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Kids First DRC GATK Mutect2 Variant Calling Subworkflow

This is a subworkflow that is part of the Kids First DRC Somatic Variant Workflow that can be run as standalone. Mutect2 is a variant caller that calls single nucleotide variants, multi-nucleotide variants, and small insertions and deletions.

Mutect2 workflow diagram In general, Mutect2 is run following the Broad Best Practices, as of this workflow. This subworkflow does the following things as described below:

  1. Run the Mutect2 variant caller tool, scattering on the input interval list for greater speed
  2. Filter tables are created using steps as outlined in section II of this documentation
  3. Scattered mutect2 VCF results are merged into a single VCF
  4. Stats tables from Mutect2 run are merged
  5. Merged stats table and outputs from filter tables are used to populate the FILTER attribute in the merged Mutect2 VCF. This creates the pre-pass VCF containing PASS and variants with various failing-to-pass criteria in the FILTER field
  6. Select PASS step subsets the pre-pass VCF to PASS only
  7. Annotate the PASS VCF using the annotation sub workflow - most relevant information is here!
  8. Rename outputs to fit a standard format

Workflow Description and KF Recommended Inputs

This workflow runs Mutect2 4.1.1.0 which calls single nucleotide variants (SNV) and insertions/deletions (INDEL).

General Workflow Inputs

inputs:
  indexed_reference_fasta: {type: File, secondaryFiles: [.fai, ^.dict]}
  reference_dict: File
  bed_invtl_split: {type: 'File[]', doc: "Bed file intervals passed on from and outside pre-processing step"}
  af_only_gnomad_vcf: {type: File, secondaryFiles: ['.tbi']}
  exac_common_vcf: {type: File, secondaryFiles: ['.tbi']}
  input_tumor_aligned:
    type: File
    secondaryFiles: |
      ${
        var dpath = self.location.replace(self.basename, "")
        if(self.nameext == '.bam'){
          return {"location": dpath+self.nameroot+".bai", "class": "File"}
        }
        else{
          return {"location": dpath+self.basename+".crai", "class": "File"}
        }
      }
    doc: "tumor BAM or CRAM"

  input_tumor_name: string
  input_normal_aligned:
    type: File
    secondaryFiles: |
      ${
        var dpath = self.location.replace(self.basename, "")
        if(self.nameext == '.bam'){
          return {"location": dpath+self.nameroot+".bai", "class": "File"}
        }
        else{
          return {"location": dpath+self.basename+".crai", "class": "File"}
        }
      }
    doc: "normal BAM or CRAM"

  input_normal_name: string
  exome_flag: {type: ['null', string], doc: "set to 'Y' for exome mode"}
  output_basename: string
  getpileup_memory: {type: int?}
  learnorientation_memory: {type: int?}
  filtermutectcalls_memory: {type: int?}
  select_vars_mode: {type: ['null', {type: enum, name: select_vars_mode, symbols: ["gatk", "grep"]}], doc: "Choose 'gatk' for SelectVariants tool, or 'grep' for grep expression", default: "gatk"}
  tool_name: {type: string?, doc: "String to describe what tool was run as part of file name", default: "mutect2_somatic"}

  # VEP params
  vep_cache: {type: 'File', doc: "tar gzipped cache from ensembl/local converted cache",  "sbg:suggestedValue": {class: File, path: 6332f8e47535110eb79c794f,
      name: homo_sapiens_merged_vep_105_indexed_GRCh38.tar.gz}}
  vep_ram: {type: 'int?', default: 32, doc: "In GB, may need to increase this value depending on the size/complexity of input"}
  vep_cores: {type: 'int?', default: 16, doc: "Number of cores to use. May need to increase for really large inputs"}
  vep_buffer_size: {type: 'int?', default: 1000, doc: "Increase or decrease to balance speed and memory usage"}
  dbnsfp: { type: 'File?', secondaryFiles: [.tbi,^.readme.txt], doc: "VEP-formatted plugin file, index, and readme file containing dbNSFP annotations" }
  dbnsfp_fields: { type: 'string?', doc: "csv string with desired fields to annotate. Use ALL to grab all"}
  merged: { type: 'boolean?', doc: "Set to true if merged cache used", default: true }
  cadd_indels: { type: 'File?', secondaryFiles: [.tbi], doc: "VEP-formatted plugin file and index containing CADD indel annotations" }
  cadd_snvs: { type: 'File?', secondaryFiles: [.tbi], doc: "VEP-formatted plugin file and index containing CADD SNV annotations" }
  run_cache_existing: { type: 'boolean?', doc: "Run the check_existing flag for cache" }
  run_cache_af: { type: 'boolean?', doc: "Run the allele frequency flags for cache" }

  # annotation vars
  genomic_hotspots: { type: 'File[]?', doc: "Tab-delimited BED formatted file(s) containing hg38 genomic positions corresponding to hotspots", "sbg:suggestedValue": [{class: File, path: 607713829360f10e3982a423, name: tert.bed}] }
  protein_snv_hotspots: { type: 'File[]?', doc: "Column-name-containing, tab-delimited file(s) containing protein names and amino acid positions corresponding to hotspots", "sbg:suggestedValue": [{class: File, path: 66980e845a58091951d53984, name: kfdrc_protein_snv_cancer_hotspots_20240718.txt}] }
  protein_indel_hotspots: { type: 'File[]?', doc: "Column-name-containing, tab-delimited file(s) containing protein names and amino acid position ranges corresponding to hotspots", "sbg:suggestedValue": [{class: File, path: 663d2bcc27374715fccd8c6f, name: protein_indel_cancer_hotspots_v2.ENS105_liftover.tsv}] }
  retain_info: {type: 'string?', doc: "csv string with INFO fields that you want to keep", default: "gnomad_3_1_1_AC,gnomad_3_1_1_AN,gnomad_3_1_1_AF,gnomad_3_1_1_nhomalt,gnomad_3_1_1_AC_popmax,gnomad_3_1_1_AN_popmax,gnomad_3_1_1_AF_popmax,gnomad_3_1_1_nhomalt_popmax,gnomad_3_1_1_AC_controls_and_biobanks,gnomad_3_1_1_AN_controls_and_biobanks,gnomad_3_1_1_AF_controls_and_biobanks,gnomad_3_1_1_AF_non_cancer,gnomad_3_1_1_primate_ai_score,gnomad_3_1_1_splice_ai_consequence,MBQ,TLOD,HotSpotAllele"}
  retain_fmt: {type: 'string?', doc: "csv string with FORMAT fields that you want to keep"}
  retain_ann: { type: 'string?', doc: "csv string of annotations (within the VEP CSQ/ANN) to retain as extra columns in MAF", default: "HGVSg" }
  echtvar_anno_zips: {type: 'File[]?', doc: "Annotation ZIP files for echtvar anno"}
  bcftools_strip_columns: {type: 'string?', doc: "csv string of columns to strip if needed to avoid conflict, i.e INFO/AF"}
  bcftools_public_filter: {type: 'string?', doc: "Will hard filter final result to create a public version", default: FILTER="PASS"|INFO/HotSpotAllele=1}
  gatk_filter_name: {type: 'string[]', doc: "Array of names for each filter tag to add, recommend: [\"NORM_DP_LOW\", \"GNOMAD_AF_HIGH\"]"}
  gatk_filter_expression: {type: 'string[]', doc: "Array of filter expressions to establish criteria to tag variants with. See https://gatk.broadinstitute.org/hc/en-us/articles/360036730071-VariantFiltration, recommend: \"vc.getGenotype('\" + inputs.input_normal_name + \"').getDP() <= 7\"), \"gnomad_3_1_1_AF != '.' && gnomad_3_1_1_AF > 0.001\"]"}
  disable_hotspot_annotation: { type: 'boolean?', doc: "Disable Hotspot Annotation and skip this task.", default: false }
  maf_center: {type: 'string?', doc: "Sequencing center of variant called", default: "."}

Recommended reference inputs - all file references can be obtained here*

Secondary files needed for each reference file will be a sub-bullet point.

  • indexed_reference_fasta: Homo_sapiens_assembly38.fasta
    • Homo_sapiens_assembly38.fasta.fai
    • Homo_sapiens_assembly38.dict
  • reference_dict: Homo_sapiens_assembly38.dict
  • hg38_strelka_bed: hg38_strelka.bed.gz
  • bed_invtl_split: an array of bed intervals, would be created by a wrapper workflow
  • af_only_gnomad_vcf: af-only-gnomad.hg38.vcf.gz
    • af-only-gnomad.hg38.vcf.gz.tbi
  • echtvar_anno_zips: gnomad_3_1_1.vwb_subset.echtvar_0_1_9.zip
  • exac_common_vcf: small_exac_common_3.hg38.vcf.gz
    • small_exac_common_3.hg38.vcf.gz.tbi

Source-specific inputs

  • exome_flag
    • Y if exome
    • N or leave blank if WGS

Worflow performance tuning

Occassionally, default memory values may not suffice to process the inputs. Edit where needed to unblock these steps. All memory units are in whole GB

  getpileup_memory: {type: int?}
  learnorientation_memory: {type: int?}
  filtermutectcalls_memory: {type: int?}

Workflow outputs

outputs:
  mutect2_filtered_stats: {type: File, outputSource: filter_mutect2_vcf/stats_table}
  mutect2_filtered_vcf: {type: File, outputSource: filter_mutect2_vcf/filtered_vcf}
  mutect2_protected_outputs: {type: 'File[]', outputSource: rename_protected/renamed_files}
  mutect2_public_outputs: {type: 'File[]', outputSource: rename_public/renamed_files}
  • mutect2_filtered_stats: Summary table of FILTER stats
  • mutect2_filtered_vcf: VCF with SNV, MNV, and INDEL variant calls. Contains all soft FILTER values generated by variant caller. Use this file if you believe important variants are being left out when using the algorithm's PASS filter
  • mutect2_protected_outputs: Array of files containing MAF format of PASS hits, PASS VCF with annotation pipeline soft FILTER-added values, and VCF index
  • mutect2_public_outputs: Same as above, except MAF and VCF have had entries with soft FILTER values removed