.report_dirname}/version", mode: 'copy', pattern: 'v_*.txt'
input:
set sample, file(fastq) from longreadsmanifest
file ready from ready_lr
output:
file "*.bam" into lr_mapped
file 'lr_mapping.ok' into process_lr_mapping_report
file 'lr_samtools-sort.log' into process_lr_sort_report
file 'v_minimap2.txt' into v_minimap2_version
shell:
"""
minimap2 -t ${task.cpus} -K 25M -ax ${params.lr_type} -L ${params.lr_tax_fna} ${fastq} | samtools view -h -F0xe00 | samtools sort -o ${sample}.bam -O bam - &> lr_minimap2.log 2>&1
touch lr_mapping.ok
samtools index ${sample}.bam &> lr_samtools-sort.log 2>&1
touch lr_samtools-sort.ok
minimap2 --version > v_minimap2.txt
"""
}
process lr_getfasta {
label 'seqtk_env'
input :
file(fastq) from longreadstofasta
output :
file 'lr_sequences.fasta' into lr_sequences
shell :
"""
seqtk seq -a ${fastq} > 'lr_sequences.fasta'
"""
}
lr_sequences.collectFile(name : 'lr_sequences.fasta', newLine : false, storeDir : "${params.outdir}/${params.lr_mapping_dirname}")
.subscribe { println "Fasta sequences are saved to file : $it" }
process lr_get_taxonomy {
label 'biopython_env'
publishDir "${params.outdir}/${params.lr_taxonomy_dirname}", mode: 'copy', pattern: '*.tsv'
input:
file '*' from lr_mapped.collect()
output:
file 'samples.tsv' into lr_biom_tsv
file 'lr_count_table.ok' into process_lr_taxonomy_report
shell:
"""
lr_count_table_minimap2.py -b "." -t "${params.lr_taxo_flat}" -r ${params.lr_rank} -o samples.tsv &> lr_count_table.log 2>&1
touch lr_count_table.ok
"""
}
}
if (!params.longreads) {
seqs_phyloA = params.dada2merge ? merge_seqs_phylo : dada2_seqs_phylo
seqs_phyloB = params.dbotu3_enable ? dbotu3_seqs_phylo : seqs_phyloA