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## Copyright Broad Institute, 2019
##
## This WDL pipeline implements data pre-processing according to the GATK Best Practices.
##
## Requirements/expectations :
## - Pair-end sequencing data in unmapped BAM (uBAM) format
## - One or more read groups, one per uBAM file, all belonging to a single sample (SM)
## - Input uBAM files must additionally comply with the following requirements:
## - - filenames all have the same suffix (we use ".unmapped.bam")
## - - files must pass validation by ValidateSamFile
## - - reads are provided in query-sorted order
## - - all reads must have an RG tag
##
## Output :
## - A clean BAM file and its index, suitable for variant discovery analyses.
##
## Software version requirements
## - GATK 4 or later
## - BWA 0.7.15-r1140
## - Picard 2.16.0-SNAPSHOT
## - Samtools 1.3.1 (using htslib 1.3.1)
## - Python 2.7
##
## Cromwell version support
## - Successfully tested on v37
## - Does not work on versions < v23 due to output syntax
##
## Runtime parameters are optimized for Broad's Google Cloud Platform implementation.
##
## LICENSING :
## This script is released under the WDL source code license (BSD-3) (see LICENSE in
## https://github.com/broadinstitute/wdl). Note however that the programs it calls may
## be subject to different licenses. Users are responsible for checking that they are
## authorized to run all programs before running this script. Please see the dockers
## for detailed licensing information pertaining to the included programs.
# WORKFLOW DEFINITION
workflow PreProcessingForVariantDiscovery_GATK4 {
String sample_name
String ref_name
File flowcell_unmapped_bams_list
String unmapped_bam_suffix
File ref_fasta
File ref_fasta_index
File ref_dict
File dbSNP_vcf
File dbSNP_vcf_index
Array[File] known_indels_sites_VCFs
Array[File] known_indels_sites_indices
String? bwa_commandline_override
String bwa_commandline = select_first([bwa_commandline_override, "bwa mem -K 100000000 -p -v 3 -t 16 -Y $bash_ref_fasta"])
Int compression_level
String? gatk_docker_override
String gatk_docker = select_first([gatk_docker_override, "broadinstitute/gatk:4.1.0.0"])
String? gatk_path_override
String gatk_path = select_first([gatk_path_override, "/gatk/gatk"])
String? gotc_docker_override
String gotc_docker = select_first([gotc_docker_override, "broadinstitute/genomes-in-the-cloud:2.3.1-1512499786"])
String? gotc_path_override
String gotc_path = select_first([gotc_path_override, "/usr/gitc/"])
String? python_docker_override
String python_docker = select_first([python_docker_override, "python:2.7"])
Int flowcell_small_disk
Int flowcell_medium_disk
Int agg_small_disk
Int agg_medium_disk
Int agg_large_disk
String? preemptible_tries_override
Int preemptible_tries = select_first([preemptible_tries_override, "3"])
String base_file_name = sample_name + "." + ref_name
Array[File] flowcell_unmapped_bams = read_lines(flowcell_unmapped_bams_list)
# Get the version of BWA to include in the PG record in the header of the BAM produced
# by MergeBamAlignment.
call GetBwaVersion {
input:
docker_image = gotc_docker,
bwa_path = gotc_path,
preemptible_tries = preemptible_tries
}
# Align flowcell-level unmapped input bams in parallel
scatter (unmapped_bam in flowcell_unmapped_bams) {
# Get the basename, i.e. strip the filepath and the extension
String bam_basename = basename(unmapped_bam, unmapped_bam_suffix)
# Map reads to reference
call SamToFastqAndBwaMem {
input:
input_bam = unmapped_bam,
bwa_commandline = bwa_commandline,
output_bam_basename = bam_basename + ".unmerged",
ref_fasta = ref_fasta,
ref_fasta_index = ref_fasta_index,
ref_dict = ref_dict,
docker_image = gotc_docker,
bwa_path = gotc_path,
gotc_path = gotc_path,
disk_size = flowcell_medium_disk,
preemptible_tries = preemptible_tries,
compression_level = compression_level
}
# Merge original uBAM and BWA-aligned BAM
call MergeBamAlignment {
input:
unmapped_bam = unmapped_bam,
bwa_commandline = bwa_commandline,
bwa_version = GetBwaVersion.version,
aligned_bam = SamToFastqAndBwaMem.output_bam,
output_bam_basename = bam_basename + ".aligned.unsorted",
ref_fasta = ref_fasta,
ref_fasta_index = ref_fasta_index,
ref_dict = ref_dict,
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = flowcell_medium_disk,
preemptible_tries = preemptible_tries,
compression_level = compression_level
}
}
# Aggregate aligned+merged flowcell BAM files and mark duplicates
# We take advantage of the tool's ability to take multiple BAM inputs and write out a single output
# to avoid having to spend time just merging BAM files.
call MarkDuplicates {
input:
input_bams = MergeBamAlignment.output_bam,
output_bam_basename = base_file_name + ".aligned.unsorted.duplicates_marked",
metrics_filename = base_file_name + ".duplicate_metrics",
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = agg_large_disk,
compression_level = compression_level,
preemptible_tries = preemptible_tries
}
# Sort aggregated+deduped BAM file and fix tags
call SortAndFixTags {
input:
input_bam = MarkDuplicates.output_bam,
output_bam_basename = base_file_name + ".aligned.duplicate_marked.sorted",
ref_dict = ref_dict,
ref_fasta = ref_fasta,
ref_fasta_index = ref_fasta_index,
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = agg_large_disk,
preemptible_tries = 0,
compression_level = compression_level
}
# Create list of sequences for scatter-gather parallelization
call CreateSequenceGroupingTSV {
input:
ref_dict = ref_dict,
docker_image = python_docker,
preemptible_tries = preemptible_tries
}
# Perform Base Quality Score Recalibration (BQSR) on the sorted BAM in parallel
scatter (subgroup in CreateSequenceGroupingTSV.sequence_grouping) {
# Generate the recalibration model by interval
call BaseRecalibrator {
input:
input_bam = SortAndFixTags.output_bam,
input_bam_index = SortAndFixTags.output_bam_index,
recalibration_report_filename = base_file_name + ".recal_data.csv",
sequence_group_interval = subgroup,
dbSNP_vcf = dbSNP_vcf,
dbSNP_vcf_index = dbSNP_vcf_index,
known_indels_sites_VCFs = known_indels_sites_VCFs,
known_indels_sites_indices = known_indels_sites_indices,
ref_dict = ref_dict,
ref_fasta = ref_fasta,
ref_fasta_index = ref_fasta_index,
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = agg_small_disk,
preemptible_tries = preemptible_tries
}
}
# Merge the recalibration reports resulting from by-interval recalibration
call GatherBqsrReports {
input:
input_bqsr_reports = BaseRecalibrator.recalibration_report,
output_report_filename = base_file_name + ".recal_data.csv",
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = flowcell_small_disk,
preemptible_tries = preemptible_tries
}
scatter (subgroup in CreateSequenceGroupingTSV.sequence_grouping_with_unmapped) {
# Apply the recalibration model by interval
call ApplyBQSR {
input:
input_bam = SortAndFixTags.output_bam,
input_bam_index = SortAndFixTags.output_bam_index,
output_bam_basename = base_file_name + ".aligned.duplicates_marked.recalibrated",
recalibration_report = GatherBqsrReports.output_bqsr_report,
sequence_group_interval = subgroup,
ref_dict = ref_dict,
ref_fasta = ref_fasta,
ref_fasta_index = ref_fasta_index,
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = agg_small_disk,
preemptible_tries = preemptible_tries
}
}
# Merge the recalibrated BAM files resulting from by-interval recalibration
call GatherBamFiles {
input:
input_bams = ApplyBQSR.recalibrated_bam,
output_bam_basename = base_file_name,
docker_image = gatk_docker,
gatk_path = gatk_path,
disk_size = agg_large_disk,
preemptible_tries = preemptible_tries,
compression_level = compression_level
}
# Outputs that will be retained when execution is complete
output {
File duplication_metrics = MarkDuplicates.duplicate_metrics
File bqsr_report = GatherBqsrReports.output_bqsr_report
File analysis_ready_bam = GatherBamFiles.output_bam
File analysis_ready_bam_index = GatherBamFiles.output_bam_index
File analysis_ready_bam_md5 = GatherBamFiles.output_bam_md5
}
}
# TASK DEFINITIONS
# Get version of BWA
task GetBwaVersion {
Int preemptible_tries
String mem_size
String docker_image
String bwa_path
command {
# Not setting "set -o pipefail" here because /bwa has a rc=1 and we don't want to allow rc=1 to succeed
# because the sed may also fail with that error and that is something we actually want to fail on.
${bwa_path}bwa 2>&1 | \
grep -e '^Version' | \
sed 's/Version: //'
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
}
output {
String version = read_string(stdout())
}
}
# Read unmapped BAM, convert on-the-fly to FASTQ and stream to BWA MEM for alignment
task SamToFastqAndBwaMem {
File input_bam
String bwa_commandline
String output_bam_basename
File ref_fasta
File ref_fasta_index
File ref_dict
# This is the .alt file from bwa-kit (https://github.com/lh3/bwa/tree/master/bwakit),
# listing the reference contigs that are "alternative". Leave blank in JSON for legacy
# references such as b37 and hg19.
File? ref_alt
File ref_amb
File ref_ann
File ref_bwt
File ref_pac
File ref_sa
Int compression_level
Int preemptible_tries
Int disk_size
String mem_size
String num_cpu
String docker_image
String bwa_path
String gotc_path
String java_opt
command <<<
set -o pipefail
set -e
# set the bash variable needed for the command-line
bash_ref_fasta=${ref_fasta}
java -Dsamjdk.compression_level=${compression_level} ${java_opt} -jar ${gotc_path}picard.jar \
SamToFastq \
INPUT=${input_bam} \
FASTQ=/dev/stdout \
INTERLEAVE=true \
NON_PF=true \
| \
${bwa_path}${bwa_commandline} /dev/stdin - 2> >(tee ${output_bam_basename}.bwa.stderr.log >&2) \
| \
samtools view -1 - > ${output_bam_basename}.bam
>>>
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
cpu: num_cpu
disk: disk_size + " GB"
}
output {
File output_bam = "${output_bam_basename}.bam"
File bwa_stderr_log = "${output_bam_basename}.bwa.stderr.log"
}
}
# Merge original input uBAM file with BWA-aligned BAM file
task MergeBamAlignment {
File unmapped_bam
String bwa_commandline
String bwa_version
File aligned_bam
String output_bam_basename
File ref_fasta
File ref_fasta_index
File ref_dict
Int compression_level
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt
command {
# set the bash variable needed for the command-line
bash_ref_fasta=${ref_fasta}
${gatk_path} --java-options "-Dsamjdk.compression_level=${compression_level} ${java_opt}" \
MergeBamAlignment \
--VALIDATION_STRINGENCY SILENT \
--EXPECTED_ORIENTATIONS FR \
--ATTRIBUTES_TO_RETAIN X0 \
--ALIGNED_BAM ${aligned_bam} \
--UNMAPPED_BAM ${unmapped_bam} \
--OUTPUT ${output_bam_basename}.bam \
--REFERENCE_SEQUENCE ${ref_fasta} \
--PAIRED_RUN true \
--SORT_ORDER "unsorted" \
--IS_BISULFITE_SEQUENCE false \
--ALIGNED_READS_ONLY false \
--CLIP_ADAPTERS false \
--MAX_RECORDS_IN_RAM 2000000 \
--ADD_MATE_CIGAR true \
--MAX_INSERTIONS_OR_DELETIONS -1 \
--PRIMARY_ALIGNMENT_STRATEGY MostDistant \
--PROGRAM_RECORD_ID "bwamem" \
--PROGRAM_GROUP_VERSION "${bwa_version}" \
--PROGRAM_GROUP_COMMAND_LINE "${bwa_commandline}" \
--PROGRAM_GROUP_NAME "bwamem" \
--UNMAPPED_READ_STRATEGY COPY_TO_TAG \
--ALIGNER_PROPER_PAIR_FLAGS true \
--UNMAP_CONTAMINANT_READS true
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File output_bam = "${output_bam_basename}.bam"
}
}
# Sort BAM file by coordinate order and fix tag values for NM and UQ
task SortAndFixTags {
File input_bam
String output_bam_basename
File ref_dict
File ref_fasta
File ref_fasta_index
Int compression_level
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt_sort
String java_opt_fix
command {
set -o pipefail
${gatk_path} --java-options "-Dsamjdk.compression_level=${compression_level} ${java_opt_sort}" \
SortSam \
--INPUT ${input_bam} \
--OUTPUT /dev/stdout \
--SORT_ORDER "coordinate" \
--CREATE_INDEX false \
--CREATE_MD5_FILE false \
| \
${gatk_path} --java-options "-Dsamjdk.compression_level=${compression_level} ${java_opt_fix}" \
SetNmMdAndUqTags \
--INPUT /dev/stdin \
--OUTPUT ${output_bam_basename}.bam \
--CREATE_INDEX true \
--CREATE_MD5_FILE true \
--REFERENCE_SEQUENCE ${ref_fasta}
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File output_bam = "${output_bam_basename}.bam"
File output_bam_index = "${output_bam_basename}.bai"
File output_bam_md5 = "${output_bam_basename}.bam.md5"
}
}
# Mark duplicate reads to avoid counting non-independent observations
task MarkDuplicates {
Array[File] input_bams
String output_bam_basename
String metrics_filename
Int compression_level
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt
# Task is assuming query-sorted input so that the Secondary and Supplementary reads get marked correctly.
# This works because the output of BWA is query-grouped and therefore, so is the output of MergeBamAlignment.
# While query-grouped isn't actually query-sorted, it's good enough for MarkDuplicates with ASSUME_SORT_ORDER="queryname"
command {
${gatk_path} --java-options "-Dsamjdk.compression_level=${compression_level} ${java_opt}" \
MarkDuplicates \
--INPUT ${sep=' --INPUT ' input_bams} \
--OUTPUT ${output_bam_basename}.bam \
--METRICS_FILE ${metrics_filename} \
--VALIDATION_STRINGENCY SILENT \
--OPTICAL_DUPLICATE_PIXEL_DISTANCE 2500 \
--ASSUME_SORT_ORDER "queryname" \
--CREATE_MD5_FILE true
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File output_bam = "${output_bam_basename}.bam"
File duplicate_metrics = "${metrics_filename}"
}
}
# Generate sets of intervals for scatter-gathering over chromosomes
task CreateSequenceGroupingTSV {
File ref_dict
Int preemptible_tries
String mem_size
String docker_image
# Use python to create the Sequencing Groupings used for BQSR and PrintReads Scatter.
# It outputs to stdout where it is parsed into a wdl Array[Array[String]]
# e.g. [["1"], ["2"], ["3", "4"], ["5"], ["6", "7", "8"]]
command <<<
python <<CODE
with open("${ref_dict}", "r") as ref_dict_file:
sequence_tuple_list = []
longest_sequence = 0
for line in ref_dict_file:
if line.startswith("@SQ"):
line_split = line.split("\t")
# (Sequence_Name, Sequence_Length)
sequence_tuple_list.append((line_split[1].split("SN:")[1], int(line_split[2].split("LN:")[1])))
longest_sequence = sorted(sequence_tuple_list, key=lambda x: x[1], reverse=True)[0][1]
# We are adding this to the intervals because hg38 has contigs named with embedded colons (:) and a bug in
# some versions of GATK strips off the last element after a colon, so we add this as a sacrificial element.
hg38_protection_tag = ":1+"
# initialize the tsv string with the first sequence
tsv_string = sequence_tuple_list[0][0] + hg38_protection_tag
temp_size = sequence_tuple_list[0][1]
for sequence_tuple in sequence_tuple_list[1:]:
if temp_size + sequence_tuple[1] <= longest_sequence:
temp_size += sequence_tuple[1]
tsv_string += "\t" + sequence_tuple[0] + hg38_protection_tag
else:
tsv_string += "\n" + sequence_tuple[0] + hg38_protection_tag
temp_size = sequence_tuple[1]
# add the unmapped sequences as a separate line to ensure that they are recalibrated as well
with open("sequence_grouping.txt","w") as tsv_file:
tsv_file.write(tsv_string)
tsv_file.close()
tsv_string += '\n' + "unmapped"
with open("sequence_grouping_with_unmapped.txt","w") as tsv_file_with_unmapped:
tsv_file_with_unmapped.write(tsv_string)
tsv_file_with_unmapped.close()
CODE
>>>
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
}
output {
Array[Array[String]] sequence_grouping = read_tsv("sequence_grouping.txt")
Array[Array[String]] sequence_grouping_with_unmapped = read_tsv("sequence_grouping_with_unmapped.txt")
}
}
# Generate Base Quality Score Recalibration (BQSR) model
task BaseRecalibrator {
File input_bam
File input_bam_index
String recalibration_report_filename
Array[String] sequence_group_interval
File dbSNP_vcf
File dbSNP_vcf_index
Array[File] known_indels_sites_VCFs
Array[File] known_indels_sites_indices
File ref_dict
File ref_fasta
File ref_fasta_index
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt
command {
${gatk_path} --java-options "${java_opt}" \
BaseRecalibrator \
-R ${ref_fasta} \
-I ${input_bam} \
--use-original-qualities \
-O ${recalibration_report_filename} \
--known-sites ${dbSNP_vcf} \
--known-sites ${sep=" --known-sites " known_indels_sites_VCFs} \
-L ${sep=" -L " sequence_group_interval}
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File recalibration_report = "${recalibration_report_filename}"
}
}
# Combine multiple recalibration tables from scattered BaseRecalibrator runs
# Note that when run from GATK 3.x the tool is not a walker and is invoked differently.
task GatherBqsrReports {
Array[File] input_bqsr_reports
String output_report_filename
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt
command {
${gatk_path} --java-options "${java_opt}" \
GatherBQSRReports \
-I ${sep=' -I ' input_bqsr_reports} \
-O ${output_report_filename}
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File output_bqsr_report = "${output_report_filename}"
}
}
# Apply Base Quality Score Recalibration (BQSR) model
task ApplyBQSR {
File input_bam
File input_bam_index
String output_bam_basename
File recalibration_report
Array[String] sequence_group_interval
File ref_dict
File ref_fasta
File ref_fasta_index
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt
command {
${gatk_path} --java-options "${java_opt}" \
ApplyBQSR \
-R ${ref_fasta} \
-I ${input_bam} \
-O ${output_bam_basename}.bam \
-L ${sep=" -L " sequence_group_interval} \
-bqsr ${recalibration_report} \
--static-quantized-quals 10 --static-quantized-quals 20 --static-quantized-quals 30 \
--add-output-sam-program-record \
--create-output-bam-md5 \
--use-original-qualities
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File recalibrated_bam = "${output_bam_basename}.bam"
}
}
# Combine multiple recalibrated BAM files from scattered ApplyRecalibration runs
task GatherBamFiles {
Array[File] input_bams
String output_bam_basename
Int compression_level
Int preemptible_tries
Int disk_size
String mem_size
String docker_image
String gatk_path
String java_opt
command {
${gatk_path} --java-options "-Dsamjdk.compression_level=${compression_level} ${java_opt}" \
GatherBamFiles \
--INPUT ${sep=' --INPUT ' input_bams} \
--OUTPUT ${output_bam_basename}.bam \
--CREATE_INDEX true \
--CREATE_MD5_FILE true
}
runtime {
maxRetries: preemptible_tries
docker: docker_image
memory: mem_size
disk: disk_size + " GB"
}
output {
File output_bam = "${output_bam_basename}.bam"
File output_bam_index = "${output_bam_basename}.bai"
File output_bam_md5 = "${output_bam_basename}.bam.md5"
}
}