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cnv_somatic_panel_workflow.wdl
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cnv_somatic_panel_workflow.wdl
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# Workflow for creating a GATK CNV Panel of Normals given a list of normal samples. Supports both WGS and WES.
#
# Notes:
#
# - The intervals argument is required for both WGS and WES workflows and accepts formats compatible with the
# GATK -L argument (see https://gatkforums.broadinstitute.org/gatk/discussion/11009/intervals-and-interval-lists).
# These intervals will be padded on both sides by the amount specified by padding (default 250)
# and split into bins of length specified by bin_length (default 1000; specify 0 to skip binning,
# e.g., for WES). For WGS, the intervals should simply cover the autosomal chromosomes (sex chromosomes may be
# included, but care should be taken to 1) avoid creating panels of mixed sex, and 2) denoise case samples only
# with panels containing only individuals of the same sex as the case samples).
#
# - Intervals can be blacklisted from coverage collection and all downstream steps by using the blacklist_intervals
# argument, which accepts formats compatible with the GATK -XL argument
# (see https://gatkforums.broadinstitute.org/gatk/discussion/11009/intervals-and-interval-lists).
# This may be useful for excluding centromeric regions, etc. from analysis. Alternatively, these regions may
# be manually filtered from the final callset.
#
# A reasonable blacklist for excluded intervals (-XL) can be found at:
# hg19: gs://gatk-best-practices/somatic-b37/CNV_and_centromere_blacklist.hg19.list
# hg38: gs://gatk-best-practices/somatic-hg38/CNV_and_centromere_blacklist.hg38liftover.list (untested)
#
# - Example invocation:
#
# java -jar cromwell.jar run cnv_somatic_panel_workflow.wdl -i my_parameters.json
#
#############
#import "../cnv_common_tasks.wdl" as CNVTasks
import "https://raw.githubusercontent.com/gatk-workflows/gatk4-somatic-cnvs/1.4.0/tasks/cnv_common_tasks.wdl" as CNVTasks
workflow CNVSomaticPanelWorkflow {
##################################
#### required basic arguments ####
##################################
File intervals
File? blacklist_intervals
Array[String] normal_bams
Array[String] normal_bais
String pon_entity_id
File ref_fasta_dict
File ref_fasta_fai
File ref_fasta
String gatk_docker
##################################
#### optional basic arguments ####
##################################
# If true, AnnotateIntervals will be run to create GC annotations and explicit GC correction
# will be performed by the PoN generated by CreateReadCountPanelOfNormals before PCA is performed on subsequent cases
Boolean? do_explicit_gc_correction
File? gatk4_jar_override
Int? preemptible_attempts
####################################################
#### optional arguments for PreprocessIntervals ####
####################################################
Int? padding
Int? bin_length
Int? mem_gb_for_preprocess_intervals
##################################################
#### optional arguments for AnnotateIntervals ####
##################################################
File? mappability_track_bed
File? mappability_track_bed_idx
File? segmental_duplication_track_bed
File? segmental_duplication_track_bed_idx
Int? feature_query_lookahead
Int? mem_gb_for_annotate_intervals
##############################################
#### optional arguments for CollectCounts ####
##############################################
String? collect_counts_format
Int? mem_gb_for_collect_counts
##############################################################
#### optional arguments for CreateReadCountPanelOfNormals ####
##############################################################
Float? minimum_interval_median_percentile
Float? maximum_zeros_in_sample_percentage
Float? maximum_zeros_in_interval_percentage
Float? extreme_sample_median_percentile
Boolean? do_impute_zeros
Float? extreme_outlier_truncation_percentile
Int? number_of_eigensamples
Int? maximum_chunk_size
Int? mem_gb_for_create_read_count_pon
Array[Pair[String, String]] normal_bams_and_bais = zip(normal_bams, normal_bais)
call CNVTasks.PreprocessIntervals {
input:
intervals = intervals,
blacklist_intervals = blacklist_intervals,
ref_fasta = ref_fasta,
ref_fasta_fai = ref_fasta_fai,
ref_fasta_dict = ref_fasta_dict,
padding = padding,
bin_length = bin_length,
gatk4_jar_override = gatk4_jar_override,
gatk_docker = gatk_docker,
mem_gb = mem_gb_for_preprocess_intervals,
preemptible_attempts = preemptible_attempts
}
if (select_first([do_explicit_gc_correction, true])) {
call CNVTasks.AnnotateIntervals {
input:
intervals = PreprocessIntervals.preprocessed_intervals,
ref_fasta = ref_fasta,
ref_fasta_fai = ref_fasta_fai,
ref_fasta_dict = ref_fasta_dict,
mappability_track_bed = mappability_track_bed,
mappability_track_bed_idx = mappability_track_bed_idx,
segmental_duplication_track_bed = segmental_duplication_track_bed,
segmental_duplication_track_bed_idx = segmental_duplication_track_bed_idx,
feature_query_lookahead = feature_query_lookahead,
gatk4_jar_override = gatk4_jar_override,
gatk_docker = gatk_docker,
mem_gb = mem_gb_for_annotate_intervals,
preemptible_attempts = preemptible_attempts
}
}
scatter (normal_bam_and_bai in normal_bams_and_bais) {
call CNVTasks.CollectCounts {
input:
intervals = PreprocessIntervals.preprocessed_intervals,
bam = normal_bam_and_bai.left,
bam_idx = normal_bam_and_bai.right,
ref_fasta = ref_fasta,
ref_fasta_fai = ref_fasta_fai,
ref_fasta_dict = ref_fasta_dict,
format = collect_counts_format,
gatk4_jar_override = gatk4_jar_override,
gatk_docker = gatk_docker,
mem_gb = mem_gb_for_collect_counts,
preemptible_attempts = preemptible_attempts
}
}
call CreateReadCountPanelOfNormals {
input:
pon_entity_id = pon_entity_id,
read_count_files = CollectCounts.counts,
minimum_interval_median_percentile = minimum_interval_median_percentile,
maximum_zeros_in_sample_percentage = maximum_zeros_in_sample_percentage,
maximum_zeros_in_interval_percentage = maximum_zeros_in_interval_percentage,
extreme_sample_median_percentile = extreme_sample_median_percentile,
do_impute_zeros = do_impute_zeros,
extreme_outlier_truncation_percentile = extreme_outlier_truncation_percentile,
number_of_eigensamples = number_of_eigensamples,
maximum_chunk_size = maximum_chunk_size,
annotated_intervals = AnnotateIntervals.annotated_intervals,
gatk4_jar_override = gatk4_jar_override,
gatk_docker = gatk_docker,
mem_gb = mem_gb_for_create_read_count_pon,
preemptible_attempts = preemptible_attempts
}
output {
File preprocessed_intervals = PreprocessIntervals.preprocessed_intervals
Array[String] read_counts_entity_ids = CollectCounts.entity_id
Array[File] read_counts = CollectCounts.counts
File read_count_pon = CreateReadCountPanelOfNormals.read_count_pon
}
}
task CreateReadCountPanelOfNormals {
String pon_entity_id
Array[File] read_count_files
Float? minimum_interval_median_percentile
Float? maximum_zeros_in_sample_percentage
Float? maximum_zeros_in_interval_percentage
Float? extreme_sample_median_percentile
Boolean? do_impute_zeros
Float? extreme_outlier_truncation_percentile
Int? number_of_eigensamples
Int? maximum_chunk_size
File? annotated_intervals #do not perform explicit GC correction by default
File? gatk4_jar_override
# Runtime parameters
String gatk_docker
Int? mem_gb
Int? disk_space_gb
Boolean use_ssd = false
Int? cpu
Int? preemptible_attempts
Int machine_mem_mb = select_first([mem_gb, 7]) * 1000
Int command_mem_mb = machine_mem_mb - 500
command <<<
set -e
export GATK_LOCAL_JAR=${default="/root/gatk.jar" gatk4_jar_override}
gatk --java-options "-Xmx${command_mem_mb}m" CreateReadCountPanelOfNormals \
--input ${sep=" --input " read_count_files} \
--minimum-interval-median-percentile ${default="10.0" minimum_interval_median_percentile} \
--maximum-zeros-in-sample-percentage ${default="5.0" maximum_zeros_in_sample_percentage} \
--maximum-zeros-in-interval-percentage ${default="5.0" maximum_zeros_in_interval_percentage} \
--extreme-sample-median-percentile ${default="2.5" extreme_sample_median_percentile} \
--do-impute-zeros ${default="true" do_impute_zeros} \
--extreme-outlier-truncation-percentile ${default="0.1" extreme_outlier_truncation_percentile} \
--number-of-eigensamples ${default="20" number_of_eigensamples} \
--maximum-chunk-size ${default="16777216" maximum_chunk_size} \
${"--annotated-intervals " + annotated_intervals} \
--output ${pon_entity_id}.pon.hdf5
>>>
runtime {
docker: "${gatk_docker}"
memory: machine_mem_mb + " MB"
disks: "local-disk " + select_first([disk_space_gb, 150]) + if use_ssd then " SSD" else " HDD"
cpu: select_first([cpu, 1])
preemptible: select_first([preemptible_attempts, 2])
}
output {
File read_count_pon = "${pon_entity_id}.pon.hdf5"
}
}