callability

Workflow to calculate the callability of a matched tumour sample, where callability is defined as the percentage of genomic regions where a normal and a tumor bam coverage is greater than a threshold(s).

Overview

Dependencies

• mosdepth 0.2.9
• bedtools 2.27
• python 3.7

Usage

Cromwell

java -jar cromwell.jar run callability.wdl --inputs inputs.json

Inputs

Required workflow parameters:

Parameter	Value	Description
normalBam	File	Normal bam input file.
normalBamIndex	File	Normal bam index input file.
tumorBam	File	Tumor bam input file.
tumorBamIndex	File	Tumor bam index input file.
normalMinCoverage	Int	Normal must have at least this coverage to be considered callable.
tumorMinCoverage	Int	Tumor must have at least this coverage to be considered callable.
intervalFile	String	The interval file of regions to calculate callability on.

Optional workflow parameters:

Parameter	Value	Default	Description

Optional task parameters:

Parameter	Value	Default	Description
calculateCallability.threads	Int	4	The number of threads to run mosdepth with.
calculateCallability.outputFileNamePrefix	String?	None	Output files will be prefixed with this.
calculateCallability.outputFileName	String	"callability_metrics.json"	Output callability metrics file name.
calculateCallability.jobMemory	Int	8	Memory allocated to job (in GB).
calculateCallability.cores	Int	1	The number of cores to allocate to the job.
calculateCallability.timeout	Int	12	Maximum amount of time (in hours) the task can run for.
calculateCallability.modules	String	"mosdepth/0.2.9 bedtools/2.27 python/3.7"	Environment module name and version to load (space separated) before command execution.

Outputs

Output	Type	Description	Labels
callabilityMetrics	File	Json file with pass, fail and callability percent (# of pass bases / # total bases)	vidarr_label: callabilityMetrics

Commands

This section lists command(s) run by callability workflow

• Running callability

Use mosdepth to merge neighboring regions, bedtools to get an intersection of the normal and tumor bam coverage by region, and then use awk to count the regions passing a coverage threshold:

   #export variables with mosdepth uses to add a fourth column to a new bed, merging neighboring regions if CALLABLE or LOW_COVERAGE
   export MOSDEPTH_Q0=LOW_COVERAGE
   export MOSDEPTH_Q1=CALLABLE
   mosdepth -t ~{threads} -n --quantize 0:~{normalMinCoverage - 1}: normal ~{normalBam}
   mosdepth -t ~{threads} -n --quantize 0:~{tumorMinCoverage - 1}: tumor ~{tumorBam}
   zcat normal.quantized.bed.gz | awk '$4 == "CALLABLE"' | bedtools intersect -a stdin -b ~{intervalFile} > normal.callable
   zcat tumor.quantized.bed.gz | awk '$4 == "CALLABLE"' | bedtools intersect -a stdin -b ~{intervalFile} > tumor.callable
 
   PASS="$(bedtools intersect -a normal.callable -b tumor.callable -wao | awk '{sum+=$9} END{print sum}')"
   TOTAL="$(zcat -f ~{intervalFile} | awk -F'\t' 'BEGIN{SUM=0}{SUM+=$3-$2} END{print SUM}')"
 
   python3 <<CODE
   import json
 
   total_count = int(float("${TOTAL}"))
   pass_count = int(float("${PASS}"))
   fail_count = total_count - pass_count
   if pass_count == 0 and fail_count == 0:
       callability = 0
   else:
       callability = pass_count / (pass_count + fail_count)
 
   metrics = {
       "pass": pass_count,
       "fail": fail_count,
       "callability": round(callability, 6),
       "normal_min_coverage": ~{normalMinCoverage},
       "tumor_min_coverage": ~{tumorMinCoverage}
   }
   with open('~{outputFileNamePrefix}~{outputFileName}', 'w') as json_file:
       json.dump(metrics, json_file)
   CODE

Support

For support, please file an issue on the Github project or send an email to [email protected] .

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