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KF_SENTIEON_VQSR.md

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Kids First-Sentieon VQSR Equivalent Workflow

This workflow generally follows the guidelines outlined in the Variant Quality Score Recalibration (VQSR) and Which training sets arguments should I use for running VQSR?. Many find VQSR useful for evaluating the quality of calls made. We use Sentieon tools to more efficiently implement an equivalent of the VQSR portion of our Kids First DRC Joint Genotyping Workflow used in trio calling. It is to be run after the Kids First-Sentieon Joint Cohort Calling Workflow.

Inputs

Required:

  • reference: Indexed FASTA file reference. Should be the same one used to create the input gVCFs
  • input_vcfs: Array of by-chromosome joint called VCFs. Workflow will merge before applying VQSR
  • sentieon_license: Sentieon license server host and port in format 0.0.0.0:0000. Is set by default by the workflow, but can be changed if circumstances require it
  • dbsnp_vcf: Homo_sapiens_assembly38.dbsnp138.vcf # pulled by workflow by default
  • hapmap_resource_vcf: hapmap_3.3.hg38.vcf.gz
  • mills_resource_vcf: Mills_and_1000G_gold_standard.indels.hg38.vcf.gz
  • omni_resource_vcf: 1000G_omni2.5.hg38.vcf.gz
  • one_thousand_genomes_resource_vcf: 1000G_phase1.snps.high_confidence.hg38.vcf.gz
  • output_basename: String to prepend to output

Optional

  • bcftools_cpu: Default 8. Number of cores to be used ot merge VCFs
  • output_type Default z. Format of merged variants file
  • varcal_threads: Default 1. Sentieon documentation states for VarCal to be deterministic, it must be set 1, but will be much slower varcal_ram: Default 16. RAM in GB to providew to VarCal jobs. May need to increase depending on size of input
  • srand: Default 42. Determines the seed to use in the random number generation. You can set RANDOM_SEED to 0 and the software will use the random seed from your computer. In order to generate a deterministic result, you should use a non-zero RANDOM_SEED
  • snp_max_gaussians: Default 6. Integer value for max gaussians in SNP VariantRecalibration. If a dataset gives fewer variants than the expected scale, the number of Gaussians for training should be turned down. Lowering the max-Gaussians forces the program to group variants into a smaller number of clusters, which results in more variants per cluster
  • snp_tranche: Default [ 100.0, 99.95, 99.9, 99.8, 99.6, 99.5, 99.4, 99.3, 99.0, 98.0, 97.0, 90.0 ]. Normalized quality threshold for each tranche; the TRANCH_THRESHOLD number is a number between 0 and 100
  • snp_annotation: Default: [ 'QD', 'MQRankSum', 'ReadPosRankSum', 'FS', 'MQ', 'SOR', 'DP' ]. determine annotation that will be used during the indel recalibration
  • indel_max_gaussians: Default 4. Integer value for max gaussians in INDEL VariantRecalibration. If a dataset gives fewer variants than the expected scale, the number of Gaussians for training should be turned down. Lowering the max-Gaussians forces the program to group variants into a smaller number of clusters, which results in more variants per cluster.
  • indel_tranche: Default [ 100.0, 99.95, 99.9, 99.5, 99.0, 97.0, 96.0, 95.0, 94.0, 93.5, 93.0, 92.0, 91.0, 90.0 ]. Normalized quality threshold for each tranche; the TRANCH_THRESHOLD number is a number between 0 and 100
  • indel_annotation: Default: [ 'FS', 'ReadPosRankSum', 'MQRankSum', 'QD', 'SOR', 'DP' ]. determine annotation that will be used during indel recalibration

Outputs:

  • vqsr_vcf: Merged VQSR VCF with tranch filters

Run tips

  • The default 1TB storage per instance might be enough for up to a 1500 sample cohort size. To be safe, set this to at least 2TB if not more for larger cohorts in the task, documentation on this here: https://docs.sevenbridges.com/docs/set-execution-hints-at-task-level. An example would be to use the following:
    • Instance type: c5.2xlarge # Must meet requirements of min threads set for any tool
    • EBS storage: 2048 up to 4096
    • Number of parallel instances: 2. Most users have an 80 max limit per account