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kfdrc-somatic-snv-annot-workflow.cwl
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kfdrc-somatic-snv-annot-workflow.cwl
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cwlVersion: v1.2
class: Workflow
id: kfdrc-somatic-snv-annot-wf
label: Kids First DRC Somatic SNV Annotation Workflow
doc: |
# Kids First DRC Somatic Variant Annotation Workflow
This is a subworkflow that is part of the Kids First DRC Somatic Variant Workflow that can be run as standalone.
Annotation of variant calls helps give context to the possible biological consequences of each variant.
It does the following things as described below:
1. Normalize VCF
1. Strip specified `INFO` and `FORMAT` fields (Only if adding a new annotation that clashes with existing)
1. Annotate with VEP - can be skipped if VEP run previously and downstream tools are to be repeated
1. Annotated with an additional vcf - optional, recommend using a gnomAD VCF with at least AF
1. Soft filter on remarkable variant characteristics
- KF recommends normal read depth <= 7 and gnomAD AF > 0.001 and gnomAD FILTER == PASS
- This output will be considered `protected`
1. Annotate with hotspots - KF recommends cancer genome hotspots v2, formatting required and explained below
1. Create MAF output using a modified version of MSKCC's vcf2maf
1. Hard filter on vcf based on user-specified criteria - this output would be considered `public`
1. Create MAF output based on `public` vcf
## Workflow Description and KF Recommended Inputs
The additional gnomAD annotation, hotspot annotation, and soft + hard filtering are part of process called "Germline Masking."
The purpose of this is to create outputs that are safe for public consumption by marking and creating a version of outputs deemed a "germline risk" based on specified criteria.
For KF, based on guidance from the Genomic Data Commons (GDC), this means filtering variants with a normal read depth of <= 7 reads and an AF score greater than 0.001 from a PASS gnomAD record.
The gnomAD AF filter is pretty intuitive - gnomAD is a database resource of variants and their estimated prevalence in the human population.
Therefore, a variant that is higher than the recommended threshold can be seen as a higher risk of being a common and identifiable variant, and a lower risk for being disease-causing.
Additionally, this AF score needs to come from a PASS/non-filtered record. Therefore, records that do not have their gnomad_FILTER set to PASS are not considered.
The normal depth argument may be less intuitive to some, and an example might help explain its importance:
You've got a somatic variant candidate, and the normal-sample reads all support the reference allele.
However, if there are only ~5 of those reads, then there's a decent chance that it's a germline het variant and you just didn't get lucky enough to see any alt reads in the normal sample.
A prior understanding that heterozygous germline variant are much more common than somatic variants informs this.
### Recommended reference inputs - all file references can be obtained [here](https://cavatica.sbgenomics.com/u/kfdrc-harmonization/kf-references/)
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`
- `echtvar_anno_zips`: `gnomad.v3.1.1.custom.echtvar.zip`
- `bcftools_strip_columns`: csv string of columns to strip if needed to avoid conflict, i.e INFO/AF
- `bcftools_public_filter`: 'FILTER="PASS"|INFO/HotSpotAllele=1'
- `gatk_filter_name`: ["NORM_DP_LOW", "GNOMAD_AF_HIGH"]
- `gatk_filter_expression`: ["vc.getGenotype('insert_normal_sample_name').getDP() <= 7", "gnomad_3_1_1_AF != '.' && gnomad_3_1_1_AF > 0.001 && gnomad_3_1_1_FILTER=='PASS'"] # NOTE!! Replace `insert_normal_sample_name` with the value you'd use for `input_normal_name`! # NOTE!! If your annotation includes dot values, those values must first be excluded! If they are not, GATK will error trying to convert those values!
- `vep_cache`: `homo_sapiens_merged_vep_105_indexed_GRCh38.tar.gz`
- `genomic_hotspots`: `tert.bed` # This file has two common TERT promoter gene hot spots
- `protein_snv_hotspots`: `kfdrc_protein_snv_cancer_hotspots_20240718.txt` # Column-name-containing, tab-delimited file(s) containing protein names and amino acid positions corresponding to hotspots. File header contains generation history
- `protein_indel_hotspots`: `protein_indel_cancer_hotspots_v2.ENS105_liftover.tsv` # A tsv formatted INDEL subset of https://www.cancerhotspots.org/files/hotspots_v2.xls
- `custom_enst`: `kf_isoform_override.tsv` # As of VEP 104, several genes have had their canonical transcripts redefined. While the VCF will have all possible isoforms, this affects maf file output and may results in representative protein changes that defy historical expectations
### Source-specific inputs
For each input, the sub-bullet refers to when to use the suggested input
- `add_common_fields`
- Strelka2 calls: `true`, *exception if already run previously and other downstream tools are being run*
- All others: `false`
- `retain_info` # This is fairly subjective, some useful columns unique from each caller to carry over from VCF to MAF
- Strelka2: "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,gnomad_3_1_1_AF_non_cancer_afr,gnomad_3_1_1_AF_non_cancer_ami,gnomad_3_1_1_AF_non_cancer_asj,gnomad_3_1_1_AF_non_cancer_eas,gnomad_3_1_1_AF_non_cancer_fin,gnomad_3_1_1_AF_non_cancer_mid,gnomad_3_1_1_AF_non_cancer_nfe,gnomad_3_1_1_AF_non_cancer_oth,gnomad_3_1_1_AF_non_cancer_raw,gnomad_3_1_1_AF_non_cancer_sas,gnomad_3_1_1_AF_non_cancer_amr,gnomad_3_1_1_AF_non_cancer_popmax,gnomad_3_1_1_AF_non_cancer_all_popmax,gnomad_3_1_1_FILTER,MQ,MQ0,QSI,HotSpotAllele"
- Mutect2: "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,gnomad_3_1_1_AF_non_cancer_afr,gnomad_3_1_1_AF_non_cancer_ami,gnomad_3_1_1_AF_non_cancer_asj,gnomad_3_1_1_AF_non_cancer_eas,gnomad_3_1_1_AF_non_cancer_fin,gnomad_3_1_1_AF_non_cancer_mid,gnomad_3_1_1_AF_non_cancer_nfe,gnomad_3_1_1_AF_non_cancer_oth,gnomad_3_1_1_AF_non_cancer_raw,gnomad_3_1_1_AF_non_cancer_sas,gnomad_3_1_1_AF_non_cancer_amr,gnomad_3_1_1_AF_non_cancer_popmax,gnomad_3_1_1_AF_non_cancer_all_popmax,gnomad_3_1_1_FILTER,MBQ,TLOD,HotSpotAllele"
- Lancet: "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,gnomad_3_1_1_AF_non_cancer_afr,gnomad_3_1_1_AF_non_cancer_ami,gnomad_3_1_1_AF_non_cancer_asj,gnomad_3_1_1_AF_non_cancer_eas,gnomad_3_1_1_AF_non_cancer_fin,gnomad_3_1_1_AF_non_cancer_mid,gnomad_3_1_1_AF_non_cancer_nfe,gnomad_3_1_1_AF_non_cancer_oth,gnomad_3_1_1_AF_non_cancer_raw,gnomad_3_1_1_AF_non_cancer_sas,gnomad_3_1_1_AF_non_cancer_amr,gnomad_3_1_1_AF_non_cancer_popmax,gnomad_3_1_1_AF_non_cancer_all_popmax,gnomad_3_1_1_FILTER,MS,FETS,HotSpotAllele"
- Vardict: "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,gnomad_3_1_1_AF_non_cancer_afr,gnomad_3_1_1_AF_non_cancer_ami,gnomad_3_1_1_AF_non_cancer_asj,gnomad_3_1_1_AF_non_cancer_eas,gnomad_3_1_1_AF_non_cancer_fin,gnomad_3_1_1_AF_non_cancer_mid,gnomad_3_1_1_AF_non_cancer_nfe,gnomad_3_1_1_AF_non_cancer_oth,gnomad_3_1_1_AF_non_cancer_raw,gnomad_3_1_1_AF_non_cancer_sas,gnomad_3_1_1_AF_non_cancer_amr,gnomad_3_1_1_AF_non_cancer_popmax,gnomad_3_1_1_AF_non_cancer_all_popmax,gnomad_3_1_1_FILTER,MSI,MSILEN,SOR,SSF,HotSpotAllele"
- Consensus: "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,gnomad_3_1_1_AF_non_cancer_afr,gnomad_3_1_1_AF_non_cancer_ami,gnomad_3_1_1_AF_non_cancer_asj,gnomad_3_1_1_AF_non_cancer_eas,gnomad_3_1_1_AF_non_cancer_fin,gnomad_3_1_1_AF_non_cancer_mid,gnomad_3_1_1_AF_non_cancer_nfe,gnomad_3_1_1_AF_non_cancer_oth,gnomad_3_1_1_AF_non_cancer_raw,gnomad_3_1_1_AF_non_cancer_sas,gnomad_3_1_1_AF_non_cancer_amr,gnomad_3_1_1_AF_non_cancer_popmax,gnomad_3_1_1_AF_non_cancer_all_popmax,gnomad_3_1_1_FILTER,MQ,MQ0,CAL,HotSpotAllele"
- `retain_ann` # Similar to above, if run for KF harmonization, recommend the following:
- Strelka2: "HGVSg"
- Mutect2: "HGVSg"
- Lancet: "HGVSg"
- Vardict: "HGVSg"
- Consensus: "HGVSg"
- `bcftools_strip_columns` # if reannotating an old file:
- "FILTER/GNOMAD_AF_HIGH,FILTER/NORM_DP_LOW,INFO/CSQ,INFO/HotSpotAllele" # recommended if re-annotating from an older VEP cache
- "FILTER/GNOMAD_AF_HIGH,FILTER/NORM_DP_LOW,INFO/HotSpotAllele" # recommended if repeating hot spot and want to keep VEP
- `bcftools_prefilter_csv` # if annotating a file with calls you want screen for, use this. i.e `FILTER="PASS"`
- `disable_norm` # set to `True` if existing input already normalized or of you have justification for skipping this step
- `disable_vep_annotation` # set to `True` if existing VEP annotation of file is ok
- `disable_hotspot_annotation` # set to `True` if existing HotSpot annotation is ok
- `tool_name`:
- `Strelka2`: `strelka2_somatic`
- `Mutect2`: `mutect2_somatic`
- `Lancet`: `lancet_somatic`
- `VarDict Java`: `vardict_somatic`
- `consensus`: `consensus_somatic`
- `vep_cores`: `16`
- `vep_ram`: `32`
- `vep_buffer`: `5000`
## Workflow outputs
- `annotated_protected`: `PASS` VCF with annotation pipeline soft `FILTER`-added values, VCF index, and MAF format of VCF
- `annotated_public_vcf`: Same as `annotated_protected`, hard-filtered to include `PASS` only
requirements:
- class: ScatterFeatureRequirement
- class: MultipleInputFeatureRequirement
- class: StepInputExpressionRequirement
- class: InlineJavascriptRequirement
inputs:
indexed_reference_fasta: {type: File, secondaryFiles: [.fai, ^.dict], "sbg:suggestedValue": {class: File, path: 60639014357c3a53540ca7a3,
name: Homo_sapiens_assembly38.fasta, secondaryFiles: [{class: File, path: 60639016357c3a53540ca7af, name: Homo_sapiens_assembly38.fasta.fai},
{class: File, path: 60639019357c3a53540ca7e7, name: Homo_sapiens_assembly38.dict}]}}
input_vcf: {type: 'File', secondaryFiles: ['.tbi'], doc: "Input vcf to annotate and soft filter"}
input_tumor_name: string
input_normal_name: string
add_common_fields: {type: 'boolean', doc: "Set to true if input is a strelka2 vcf that hasn't had common fields added", default: false}
bcftools_strip_columns: {type: 'string?', doc: "csv string of columns to strip if needed to avoid conflict, i.e INFO/AF"}
bcftools_prefilter_csv: {type: 'string?', doc: "csv of bcftools filter params if you want to prefilter before annotation"}
disable_norm: {type: 'boolean?', doc: "Skip normalization step. Not recommended unless input is already normalized", default: false}
disable_hotspot_annotation: {type: 'boolean?', doc: "Disable Hotspot Annotation and skip this task."}
disable_vep_annotation: {type: 'boolean?', doc: "Disable VEP Annotation and skip this task.", default: false}
echtvar_anno_zips: {type: 'File[]?', doc: "Annotation ZIP files for echtvar anno", "sbg:suggestedValue": [{class: File, path: 65c64d847dab7758206248c6,
name: gnomad.v3.1.1.custom.echtvar.zip}]}
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"}
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
for clues"}
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"}
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}}
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}
run_cache_existing: {type: 'boolean?', doc: "Run the check_existing flag for cache", default: true}
run_cache_af: {type: 'boolean?', doc: "Run the allele frequency flags for cache", default: true}
run_stats: {type: 'boolean?', doc: "Create stats file? Disable for speed", default: false}
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"}
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}]}
output_basename: string
tool_name: string
retain_info: {type: 'string?', doc: "csv string with INFO fields that you want to keep, i.e. for consensus `MQ,MQ0,CAL,Hotspot`"}
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"}
maf_center: {type: 'string?', doc: "Sequencing center of variant called", default: "."}
custom_enst: {type: 'File?', doc: "Use a file with ens tx IDs for each gene to override VEP PICK", "sbg:suggestedValue": {class: File,
path: 663d2bcc27374715fccd8c65, name: kf_isoform_override.tsv}}
outputs:
annotated_protected: {type: 'File[]', outputSource: rename_protected/renamed_files}
annotated_public: {type: 'File[]', outputSource: rename_public/renamed_files}
steps:
prefilter_vcf:
when: $(inputs.include_expression != null)
run: ../tools/bcftools_filter_vcf.cwl
in:
input_vcf: input_vcf
include_expression: bcftools_prefilter_csv
output_basename: output_basename
out: [filtered_vcf]
normalize_vcf:
when: $(inputs.disable_norm == false)
run: ../tools/normalize_vcf.cwl
in:
indexed_reference_fasta: indexed_reference_fasta
input_vcf:
source: [prefilter_vcf/filtered_vcf, input_vcf]
pickValue: first_non_null
output_basename: output_basename
tool_name: tool_name
disable_norm: disable_norm
out: [normalized_vcf]
bcftools_strip_info:
when: $(inputs.strip_info != null)
run: ../tools/bcftools_strip_ann.cwl
in:
input_vcf:
source: [normalize_vcf/normalized_vcf, prefilter_vcf/filtered_vcf, input_vcf]
pickValue: first_non_null
output_basename: output_basename
tool_name: tool_name
strip_info: bcftools_strip_columns
out: [stripped_vcf]
add_standard_fields:
run: ../tools/add_strelka2_fields.cwl
when: $(inputs.run_tool_flag)
in:
strelka2_vcf:
source: [bcftools_strip_info/stripped_vcf, normalize_vcf/normalized_vcf, prefilter_vcf/filtered_vcf, input_vcf]
pickValue: first_non_null
run_tool_flag: add_common_fields
tumor_name: input_tumor_name
normal_name: input_normal_name
output_basename: output_basename
out: [output]
vep_annotate_vcf:
when: $(inputs.disable_annotation == false)
run: ../tools/variant_effect_predictor_105.cwl
in:
reference: indexed_reference_fasta
disable_annotation: disable_vep_annotation
cores: vep_cores
ram: vep_ram
buffer_size: vep_buffer_size
input_vcf:
source: [add_standard_fields/output, bcftools_strip_info/stripped_vcf, normalize_vcf/normalized_vcf, prefilter_vcf/filtered_vcf,
input_vcf]
pickValue: first_non_null
output_basename: output_basename
tool_name: tool_name
cache: vep_cache
merged: merged
run_cache_existing: run_cache_existing
run_cache_af: run_cache_af
run_stats: run_stats
cadd_indels: cadd_indels
cadd_snvs: cadd_snvs
dbnsfp: dbnsfp
dbnsfp_fields: dbnsfp_fields
out: [output_vcf]
echtvar_anno_gnomad:
when: $(inputs.echtvar_zips != null)
run: ../tools/echtvar_anno.cwl
in:
input_vcf:
source: [vep_annotate_vcf/output_vcf, add_standard_fields/output, bcftools_strip_info/stripped_vcf, normalize_vcf/normalized_vcf,
prefilter_vcf/filtered_vcf, input_vcf]
pickValue: first_non_null
echtvar_zips: echtvar_anno_zips
tbi:
valueFrom: |
$(1 == 1)
output_filename:
source: [output_basename, tool_name]
valueFrom: |
$(self[0]).$(self[1]).echtvar_annotated.vcf.gz
out: [annotated_vcf]
gatk_add_soft_filter:
run: ../tools/gatk_variant_filter.cwl
in:
input_vcf:
source: [echtvar_anno_gnomad/annotated_vcf, vep_annotate_vcf/output_vcf, add_standard_fields/output, bcftools_strip_info/stripped_vcf,
normalize_vcf/normalized_vcf, prefilter_vcf/filtered_vcf, input_vcf]
pickValue: first_non_null
reference: indexed_reference_fasta
filter_name: gatk_filter_name
filter_expression: gatk_filter_expression
output_basename: output_basename
tool_name: tool_name
out: [gatk_soft_filtered_vcf]
hotspots_annotation:
run: ../tools/hotspots_annotation.cwl
in:
input_vcf: gatk_add_soft_filter/gatk_soft_filtered_vcf
disable_hotspot_annotation: disable_hotspot_annotation
genomic_hotspots: genomic_hotspots
protein_snvs: protein_snv_hotspots
protein_indels: protein_indel_hotspots
output_basename: output_basename
out: [hotspots_vcf]
kfdrc_vcf2maf_protected:
run: ../tools/kf_mskcc_vcf2maf.cwl
in:
reference: indexed_reference_fasta
input_vcf: hotspots_annotation/hotspots_vcf
output_basename: output_basename
tumor_id: input_tumor_name
normal_id: input_normal_name
tool_name: tool_name
retain_info: retain_info
retain_fmt: retain_fmt
retain_ann: retain_ann
maf_center: maf_center
custom_enst: custom_enst
out: [output_maf]
hard_filter_vcf:
run: ../tools/bcftools_filter_vcf.cwl
in:
input_vcf: hotspots_annotation/hotspots_vcf
include_expression: bcftools_public_filter
output_basename: output_basename
output_type:
valueFrom: "z"
out: [filtered_vcf]
kfdrc_vcf2maf_public:
run: ../tools/kf_mskcc_vcf2maf.cwl
in:
reference: indexed_reference_fasta
input_vcf: hard_filter_vcf/filtered_vcf
output_basename: output_basename
tumor_id: input_tumor_name
normal_id: input_normal_name
tool_name: tool_name
retain_info: retain_info
retain_fmt: retain_fmt
retain_ann: retain_ann
maf_center: maf_center
custom_enst: custom_enst
out: [output_maf]
rename_protected:
run: ../tools/generic_rename_outputs.cwl
label: Rename Protected Outputs
in:
input_files:
source: [hotspots_annotation/hotspots_vcf, kfdrc_vcf2maf_protected/output_maf]
valueFrom: |
$([self[0], self[0].secondaryFiles[0], self[1]])
rename_to:
source: [output_basename, tool_name]
valueFrom: |
${
var prefix = self[0] + '.' + self[1] + '.norm.annot.protected';
return [
prefix + '.vcf.gz',
prefix + '.vcf.gz.tbi',
prefix + '.maf'
];
}
out: [renamed_files]
rename_public:
run: ../tools/generic_rename_outputs.cwl
label: Rename Public Outputs
in:
input_files:
source: [hard_filter_vcf/filtered_vcf, kfdrc_vcf2maf_public/output_maf]
valueFrom: |
$([self[0], self[0].secondaryFiles[0], self[1]])
rename_to:
source: [output_basename, tool_name]
valueFrom: |
${
var prefix = self[0] + '.' + self[1] + '.norm.annot.public';
return [
prefix + '.vcf.gz',
prefix + '.vcf.gz.tbi',
prefix + '.maf'
];
}
out: [renamed_files]
$namespaces:
sbg: https://sevenbridges.com
"sbg:license": Apache License 2.0
"sbg:publisher": KFDRC
"sbg:links":
- id: 'https://github.com/kids-first/kf-annotation-tools/releases/tag/v1.2.4'
label: github-release