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WDL

README.md

ichorCNA-wdl

The ichorCNA.wdl workflow has two tasks:

  • read_counter
  • run_ichorCNA

The wdl calls a scatter and calls both tasks for each sample in batchSamples.

The workflow outputs 8 files:

  • ~{sampleName}.bin~{binSize}.wig
  • ~{sampleId}.correctedDepth.txt
  • ~{sampleId}.cna.seg
  • ~{sampleId}.params.txt
  • ~{sampleId}.seg.txt
  • ~{sampleId}.seg
  • ~{sampleId}.RData
  • ~{sampleId}.plots.tar.gz

Configuration

ichorCNA.wdl file

taskDocker: By default the WDL pipeline is curently using this gavinhalab/ichorcna:0.0.1
taskCPU: CPU variable for run_ichorCNA. May have to edit read_counter CPU in its runtime section.
memory: This may have to be edited in both read_counter and run_ichorCNA in their runtime sections.

inputs.json file

In batchSamples: All sample information including any paired normal bams, or a normal panel, and genome build information for individual bams

"sampleName"
"sex"- use "None" if both male and female are in sample
"tumorBam"- Path to tumor bam file.
"tumorBai"- Path to tumor bam.bai file.
"normalBam"- Path to normal bam file. Can be null
"normalBai"- Path to normal bam.bai file. Can be null
"normalPanel"- Median corrected depth from panel of normals. Can specify the path to the file based on the genome used. Default: null. "genomeBuild"- hg19 or hg38 only, capitalization matters
"genomeStyle"- "NCBI" when hg19 or "UCSC" when hg38

Other inputs: Workflow level params

"ichorCNA.exons"- Path to bed file containing exon regions. Default: "NULL"
"ichorCNA.binSize"- "10kb" This must match binSizeNumeric
"ichorCNA.binSizeNumeric"- 10000, but must match the other binSize
"ichorCNA.qual"- Integer- i.e. 20
"ichorCNA.normal"- Initial normal contamination; can be more than one value if additional normal initializations are desired. Default: "0.5"
"ichorCNA.ploidy"- Initial tumour ploidy; can be more than one value if additional ploidy initializations are desired. Default: "2"
"ichorCNA.estimateNormal"- Estimate normal. These need to be quoted strings in all caps instead of Boolean b/c of R. Default: "TRUE"
"ichorCNA.estimatePloidy"- Estimate tumour ploidy. Default: "TRUE"
"ichorCNA.estimateClonality"- Estimate clonality. Default: "TRUE"
"ichorCNA.scStates"- Subclonal states to consider. Default: "NULL"
"ichorCNA.maxCN"- Total clonal CN states. Default: "7"
"ichorCNA.scPenalty"- penalize subclonal events - n-fold multiplier; n=1 for no penalty
"ichorCNA.includeHOMD"- If FALSE, then exclude HOMD state. Useful when using large bins (e.g. 1Mb). Default: "FALSE"
"ichorCNA.plotFileType"- File format for output plots. pdf or png. Default: "pdf"
"ichorCNA.plotYlim"- ylim to use for chromosome plots. Default: "c(-2,2)"
"ichorCNA.likModel"- Default: "t". if multisample, use "gauss"
"ichorCNA.minMapScore"- control segmentation - higher (e.g. 0.9999999) leads to higher specificity and fewer segments
"ichorCNA.maxFracGenomeSubclone"- Exclude solutions with subclonal genome fraction greater than this value. Default: "0.5"
"ichorCNA.maxFracCNASubclone"- Exclude solutions with fraction of subclonal events greater than this value. Default: "0.7"
"ichorCNA.normal2IgnoreSC"- Ignore subclonal analysis when initial normal setting >= this value
"ichorCNA.txnE"- Self-transition probability. Increase to decrease number of segments. Lower (e.g. 0.99) leads to higher sensitivity and more segments. Default: "0.9999999". Higher (e.g. 10000000) leads to higher specificity and fewer segments and lower (e.g. 100) leads to higher sensitivity and more segments
"ichorCNA.txnStrength"- Transition pseudo-counts. Exponent should be the same as the number of decimal places of txnE. Default: "1e+07"
"ichorCNA.fracReadsInChrYForMale"- Threshold for fraction of reads in chrY to assign as male. Default: "0.001"

Outputs

A complete list of outputs can be found in this Github wiki page along with parameter info.

Instructions to setup WDL Pipeline

  1. Pull the docker image using : docker pull gavinhalab/ichorcna:1.0.0

  2. To run WDL on your machine, first you need to install cromwell or run it in a workspace thathas Cromwell server configured already.

  3. The inputs.json file currently is created for one sample. To create one inputs.json file for multiple samples run:

    python3 create_input_json.py input_test_ichorCNA.csv inputs.json

This command creates a json file with all the samples present in the input input_test_ichorCNA.csv . Please check the format of the csv file.

Run mode

To do this, enter this into your terminal:

java -jar cromwell-XX.jar run ichorCNA.wdl -i inputs.json -o options.json

with XX being the version of cromwell you have. Make sure all of your files (WDL, input, options) are in the same folder as the cromwell .jar file.

To configure the workflow, add --options workflow_options.json to the line. More on workflow options.

Server mode

Here is a tutorial on how to run Cromwell's server mode. Skip the Five Minute Introduction if you've already downloaded Cromwell and familiar with it.

Instructions for FH users

Running via shiny app

Open the shiny app and connect to your cromwell server. Under "Submit a Workflow", upload the WDL and inputs.json. If you wanted to configure the workflow, take a workflow options file and upload it as well.

Running via command line

Log into your chosen node and set the current directory to where your ichorCNA.wdl, inputs.json, and your optional workflow_options.json files are stored. Load the modules necessary:

module load java

module load cromwell

To execute the WDL, do the following:

java -jar $EBROOTCROMWELL/cromwell.jar run ichorCNA.wdl -i inputs.json