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sarek.nf
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/*
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PRINT PARAMS SUMMARY
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*/
include { paramsSummaryLog; paramsSummaryMap; fromSamplesheet } from 'plugin/nf-validation'
def logo = NfcoreTemplate.logo(workflow, params.monochrome_logs)
def citation = '\n' + WorkflowMain.citation(workflow) + '\n'
def summary_params = paramsSummaryMap(workflow)
// Print parameter summary log to screen
log.info logo + paramsSummaryLog(workflow) + citation
/*
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
VALIDATE INPUTS
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*/
// Check input path parameters to see if they exist
def checkPathParamList = [
params.ascat_alleles,
params.ascat_loci,
params.ascat_loci_gc,
params.ascat_loci_rt,
params.bwa,
params.bwamem2,
params.cf_chrom_len,
params.chr_dir,
params.cnvkit_reference,
params.dbnsfp,
params.dbnsfp_tbi,
params.dbsnp,
params.dbsnp_tbi,
params.dict,
params.dragmap,
params.fasta,
params.fasta_fai,
params.germline_resource,
params.germline_resource_tbi,
params.input,
params.intervals,
params.known_indels,
params.known_indels_tbi,
params.known_snps,
params.known_snps_tbi,
params.mappability,
params.multiqc_config,
params.pon,
params.pon_tbi,
params.spliceai_indel,
params.spliceai_indel_tbi,
params.spliceai_snv,
params.spliceai_snv_tbi
]
// only check if we are using the tools
if (params.tools && params.tools.contains("snpeff")) checkPathParamList.add(params.snpeff_cache)
if (params.tools && params.tools.contains("vep")) checkPathParamList.add(params.vep_cache)
// Validate input parameters
WorkflowSarek.initialise(params, log)
/*
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Check mandatory parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*/
for (param in checkPathParamList) if (param) file(param, checkIfExists: true)
// Set input, can either be from --input or from automatic retrieval in WorkflowSarek.groovy
if (params.input) {
ch_from_samplesheet = params.build_only_index ? Channel.empty() : Channel.fromSamplesheet("input")
} else {
ch_from_samplesheet = params.build_only_index ? Channel.empty() : Channel.fromSamplesheet("input_restart")
}
input_sample = ch_from_samplesheet
.map{ meta, fastq_1, fastq_2, table, cram, crai, bam, bai, vcf, variantcaller ->
// generate patient_sample key to group lanes together
[ meta.patient + meta.sample, [meta, fastq_1, fastq_2, table, cram, crai, bam, bai, vcf, variantcaller] ]
}
.tap{ ch_with_patient_sample } // save the channel
.groupTuple() //group by patient_sample to get all lanes
.map { patient_sample, ch_items ->
// get number of lanes per sample
[ patient_sample, ch_items.size() ]
}
.combine(ch_with_patient_sample, by: 0) // for each entry add numLanes
.map { patient_sample, num_lanes, ch_items ->
(meta, fastq_1, fastq_2, table, cram, crai, bam, bai, vcf, variantcaller) = ch_items
if (meta.lane && fastq_2) {
meta = meta + [id: "${meta.sample}-${meta.lane}".toString()]
def CN = params.seq_center ? "CN:${params.seq_center}\\t" : ''
def flowcell = flowcellLaneFromFastq(fastq_1)
// Don't use a random element for ID, it breaks resuming
def read_group = "\"@RG\\tID:${flowcell}.${meta.sample}.${meta.lane}\\t${CN}PU:${meta.lane}\\tSM:${meta.patient}_${meta.sample}\\tLB:${meta.sample}\\tDS:${params.fasta}\\tPL:${params.seq_platform}\""
meta = meta - meta.subMap('lane') + [num_lanes: num_lanes.toInteger(), read_group: read_group.toString(), data_type: 'fastq', size: 1]
if (params.step == 'mapping') return [ meta, [ fastq_1, fastq_2 ] ]
else {
error("Samplesheet contains fastq files but step is `$params.step`. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
// start from BAM
} else if (meta.lane && bam) {
if (params.step != 'mapping' && !bai) {
error("BAM index (bai) should be provided.")
}
meta = meta + [id: "${meta.sample}-${meta.lane}".toString()]
def CN = params.seq_center ? "CN:${params.seq_center}\\t" : ''
def read_group = "\"@RG\\tID:${meta.sample}_${meta.lane}\\t${CN}PU:${meta.lane}\\tSM:${meta.patient}_${meta.sample}\\tLB:${meta.sample}\\tDS:${params.fasta}\\tPL:${params.seq_platform}\""
meta = meta - meta.subMap('lane') + [num_lanes: num_lanes.toInteger(), read_group: read_group.toString(), data_type: 'bam', size: 1]
if (params.step != 'annotate') return [ meta - meta.subMap('lane'), bam, bai ]
else {
error("Samplesheet contains bam files but step is `annotate`. The pipeline is expecting vcf files for the annotation. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
// recalibration
} else if (table && cram) {
meta = meta + [id: meta.sample, data_type: 'cram']
if (!(params.step == 'mapping' || params.step == 'annotate')) return [ meta - meta.subMap('lane'), cram, crai, table ]
else {
error("Samplesheet contains cram files but step is `$params.step`. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
// recalibration when skipping MarkDuplicates
} else if (table && bam) {
meta = meta + [id: meta.sample, data_type: 'bam']
if (!(params.step == 'mapping' || params.step == 'annotate')) return [ meta - meta.subMap('lane'), bam, bai, table ]
else {
error("Samplesheet contains bam files but step is `$params.step`. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
// prepare_recalibration or variant_calling
} else if (cram) {
meta = meta + [id: meta.sample, data_type: 'cram']
if (!(params.step == 'mapping' || params.step == 'annotate')) return [ meta - meta.subMap('lane'), cram, crai ]
else {
error("Samplesheet contains cram files but step is `$params.step`. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
// prepare_recalibration when skipping MarkDuplicates or `--step markduplicates`
} else if (bam) {
meta = meta + [id: meta.sample, data_type: 'bam']
if (!(params.step == 'mapping' || params.step == 'annotate')) return [ meta - meta.subMap('lane'), bam, bai ]
else {
error("Samplesheet contains bam files but step is `$params.step`. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
// annotation
} else if (vcf) {
meta = meta + [id: meta.sample, data_type: 'vcf', variantcaller: variantcaller ?: '']
if (params.step == 'annotate') return [ meta - meta.subMap('lane'), vcf ]
else {
error("Samplesheet contains vcf files but step is `$params.step`. Please check your samplesheet or adjust the step parameter.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
} else {
error("Missing or unknown field in csv file header. Please check your samplesheet")
}
}
if (params.step != 'annotate' && params.tools && !params.build_only_index) {
// Two checks for ensuring that the pipeline stops with a meaningful error message if
// 1. the sample-sheet only contains normal-samples, but some of the requested tools require tumor-samples, and
// 2. the sample-sheet only contains tumor-samples, but some of the requested tools require normal-samples.
input_sample.filter{ it[0].status == 1 }.ifEmpty{ // In this case, the sample-sheet contains no tumor-samples
if (!params.build_only_index) {
def tools_tumor = ['ascat', 'controlfreec', 'mutect2', 'msisensorpro']
def tools_tumor_asked = []
tools_tumor.each{ tool ->
if (params.tools.split(',').contains(tool)) tools_tumor_asked.add(tool)
}
if (!tools_tumor_asked.isEmpty()) {
error('The sample-sheet only contains normal-samples, but the following tools, which were requested with "--tools", expect at least one tumor-sample : ' + tools_tumor_asked.join(", "))
}
}
}
input_sample.filter{ it[0].status == 0 }.ifEmpty{ // In this case, the sample-sheet contains no normal/germline-samples
def tools_requiring_normal_samples = ['ascat', 'deepvariant', 'haplotypecaller', 'msisensorpro']
def requested_tools_requiring_normal_samples = []
tools_requiring_normal_samples.each{ tool_requiring_normal_samples ->
if (params.tools.split(',').contains(tool_requiring_normal_samples)) requested_tools_requiring_normal_samples.add(tool_requiring_normal_samples)
}
if (!requested_tools_requiring_normal_samples.isEmpty()) {
error('The sample-sheet only contains tumor-samples, but the following tools, which were requested by the option "tools", expect at least one normal-sample : ' + requested_tools_requiring_normal_samples.join(", "))
}
}
}
// Fails when wrongfull extension for intervals file
if (params.wes && !params.step == 'annotate') {
if (params.intervals && !params.intervals.endsWith("bed")) error("Target file specified with `--intervals` must be in BED format for targeted data")
else log.warn("Intervals file was provided without parameter `--wes`: Pipeline will assume this is Whole-Genome-Sequencing data.")
} else if (params.intervals && !params.intervals.endsWith("bed") && !params.intervals.endsWith("list")) error("Intervals file must end with .bed, .list, or .interval_list")
if (params.step == 'mapping' && params.aligner.contains("dragmap") && !(params.skip_tools && params.skip_tools.split(',').contains("baserecalibrator"))) {
log.warn("DragMap was specified as aligner. Base recalibration is not contained in --skip_tools. It is recommended to skip baserecalibration when using DragMap\nhttps://gatk.broadinstitute.org/hc/en-us/articles/4407897446939--How-to-Run-germline-single-sample-short-variant-discovery-in-DRAGEN-mode")
}
if (params.step == 'mapping' && params.aligner.contains("sentieon-bwamem") && params.umi_read_structure) {
error("Sentieon BWA is currently not compatible with FGBio UMI handeling. Please choose a different aligner.")
}
if (params.tools && params.tools.contains("sentieon_haplotyper") && params.joint_germline && (!params.sentieon_haplotyper_emit_mode || !(params.sentieon_haplotyper_emit_mode.contains('gvcf')))) {
error("When setting the option `--joint_germline` and including `sentieon_haplotyper` among the requested tools, please set `--sentieon_haplotyper_emit_mode` to include `gvcf`.")
}
// Fails or warns when missing files or params for ascat
if (params.tools && params.tools.split(',').contains('ascat')) {
if (!params.ascat_alleles) {
error("No allele files were provided for running ASCAT. Please provide a zip folder with allele files.")
}
if (!params.ascat_loci) {
error("No loci files were provided for running ASCAT. Please provide a zip folder with loci files.")
}
if (!params.ascat_loci_gc && !params.ascat_loci_rt) {
log.warn("No LogRCorrection performed in ASCAT. For LogRCorrection to run, please provide either loci gc files or both loci gc files and loci rt files.")
}
if (params.wes) {
log.warn("Default reference files not suited for running ASCAT on WES data. It's recommended to use the reference files provided here: https://github.com/Wedge-lab/battenberg#required-reference-files")
}
}
// Warns when missing files or params for mutect2
if (params.tools && params.tools.split(',').contains('mutect2')) {
if (!params.pon) {
log.warn("No Panel-of-normal was specified for Mutect2.\nIt is highly recommended to use one: https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2\nFor more information on how to create one: https://gatk.broadinstitute.org/hc/en-us/articles/5358921041947-CreateSomaticPanelOfNormals-BETA-")
}
if (!params.germline_resource) {
log.warn("If Mutect2 is specified without a germline resource, no filtering will be done.\nIt is recommended to use one: https://gatk.broadinstitute.org/hc/en-us/articles/5358911630107-Mutect2")
}
if (params.pon && params.pon.contains("/Homo_sapiens/GATK/GRCh38/Annotation/GATKBundle/1000g_pon.hg38.vcf.gz")) {
log.warn("The default Panel-of-Normals provided by GATK is used for Mutect2.\nIt is highly recommended to generate one from normal samples that are technical similar to the tumor ones.\nFor more information: https://gatk.broadinstitute.org/hc/en-us/articles/360035890631-Panel-of-Normals-PON-")
}
}
// Fails when missing resources for baserecalibrator
// Warns when missing resources for haplotypecaller
if (!params.dbsnp && !params.known_indels) {
if (params.step in ['mapping', 'markduplicates', 'prepare_recalibration', 'recalibrate'] && (!params.skip_tools || (params.skip_tools && !params.skip_tools.split(',').contains('baserecalibrator')))) {
error("Base quality score recalibration requires at least one resource file. Please provide at least one of `--dbsnp` or `--known_indels`\nYou can skip this step in the workflow by adding `--skip_tools baserecalibrator` to the command.")
}
if (params.tools && (params.tools.split(',').contains('haplotypecaller') || params.tools.split(',').contains('sentieon_haplotyper'))) {
log.warn "If GATK's Haplotypecaller or Sentieon's Haplotyper is specified, without `--dbsnp` or `--known_indels no filtering will be done. For filtering, please provide at least one of `--dbsnp` or `--known_indels`.\nFor more information see FilterVariantTranches (single-sample, default): https://gatk.broadinstitute.org/hc/en-us/articles/5358928898971-FilterVariantTranches\nFor more information see VariantRecalibration (--joint_germline): https://gatk.broadinstitute.org/hc/en-us/articles/5358906115227-VariantRecalibrator\nFor more information on GATK Best practice germline variant calling: https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels-"
}
}
if (params.joint_germline && (!params.tools || !(params.tools.split(',').contains('haplotypecaller') || params.tools.split(',').contains('sentieon_haplotyper')))) {
error("The GATK's Haplotypecaller or Sentieon's Haplotyper should be specified as one of the tools when doing joint germline variant calling.) ")
}
if (params.joint_germline && (!params.dbsnp || !params.known_indels || !params.known_snps || params.no_intervals)) {
log.warn "If GATK's Haplotypecaller or Sentieon's Haplotyper is specified, without `--dbsnp`, `--known_snps`, `--known_indels` or the associated resource labels (ie `known_snps_vqsr`), no variant recalibration will be done. For recalibration you must provide all of these resources.\nFor more information see VariantRecalibration: https://gatk.broadinstitute.org/hc/en-us/articles/5358906115227-VariantRecalibrator \nJoint germline variant calling also requires intervals in order to genotype the samples. As a result, if `--no_intervals` is set to `true` the joint germline variant calling will not be performed."
}
// Fails when --joint_mutect2 is used without enabling mutect2
if (params.joint_mutect2 && (!params.tools || !params.tools.split(',').contains('mutect2'))) {
error("The mutect2 should be specified as one of the tools when doing joint somatic variant calling with Mutect2. (The mutect2 could be specified by adding `--tools mutect2` to the nextflow command.)")
}
// Fails when missing tools for variant_calling or annotate
if ((params.step == 'variant_calling' || params.step == 'annotate') && !params.tools) {
error("Please specify at least one tool when using `--step ${params.step}`.\nhttps://nf-co.re/sarek/parameters#tools")
}
// Fails when missing sex information for CNV tools
if (params.tools && (params.tools.split(',').contains('ascat') || params.tools.split(',').contains('controlfreec'))) {
input_sample.map{
if (it[0].sex == 'NA' ) {
error("Please specify sex information for each sample in your samplesheet when using '--tools' with 'ascat' or 'controlfreec'.\nhttps://nf-co.re/sarek/usage#input-samplesheet-configurations")
}
}
}
if ((params.download_cache) && (params.snpeff_cache || params.vep_cache)) {
error("Please specify either `--download_cache` or `--snpeff_cache`, `--vep_cache`.\nhttps://nf-co.re/sarek/dev/usage#how-to-customise-snpeff-and-vep-annotation")
}
/*
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
IMPORT LOCAL MODULES/SUBWORKFLOWS
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*/
// Initialize file channels based on params, defined in the params.genomes[params.genome] scope
ascat_alleles = params.ascat_alleles ? Channel.fromPath(params.ascat_alleles).collect() : Channel.empty()
ascat_loci = params.ascat_loci ? Channel.fromPath(params.ascat_loci).collect() : Channel.empty()
ascat_loci_gc = params.ascat_loci_gc ? Channel.fromPath(params.ascat_loci_gc).collect() : Channel.value([])
ascat_loci_rt = params.ascat_loci_rt ? Channel.fromPath(params.ascat_loci_rt).collect() : Channel.value([])
cf_chrom_len = params.cf_chrom_len ? Channel.fromPath(params.cf_chrom_len).collect() : []
chr_dir = params.chr_dir ? Channel.fromPath(params.chr_dir).collect() : Channel.value([])
dbsnp = params.dbsnp ? Channel.fromPath(params.dbsnp).collect() : Channel.value([])
fasta = params.fasta ? Channel.fromPath(params.fasta).first() : Channel.empty()
fasta_fai = params.fasta_fai ? Channel.fromPath(params.fasta_fai).collect() : Channel.empty()
germline_resource = params.germline_resource ? Channel.fromPath(params.germline_resource).collect() : Channel.value([]) // Mutect2 does not require a germline resource, so set to optional input
known_indels = params.known_indels ? Channel.fromPath(params.known_indels).collect() : Channel.value([])
known_snps = params.known_snps ? Channel.fromPath(params.known_snps).collect() : Channel.value([])
mappability = params.mappability ? Channel.fromPath(params.mappability).collect() : Channel.value([])
pon = params.pon ? Channel.fromPath(params.pon).collect() : Channel.value([]) // PON is optional for Mutect2 (but highly recommended)
// Initialize value channels based on params, defined in the params.genomes[params.genome] scope
ascat_genome = params.ascat_genome ?: Channel.empty()
dbsnp_vqsr = params.dbsnp_vqsr ? Channel.value(params.dbsnp_vqsr) : Channel.empty()
known_indels_vqsr = params.known_indels_vqsr ? Channel.value(params.known_indels_vqsr) : Channel.empty()
known_snps_vqsr = params.known_snps_vqsr ? Channel.value(params.known_snps_vqsr) : Channel.empty()
snpeff_db = params.snpeff_db ?: Channel.empty()
vep_cache_version = params.vep_cache_version ?: Channel.empty()
vep_genome = params.vep_genome ?: Channel.empty()
vep_species = params.vep_species ?: Channel.empty()
// Initialize files channels based on params, not defined within the params.genomes[params.genome] scope
if (params.snpeff_cache && params.tools && params.tools.contains("snpeff")) {
def snpeff_annotation_cache_key = params.use_annotation_cache_keys ? "${params.snpeff_genome}.${params.snpeff_db}/" : ""
def snpeff_cache_dir = "${snpeff_annotation_cache_key}${params.snpeff_genome}.${params.snpeff_db}"
def snpeff_cache_path_full = file("$params.snpeff_cache/$snpeff_cache_dir", type: 'dir')
if ( !snpeff_cache_path_full.exists() || !snpeff_cache_path_full.isDirectory() ) {
error("Files within --snpeff_cache invalid. Make sure there is a directory named ${snpeff_cache_dir} in ${params.snpeff_cache}.\nhttps://nf-co.re/sarek/dev/usage#how-to-customise-snpeff-and-vep-annotation")
}
snpeff_cache = Channel.fromPath(file("${params.snpeff_cache}/${snpeff_annotation_cache_key}"), checkIfExists: true).collect()
.map{ cache -> [ [ id:"${params.snpeff_genome}.${params.snpeff_db}" ], cache ] }
} else snpeff_cache = []
if (params.vep_cache && params.tools && params.tools.contains("vep")) {
def vep_annotation_cache_key = params.use_annotation_cache_keys ? "${params.vep_cache_version}_${params.vep_genome}/" : ""
def vep_cache_dir = "${vep_annotation_cache_key}${params.vep_species}/${params.vep_cache_version}_${params.vep_genome}"
def vep_cache_path_full = file("$params.vep_cache/$vep_cache_dir", type: 'dir')
if ( !vep_cache_path_full.exists() || !vep_cache_path_full.isDirectory() ) {
error("Files within --vep_cache invalid. Make sure there is a directory named ${vep_cache_dir} in ${params.vep_cache}.\nhttps://nf-co.re/sarek/dev/usage#how-to-customise-snpeff-and-vep-annotation")
}
vep_cache = Channel.fromPath(file("${params.vep_cache}/${vep_annotation_cache_key}"), checkIfExists: true).collect()
} else vep_cache = []
vep_extra_files = []
if (params.dbnsfp && params.dbnsfp_tbi) {
vep_extra_files.add(file(params.dbnsfp, checkIfExists: true))
vep_extra_files.add(file(params.dbnsfp_tbi, checkIfExists: true))
}
if (params.spliceai_snv && params.spliceai_snv_tbi && params.spliceai_indel && params.spliceai_indel_tbi) {
vep_extra_files.add(file(params.spliceai_indel, checkIfExists: true))
vep_extra_files.add(file(params.spliceai_indel_tbi, checkIfExists: true))
vep_extra_files.add(file(params.spliceai_snv, checkIfExists: true))
vep_extra_files.add(file(params.spliceai_snv_tbi, checkIfExists: true))
}
/*
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
IMPORT LOCAL/NF-CORE MODULES/SUBWORKFLOWS
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*/
// Create samplesheets to restart from different steps
include { CHANNEL_ALIGN_CREATE_CSV } from '../subworkflows/local/channel_align_create_csv/main'
include { CHANNEL_MARKDUPLICATES_CREATE_CSV } from '../subworkflows/local/channel_markduplicates_create_csv/main'
include { CHANNEL_BASERECALIBRATOR_CREATE_CSV } from '../subworkflows/local/channel_baserecalibrator_create_csv/main'
include { CHANNEL_APPLYBQSR_CREATE_CSV } from '../subworkflows/local/channel_applybqsr_create_csv/main'
include { CHANNEL_VARIANT_CALLING_CREATE_CSV } from '../subworkflows/local/channel_variant_calling_create_csv/main'
// Download annotation cache if needed
include { PREPARE_CACHE } from '../subworkflows/local/prepare_cache/main'
// Build indices if needed
include { PREPARE_GENOME } from '../subworkflows/local/prepare_genome/main'
// Build intervals if needed
include { PREPARE_INTERVALS } from '../subworkflows/local/prepare_intervals/main'
// Build CNVkit reference if needed
include { PREPARE_REFERENCE_CNVKIT } from '../subworkflows/local/prepare_reference_cnvkit/main'
// Convert BAM files to FASTQ files
include { BAM_CONVERT_SAMTOOLS as CONVERT_FASTQ_INPUT } from '../subworkflows/local/bam_convert_samtools/main'
include { BAM_CONVERT_SAMTOOLS as CONVERT_FASTQ_UMI } from '../subworkflows/local/bam_convert_samtools/main'
// Run FASTQC
include { FASTQC } from '../modules/nf-core/fastqc/main'
// TRIM/SPLIT FASTQ Files
include { FASTP } from '../modules/nf-core/fastp/main'
// Create umi consensus bams from fastq
include { FASTQ_CREATE_UMI_CONSENSUS_FGBIO } from '../subworkflows/local/fastq_create_umi_consensus_fgbio/main'
// Map input reads to reference genome
include { FASTQ_ALIGN_BWAMEM_MEM2_DRAGMAP_SENTIEON } from '../subworkflows/local/fastq_align_bwamem_mem2_dragmap_sentieon/main'
// Merge and index BAM files (optional)
include { BAM_MERGE_INDEX_SAMTOOLS } from '../subworkflows/local/bam_merge_index_samtools/main'
// Convert BAM files
include { SAMTOOLS_CONVERT as BAM_TO_CRAM } from '../modules/nf-core/samtools/convert/main'
include { SAMTOOLS_CONVERT as BAM_TO_CRAM_MAPPING } from '../modules/nf-core/samtools/convert/main'
// Convert CRAM files (optional)
include { SAMTOOLS_CONVERT as CRAM_TO_BAM } from '../modules/nf-core/samtools/convert/main'
include { SAMTOOLS_CONVERT as CRAM_TO_BAM_RECAL } from '../modules/nf-core/samtools/convert/main'
// Mark Duplicates (+QC)
include { BAM_MARKDUPLICATES } from '../subworkflows/local/bam_markduplicates/main'
include { BAM_MARKDUPLICATES_SPARK } from '../subworkflows/local/bam_markduplicates_spark/main'
include { BAM_SENTIEON_DEDUP } from '../subworkflows/local/bam_sentieon_dedup/main'
// QC on CRAM
include { CRAM_QC_MOSDEPTH_SAMTOOLS as CRAM_QC_NO_MD } from '../subworkflows/local/cram_qc_mosdepth_samtools/main'
include { CRAM_QC_MOSDEPTH_SAMTOOLS as CRAM_QC_RECAL } from '../subworkflows/local/cram_qc_mosdepth_samtools/main'
// Create recalibration tables
include { BAM_BASERECALIBRATOR } from '../subworkflows/local/bam_baserecalibrator/main'
include { BAM_BASERECALIBRATOR_SPARK } from '../subworkflows/local/bam_baserecalibrator_spark/main'
// Create recalibrated cram files to use for variant calling (+QC)
include { BAM_APPLYBQSR } from '../subworkflows/local/bam_applybqsr/main'
include { BAM_APPLYBQSR_SPARK } from '../subworkflows/local/bam_applybqsr_spark/main'
// Variant calling on a single normal sample
include { BAM_VARIANT_CALLING_GERMLINE_ALL } from '../subworkflows/local/bam_variant_calling_germline_all/main'
// Variant calling on a single tumor sample
include { BAM_VARIANT_CALLING_TUMOR_ONLY_ALL } from '../subworkflows/local/bam_variant_calling_tumor_only_all/main'
// Variant calling on tumor/normal pair
include { BAM_VARIANT_CALLING_SOMATIC_ALL } from '../subworkflows/local/bam_variant_calling_somatic_all/main'
// POST VARIANTCALLING: e.g. merging
include { POST_VARIANTCALLING } from '../subworkflows/local/post_variantcalling/main'
// QC on VCF files
include { VCF_QC_BCFTOOLS_VCFTOOLS } from '../subworkflows/local/vcf_qc_bcftools_vcftools/main'
// Annotation
include { VCF_ANNOTATE_ALL } from '../subworkflows/local/vcf_annotate_all/main'
// REPORTING VERSIONS OF SOFTWARE USED
include { CUSTOM_DUMPSOFTWAREVERSIONS } from '../modules/nf-core/custom/dumpsoftwareversions/main'
// MULTIQC
include { MULTIQC } from '../modules/nf-core/multiqc/main'
/*
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
RUN MAIN WORKFLOW
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
*/
workflow SAREK {
// MULTIQC
ch_multiqc_config = Channel.fromPath("$projectDir/assets/multiqc_config.yml", checkIfExists: true)
ch_multiqc_custom_config = params.multiqc_config ? Channel.fromPath( params.multiqc_config, checkIfExists: true ) : Channel.empty()
ch_multiqc_logo = params.multiqc_logo ? Channel.fromPath( params.multiqc_logo, checkIfExists: true ) : Channel.empty()
ch_multiqc_custom_methods_description = params.multiqc_methods_description ? file(params.multiqc_methods_description, checkIfExists: true) : file("$projectDir/assets/methods_description_template.yml", checkIfExists: true)
// To gather all QC reports for MultiQC
reports = Channel.empty()
// To gather used softwares versions for MultiQC
versions = Channel.empty()
// Download cache if needed
// Assuming that if the cache is provided, the user has already downloaded it
ensemblvep_info = params.vep_cache ? [] : Channel.of([ [ id:"${params.vep_cache_version}_${params.vep_genome}" ], params.vep_genome, params.vep_species, params.vep_cache_version ])
snpeff_info = params.snpeff_cache ? [] : Channel.of([ [ id:"${params.snpeff_genome}.${params.snpeff_db}" ], params.snpeff_genome, params.snpeff_db ])
if (params.download_cache) {
PREPARE_CACHE(ensemblvep_info, snpeff_info)
snpeff_cache = PREPARE_CACHE.out.snpeff_cache
vep_cache = PREPARE_CACHE.out.ensemblvep_cache.map{ meta, cache -> [ cache ] }
versions = versions.mix(PREPARE_CACHE.out.versions)
}
// Build indices if needed
PREPARE_GENOME(
ascat_alleles,
ascat_loci,
ascat_loci_gc,
ascat_loci_rt,
chr_dir,
dbsnp,
fasta,
fasta_fai,
germline_resource,
known_indels,
known_snps,
pon)
// Gather built indices or get them from the params
// Built from the fasta file:
dict = params.dict ? Channel.fromPath(params.dict).map{ it -> [ [id:'dict'], it ] }.collect()
: PREPARE_GENOME.out.dict
fasta_fai = params.fasta_fai ? Channel.fromPath(params.fasta_fai).collect()
: PREPARE_GENOME.out.fasta_fai
bwa = params.bwa ? Channel.fromPath(params.bwa).collect()
: PREPARE_GENOME.out.bwa
bwamem2 = params.bwamem2 ? Channel.fromPath(params.bwamem2).collect()
: PREPARE_GENOME.out.bwamem2
dragmap = params.dragmap ? Channel.fromPath(params.dragmap).collect()
: PREPARE_GENOME.out.hashtable
// Gather index for mapping given the chosen aligner
index_alignement = (params.aligner == "bwa-mem" || params.aligner == "sentieon-bwamem") ? bwa :
params.aligner == "bwa-mem2" ? bwamem2 :
dragmap
// TODO: add a params for msisensorpro_scan
msisensorpro_scan = PREPARE_GENOME.out.msisensorpro_scan
// For ASCAT, extracted from zip or tar.gz files:
allele_files = PREPARE_GENOME.out.allele_files
chr_files = PREPARE_GENOME.out.chr_files
gc_file = PREPARE_GENOME.out.gc_file
loci_files = PREPARE_GENOME.out.loci_files
rt_file = PREPARE_GENOME.out.rt_file
// Tabix indexed vcf files:
dbsnp_tbi = params.dbsnp ? params.dbsnp_tbi ? Channel.fromPath(params.dbsnp_tbi).collect() : PREPARE_GENOME.out.dbsnp_tbi : Channel.value([])
germline_resource_tbi = params.germline_resource ? params.germline_resource_tbi ? Channel.fromPath(params.germline_resource_tbi).collect() : PREPARE_GENOME.out.germline_resource_tbi : [] //do not change to Channel.value([]), the check for its existence then fails for Getpileupsumamries
known_indels_tbi = params.known_indels ? params.known_indels_tbi ? Channel.fromPath(params.known_indels_tbi).collect() : PREPARE_GENOME.out.known_indels_tbi : Channel.value([])
known_snps_tbi = params.known_snps ? params.known_snps_tbi ? Channel.fromPath(params.known_snps_tbi).collect() : PREPARE_GENOME.out.known_snps_tbi : Channel.value([])
pon_tbi = params.pon ? params.pon_tbi ? Channel.fromPath(params.pon_tbi).collect() : PREPARE_GENOME.out.pon_tbi : Channel.value([])
// known_sites is made by grouping both the dbsnp and the known snps/indels resources
// Which can either or both be optional
known_sites_indels = dbsnp.concat(known_indels).collect()
known_sites_indels_tbi = dbsnp_tbi.concat(known_indels_tbi).collect()
known_sites_snps = dbsnp.concat(known_snps).collect()
known_sites_snps_tbi = dbsnp_tbi.concat(known_snps_tbi).collect()
// Build intervals if needed
PREPARE_INTERVALS(fasta_fai, params.intervals, params.no_intervals)
// Intervals for speed up preprocessing/variant calling by spread/gather
// [interval.bed] all intervals in one file
intervals_bed_combined = params.no_intervals ? Channel.value([]) : PREPARE_INTERVALS.out.intervals_bed_combined
intervals_bed_gz_tbi_combined = params.no_intervals ? Channel.value([]) : PREPARE_INTERVALS.out.intervals_bed_gz_tbi_combined
// For QC during preprocessing, we don't need any intervals (MOSDEPTH doesn't take them for WGS)
intervals_for_preprocessing = params.wes ?
intervals_bed_combined.map{it -> [ [ id:it.baseName ], it ]}.collect() :
Channel.value([ [ id:'null' ], [] ])
intervals = PREPARE_INTERVALS.out.intervals_bed // [ interval, num_intervals ] multiple interval.bed files, divided by useful intervals for scatter/gather
intervals_bed_gz_tbi = PREPARE_INTERVALS.out.intervals_bed_gz_tbi // [ interval_bed, tbi, num_intervals ] multiple interval.bed.gz/.tbi files, divided by useful intervals for scatter/gather
intervals_and_num_intervals = intervals.map{ interval, num_intervals ->
if ( num_intervals < 1 ) [ [], num_intervals ]
else [ interval, num_intervals ]
}
intervals_bed_gz_tbi_and_num_intervals = intervals_bed_gz_tbi.map{ intervals, num_intervals ->
if ( num_intervals < 1 ) [ [], [], num_intervals ]
else [ intervals[0], intervals[1], num_intervals ]
}
if (params.tools && params.tools.split(',').contains('cnvkit')) {
if (params.cnvkit_reference) {
cnvkit_reference = Channel.fromPath(params.cnvkit_reference).collect()
} else {
PREPARE_REFERENCE_CNVKIT(fasta, intervals_bed_combined)
cnvkit_reference = PREPARE_REFERENCE_CNVKIT.out.cnvkit_reference
versions = versions.mix(PREPARE_REFERENCE_CNVKIT.out.versions)
}
} else {
cnvkit_reference = Channel.value([])
}
// Gather used softwares versions
versions = versions.mix(PREPARE_GENOME.out.versions)
versions = versions.mix(PREPARE_INTERVALS.out.versions)
// PREPROCESSING
if (params.step == 'mapping') {
// Figure out if input is bam or fastq
input_sample_type = input_sample.branch{
bam: it[0].data_type == "bam"
fastq: it[0].data_type == "fastq"
}
// Convert any bam input to fastq
// fasta are not needed when converting bam to fastq -> [ id:"fasta" ], []
// No need for fasta.fai -> []
interleave_input = false // Currently don't allow interleaved input
CONVERT_FASTQ_INPUT(
input_sample_type.bam,
[ [ id:"fasta" ], [] ], // fasta
[ [ id:'null' ], [] ], // fasta_fai
interleave_input)
// Gather fastq (inputed or converted)
// Theorically this could work on mixed input (fastq for one sample and bam for another)
// But not sure how to handle that with the samplesheet
// Or if we really want users to be able to do that
input_fastq = input_sample_type.fastq.mix(CONVERT_FASTQ_INPUT.out.reads)
// STEP 0: QC & TRIM
// `--skip_tools fastqc` to skip fastqc
// Trim only with `--trim_fastq`
// Additional options to be set up
// QC
if (!(params.skip_tools && params.skip_tools.split(',').contains('fastqc'))) {
FASTQC(input_fastq)
reports = reports.mix(FASTQC.out.zip.collect{ meta, logs -> logs })
versions = versions.mix(FASTQC.out.versions.first())
}
// UMI consensus calling
if (params.umi_read_structure) {
FASTQ_CREATE_UMI_CONSENSUS_FGBIO(
input_fastq,
fasta,
fasta_fai,
index_alignement,
params.group_by_umi_strategy)
bam_converted_from_fastq = FASTQ_CREATE_UMI_CONSENSUS_FGBIO.out.consensusbam.map{ meta, bam -> [ meta, bam, [] ] }
// Convert back to fastq for further preprocessing
// fasta are not needed when converting bam to fastq -> [ id:"fasta" ], []
// No need for fasta.fai -> []
interleave_input = false // Currently don't allow interleaved input
CONVERT_FASTQ_UMI(
bam_converted_from_fastq,
[ [ id:"fasta" ], [] ], // fasta
[ [ id:'null' ], [] ], // fasta_fai
interleave_input)
reads_for_fastp = CONVERT_FASTQ_UMI.out.reads
// Gather used softwares versions
versions = versions.mix(CONVERT_FASTQ_UMI.out.versions)
versions = versions.mix(FASTQ_CREATE_UMI_CONSENSUS_FGBIO.out.versions)
} else {
reads_for_fastp = input_fastq
}
// Trimming and/or splitting
if (params.trim_fastq || params.split_fastq > 0) {
save_trimmed_fail = false
save_merged = false
FASTP(
reads_for_fastp,
[], // we are not using any adapter fastas at the moment
save_trimmed_fail,
save_merged
)
reports = reports.mix(FASTP.out.json.collect{ meta, json -> json })
reports = reports.mix(FASTP.out.html.collect{ meta, html -> html })
if (params.split_fastq) {
reads_for_alignment = FASTP.out.reads.map{ meta, reads ->
read_files = reads.sort(false) { a,b -> a.getName().tokenize('.')[0] <=> b.getName().tokenize('.')[0] }.collate(2)
[ meta + [ size:read_files.size() ], read_files ]
}.transpose()
} else reads_for_alignment = FASTP.out.reads
versions = versions.mix(FASTP.out.versions)
} else {
reads_for_alignment = reads_for_fastp
}
// STEP 1: MAPPING READS TO REFERENCE GENOME
// reads will be sorted
reads_for_alignment = reads_for_alignment.map{ meta, reads ->
// Update meta.id to meta.sample no multiple lanes or splitted fastqs
if (meta.size * meta.num_lanes == 1) [ meta + [ id:meta.sample ], reads ]
else [ meta, reads ]
}
sort_bam = true
FASTQ_ALIGN_BWAMEM_MEM2_DRAGMAP_SENTIEON(reads_for_alignment, index_alignement, sort_bam, fasta, fasta_fai)
// Grouping the bams from the same samples not to stall the workflow
bam_mapped = FASTQ_ALIGN_BWAMEM_MEM2_DRAGMAP_SENTIEON.out.bam.map{ meta, bam ->
// Update meta.id to be meta.sample, ditching sample-lane that is not needed anymore
// Update meta.data_type
// Remove no longer necessary fields:
// read_group: Now in the BAM header
// num_lanes: only needed for mapping
// size: only needed for mapping
// Use groupKey to make sure that the correct group can advance as soon as it is complete
// and not stall the workflow until all reads from all channels are mapped
[ groupKey( meta - meta.subMap('num_lanes', 'read_group', 'size') + [ data_type:'bam', id:meta.sample ], (meta.num_lanes ?: 1) * (meta.size ?: 1)), bam ]
}.groupTuple()
bai_mapped = FASTQ_ALIGN_BWAMEM_MEM2_DRAGMAP_SENTIEON.out.bai.map{ meta, bai ->
[ groupKey( meta - meta.subMap('num_lanes', 'read_group', 'size') + [ data_type:'bai', id:meta.sample ], (meta.num_lanes ?: 1) * (meta.size ?: 1)), bai ]
}.groupTuple()
// gatk4 markduplicates can handle multiple bams as input, so no need to merge/index here
// Except if and only if save_mapped or (skipping markduplicates and sentieon-dedup)
if (
params.save_mapped ||
(
(params.skip_tools && params.skip_tools.split(',').contains('markduplicates')) &&
!(params.tools && params.tools.split(',').contains('sentieon_dedup'))
)
) {
// bams are merged (when multiple lanes from the same sample), indexed and then converted to cram
BAM_MERGE_INDEX_SAMTOOLS(bam_mapped)
BAM_TO_CRAM_MAPPING(BAM_MERGE_INDEX_SAMTOOLS.out.bam_bai, fasta, fasta_fai)
// Create CSV to restart from this step
params.save_output_as_bam ? CHANNEL_ALIGN_CREATE_CSV(BAM_MERGE_INDEX_SAMTOOLS.out.bam_bai) : CHANNEL_ALIGN_CREATE_CSV(BAM_TO_CRAM_MAPPING.out.alignment_index)
// Gather used softwares versions
versions = versions.mix(BAM_MERGE_INDEX_SAMTOOLS.out.versions)
versions = versions.mix(BAM_TO_CRAM_MAPPING.out.versions)
}
// Gather used softwares versions
versions = versions.mix(CONVERT_FASTQ_INPUT.out.versions)
versions = versions.mix(FASTQ_ALIGN_BWAMEM_MEM2_DRAGMAP_SENTIEON.out.versions)
}
if (params.step in ['mapping', 'markduplicates']) {
// ch_cram_no_markduplicates_restart = Channel.empty()
cram_markduplicates_no_spark = Channel.empty()
cram_sentieon_dedup = Channel.empty()
cram_markduplicates_spark = Channel.empty()
// STEP 2: markduplicates (+QC) + convert to CRAM
// ch_bam_for_markduplicates will contain bam mapped with FASTQ_ALIGN_BWAMEM_MEM2_DRAGMAP_SENTIEON when step is mapping
// Or bams that are specified in the samplesheet.csv when step is prepare_recalibration
cram_for_markduplicates = params.step == 'mapping' ? bam_mapped : input_sample.map{ meta, input, index -> [ meta, input ] }
// if no MD is done, then run QC on mapped & converted CRAM files
// or the input BAM (+converted) or CRAM files
cram_skip_markduplicates = Channel.empty()
// Should it be possible to restart from converted crams?
// For now, conversion from bam to cram is only done when skipping markduplicates
if (
params.skip_tools &&
params.skip_tools.split(',').contains('markduplicates') &&
!(params.tools && params.tools.split(',').contains('sentieon_dedup'))
) {
if (params.step == 'mapping') {
cram_skip_markduplicates = BAM_TO_CRAM_MAPPING.out.alignment_index
} else {
input_markduplicates_convert = input_sample.branch{
bam: it[0].data_type == "bam"
cram: it[0].data_type == "cram"
}
// Convert any input BAMs to CRAM
BAM_TO_CRAM(input_markduplicates_convert.bam, fasta, fasta_fai)
versions = versions.mix(BAM_TO_CRAM.out.versions)
cram_skip_markduplicates = Channel.empty().mix(input_markduplicates_convert.cram, BAM_TO_CRAM.out.alignment_index)
}
CRAM_QC_NO_MD(cram_skip_markduplicates, fasta, intervals_for_preprocessing)
// Gather QC reports
reports = reports.mix(CRAM_QC_NO_MD.out.reports.collect{ meta, report -> report })
// Gather used softwares versions
versions = versions.mix(CRAM_QC_NO_MD.out.versions)
} else if (params.use_gatk_spark && params.use_gatk_spark.contains('markduplicates')) {
BAM_MARKDUPLICATES_SPARK(
cram_for_markduplicates,
dict.map{ meta, dict -> [ dict ] },
fasta,
fasta_fai,
intervals_for_preprocessing)
cram_markduplicates_spark = BAM_MARKDUPLICATES_SPARK.out.cram
// Gather QC reports
reports = reports.mix(BAM_MARKDUPLICATES_SPARK.out.reports.collect{ meta, report -> report })
// Gather used softwares versions
versions = versions.mix(BAM_MARKDUPLICATES_SPARK.out.versions)
} else if (params.tools && params.tools.split(',').contains('sentieon_dedup')) {
crai_for_markduplicates = params.step == 'mapping' ? bai_mapped : input_sample.map{ meta, input, index -> [ meta, index ] }
BAM_SENTIEON_DEDUP(
cram_for_markduplicates,
crai_for_markduplicates,
fasta,
fasta_fai,
intervals_for_preprocessing)
cram_sentieon_dedup = BAM_SENTIEON_DEDUP.out.cram
// Gather QC reports
reports = reports.mix(BAM_SENTIEON_DEDUP.out.reports.collect{ meta, report -> report })
// Gather used softwares versions
versions = versions.mix(BAM_SENTIEON_DEDUP.out.versions)
} else {
BAM_MARKDUPLICATES(
cram_for_markduplicates,
fasta,
fasta_fai,
intervals_for_preprocessing)
cram_markduplicates_no_spark = BAM_MARKDUPLICATES.out.cram
// Gather QC reports
reports = reports.mix(BAM_MARKDUPLICATES.out.reports.collect{ meta, report -> report })
// Gather used softwares versions
versions = versions.mix(BAM_MARKDUPLICATES.out.versions)
}
// ch_md_cram_for_restart contains either:
// - crams from markduplicates
// - crams from sentieon_dedup
// - crams from markduplicates_spark
// - crams from input step markduplicates --> from the converted ones only?
ch_md_cram_for_restart = Channel.empty().mix(cram_markduplicates_no_spark, cram_markduplicates_spark, cram_sentieon_dedup)
// Make sure correct data types are carried through
.map{ meta, cram, crai -> [ meta + [data_type: "cram"], cram, crai ] }
// If params.save_output_as_bam, then convert CRAM files to BAM
CRAM_TO_BAM(ch_md_cram_for_restart, fasta, fasta_fai)
versions = versions.mix(CRAM_TO_BAM.out.versions)
// CSV should be written for the file actually out, either CRAM or BAM
// Create CSV to restart from this step
csv_subfolder = (params.tools && params.tools.split(',').contains('sentieon_dedup')) ? 'sentieon_dedup' : 'markduplicates'
params.save_output_as_bam ? CHANNEL_MARKDUPLICATES_CREATE_CSV(CRAM_TO_BAM.out.alignment_index, csv_subfolder, params.outdir, params.save_output_as_bam) : CHANNEL_MARKDUPLICATES_CREATE_CSV(ch_md_cram_for_restart, csv_subfolder, params.outdir, params.save_output_as_bam)
}
if (params.step in ['mapping', 'markduplicates', 'prepare_recalibration']) {
// Run if starting from step "prepare_recalibration"
if (params.step == 'prepare_recalibration') {
// Support if starting from BAM or CRAM files
input_prepare_recal_convert = input_sample.branch{
bam: it[0].data_type == "bam"
cram: it[0].data_type == "cram"
}
// BAM files first must be converted to CRAM files since from this step on we base everything on CRAM format
BAM_TO_CRAM(input_prepare_recal_convert.bam, fasta, fasta_fai)
versions = versions.mix(BAM_TO_CRAM.out.versions)
ch_cram_from_bam = BAM_TO_CRAM.out.alignment_index
// Make sure correct data types are carried through
.map{ meta, cram, crai -> [ meta + [data_type: "cram"], cram, crai ] }
ch_cram_for_bam_baserecalibrator = Channel.empty().mix(ch_cram_from_bam, input_prepare_recal_convert.cram)
ch_md_cram_for_restart = ch_cram_from_bam
} else {
// ch_cram_for_bam_baserecalibrator contains either:
// - crams from markduplicates
// - crams from markduplicates_spark
// - crams converted from bam mapped when skipping markduplicates
// - input cram files, when start from step markduplicates
ch_cram_for_bam_baserecalibrator = Channel.empty().mix(ch_md_cram_for_restart, cram_skip_markduplicates )
// Make sure correct data types are carried through
.map{ meta, cram, crai -> [ meta + [data_type: "cram"], cram, crai ] }
}
// STEP 3: Create recalibration tables
if (!(params.skip_tools && params.skip_tools.split(',').contains('baserecalibrator'))) {
ch_table_bqsr_no_spark = Channel.empty()
ch_table_bqsr_spark = Channel.empty()
if (params.use_gatk_spark && params.use_gatk_spark.contains('baserecalibrator')) {
BAM_BASERECALIBRATOR_SPARK(
ch_cram_for_bam_baserecalibrator,
dict,
fasta,
fasta_fai,
intervals_and_num_intervals,
known_sites_indels,
known_sites_indels_tbi)
ch_table_bqsr_spark = BAM_BASERECALIBRATOR_SPARK.out.table_bqsr
// Gather used softwares versions
versions = versions.mix(BAM_BASERECALIBRATOR_SPARK.out.versions)
} else {
BAM_BASERECALIBRATOR(
ch_cram_for_bam_baserecalibrator,
dict,
fasta,
fasta_fai,
intervals_and_num_intervals,
known_sites_indels,
known_sites_indels_tbi)
ch_table_bqsr_no_spark = BAM_BASERECALIBRATOR.out.table_bqsr
// Gather used softwares versions
versions = versions.mix(BAM_BASERECALIBRATOR.out.versions)
}
// ch_table_bqsr contains either:
// - bqsr table from baserecalibrator
// - bqsr table from baserecalibrator_spark
ch_table_bqsr = Channel.empty().mix(
ch_table_bqsr_no_spark,
ch_table_bqsr_spark)
reports = reports.mix(ch_table_bqsr.collect{ meta, table -> table })
cram_applybqsr = ch_cram_for_bam_baserecalibrator.join(ch_table_bqsr, failOnDuplicate: true, failOnMismatch: true)
// Create CSV to restart from this step
CHANNEL_BASERECALIBRATOR_CREATE_CSV(ch_md_cram_for_restart.join(ch_table_bqsr, failOnDuplicate: true), params.tools, params.skip_tools, params.save_output_as_bam, params.outdir)
}
}
// STEP 4: RECALIBRATING
if (params.step in ['mapping', 'markduplicates', 'prepare_recalibration', 'recalibrate']) {
// Run if starting from step "prepare_recalibration"
if (params.step == 'recalibrate') {
// Support if starting from BAM or CRAM files
input_recal_convert = input_sample.branch{
bam: it[0].data_type == "bam"
cram: it[0].data_type == "cram"
}
// If BAM file, split up table and mapped file to convert BAM to CRAM
input_only_table = input_recal_convert.bam.map{ meta, bam, bai, table -> [ meta, table ] }
input_only_bam = input_recal_convert.bam.map{ meta, bam, bai, table -> [ meta, bam, bai ] }
// BAM files first must be converted to CRAM files since from this step on we base everything on CRAM format
BAM_TO_CRAM(input_only_bam, fasta, fasta_fai)
versions = versions.mix(BAM_TO_CRAM.out.versions)
cram_applybqsr = Channel.empty().mix(
BAM_TO_CRAM.out.alignment_index.join(input_only_table, failOnDuplicate: true, failOnMismatch: true),
input_recal_convert.cram)
// Join together converted cram with input tables
.map{ meta, cram, crai, table -> [ meta + [data_type: "cram"], cram, crai, table ]}
}
if (!(params.skip_tools && params.skip_tools.split(',').contains('baserecalibrator'))) {
cram_variant_calling_no_spark = Channel.empty()
cram_variant_calling_spark = Channel.empty()
if (params.use_gatk_spark && params.use_gatk_spark.contains('baserecalibrator')) {
BAM_APPLYBQSR_SPARK(
cram_applybqsr,
dict,
fasta,
fasta_fai,
intervals_and_num_intervals)
cram_variant_calling_spark = BAM_APPLYBQSR_SPARK.out.cram
// Gather used softwares versions
versions = versions.mix(BAM_APPLYBQSR_SPARK.out.versions)
} else {
BAM_APPLYBQSR(
cram_applybqsr,
dict,
fasta,
fasta_fai,
intervals_and_num_intervals)
cram_variant_calling_no_spark = BAM_APPLYBQSR.out.cram
// Gather used softwares versions
versions = versions.mix(BAM_APPLYBQSR.out.versions)
}
cram_variant_calling = Channel.empty().mix(