Bone_Cancer_NOS.toml

# Basic PCGR configuration options (TOML).


[tumor_only]
## If input VCF contains mix of germline/somatic (variants called with no matching control, i.e. tumor-only) set vcf_tumor_only to true
vcf_tumor_only = false

## If vcf_tumor_only = true, several filters can be configured, all as a means to minimize the proportion of germline calls in the raw set derived from tumor-only calling

## Exclude variants (SNVs/InDels) with minor allele frequency above the following population-specific thresholds
## 1000 Genomes Project - WGS data
maf_onekg_eur = 0.002
maf_onekg_amr = 0.002
maf_onekg_afr = 0.002
maf_onekg_sas = 0.002
maf_onekg_eas = 0.002
maf_onekg_global = 0.002

## exclude variants with minor allele frequency above the following population-specific thresholds
## gnomAD - WES data
maf_gnomad_nfe = 0.002
maf_gnomad_amr = 0.002
maf_gnomad_afr = 0.002
maf_gnomad_asj = 0.002
maf_gnomad_sas = 0.002
maf_gnomad_eas = 0.002
maf_gnomad_fin = 0.002
maf_gnomad_oth = 0.002
maf_gnomad_global = 0.002

## Exclude variants occurring in PoN (panel of normals, if provided as VCF)
exclude_pon = true

## Exclude likely homozygous germline variants (100% allelic fraction for alternate allele in tumor, very unlikely somatic event)
exclude_likely_hom_germline = false

## Exclude likely heterozygous germline variants
## Must satisfy i) 40-60 % allelic fraction for alternate allele in tumor sample, ii) present in dbSNP + gnomAD, ii) not existing as somatic event in COSMIC/TCGA
## Note that the application of this filter may be suboptimal for very impure tumors or variants affected by CNAs etc (under these circumstances, the allelic fraction
## will be skewed (see e.g. discussion in PMID:29249243)
exclude_likely_het_germline = false

## Exclude variants found in dbSNP (only those that are NOT found in ClinVar(somatic origin)/DoCM/TCGA/COSMIC)
exclude_dbsnp_nonsomatic = false

## exclude all non-exonic variants
exclude_nonexonic = true

[allelic_support]
## Specify INFO tags in input VCF that denotes depth/allelic fraction in tumor and normal sample
## An additional tag that denotes call confidence (call_conf_tag) can also be specified, which will
## be used for exploration in the global variant browser. Note that 'tumor_dp_tag' must be of
## Type=Integer, and 'tumor_af_tag' must be of Type=Float (similarly for normal sample)
tumor_dp_tag = ""
tumor_af_tag = ""
control_dp_tag = ""
control_af_tag = ""
call_conf_tag = ""

## set thresholds for tumor depth/allelic fraction, will be applied before report generation
## will only apply if 'tumor_dp_tag' and 'tumor_af_tag' are specified above (similarly
## for 'control_dp_tag' and 'control_af_tag'
tumor_dp_min = 0
tumor_af_min = 0.0
control_dp_min = 0
control_af_max = 1.0

[mutational_burden]
## Calculate mutational burden (similar to Chalmers et al., Genome Med, 2017)
mutational_burden = true
## Size of coding target region in megabases (defaults to size of protein-coding regions of GENCODE ~ 34 Mb)
## Note: this should ideally denote the callable target size (i.e. reflecting variable sequencing depth)
target_size_mb = 34.0
## set upper limits to tumor mutational burden tertiles (mutations/Mb)
tmb_low_limit = 5
tmb_intermediate_limit = 20
## tmb_high = tmb > tmb_intermediate_limit

[cna]
## log ratio thresholds for determination of copy number gains and homozygous deletions
logR_gain = 0.8
logR_homdel = -0.8

## mean percent overlap between copy number segment and gene transcripts for reporting of gains/losses in tumor suppressor genes/oncogenes
cna_overlap_pct = 50

[msi]
## Predict microsatellite instability
msi = true

[mutational_signatures]
## Identify relative contribution of 30 known mutational signatures (COSMIC) through the deconstructSigs framework
mutsignatures = true
## deconstructSigs option: number of mutational signatures to limit the search to ('signatures.limit' in whichSignatures)
mutsignatures_signature_limit = 6
## deconstructSigs option: type of trimer count normalization for inference of known mutational signatures, see explanation at https://github.com/raerose01/deconstructSigs"
## options = 'default', 'exome', 'genome', 'exome2genome'
## NOTE: If your data (VCF) is from exome sequencing, 'default' or 'exome2genome' should be used. See https://github.com/raerose01/deconstructSigs/issues/2
mutsignatures_normalization = "exome2genome"
## Require a minimum number of mutations for signature estimation
mutsignatures_mutation_limit = 100
## deconstructSigs option: discard any signature contributions with a weight less than this amount
mutsignatures_cutoff = 0.06

[visual]
## Choose visual theme of report, any of: "default", "cerulean", "journal", "flatly", "readable", "spacelab", "united", "cosmo", "lumen", "paper", "sandstone", "simplex", or "yeti" (https://bootswatch.com/)
report_theme = "default"

[custom_tags]
## list VCF info tags that should be present in JSON and TSV output
## tags should be comma separated, i.e. custom_tags = "MUTECT2_FILTER,STRELKA_FILTER"
custom_tags = ""

[other]
## list/do not list noncoding variants
list_noncoding = true
## VEP/vcfanno processing options
n_vcfanno_proc = 4
n_vep_forks = 4
## Customise the order of criteria used to pick the primary transcript in VEP (see https://www.ensembl.org/info/docs/tools/vep/script/vep_options.html#opt_pick_order)
vep_pick_order = "canonical,appris,biotype,ccds,rank,tsl,length"
## omit intergenic variants during VEP processing
vep_skip_intergenic = false
## generate a MAF for input VCF using https://github.com/mskcc/vcf2maf
vcf2maf = true

## Not for edit
[tumor_type]
type = "Bone_Cancer_NOS"