Germline Copy Number Variant Calling for Whole-Genome-Sequencing with CNVscope

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CNVscope

Introduction

This document describes the capabilities of CNVscope for germline copy number variation (CNV) calling for whole-genome sequencing (WGS). If you have any additional questions, please contact the technical support at Sentieon® Inc. at support@sentieon.com.

Germline CNV calling with CNVscope

Basic usage of CNVscope

Two individual commands are run to call CNV and to apply the machine learning model. The input BAM file should come from a pipeline where alignment and deduplication have been performed.

sentieon driver [--interval INTERVAL_FILE] -t NUMBER_THREADS \
  -r REFERENCE -i DEDUPED_BAM --algo CNVscope \
  --model ML_MODEL/cnv.model TMP_VARIANT_VCF

sentieon driver -t NUMBER_THREADS -r REFERENCE --algo CNVModelApply \
  --model ML_MODEL/cnv.model -v TMP_VARIANT_VCF VARIANT_VCF

Reminder

It is important to use the same model for CNVscope and CNVModelApply. If different models are used, CNVModelApply will give an error.

The following inputs are required for the command:

NUMBER_THREADS: the number of computer threads that will be used in the calculation. We recommend that the number does not exceed the number of computing cores available in your system.
REFERENCE: the location of the reference FASTA file. You should make sure that the reference is the same as the one used in the mapping stage.
DEDUPED_BAM: the location of the input BAM file.
TMP_VARIANT_VCF: the location and filename of the variant calling output of CNVscope. This is a temporary file.
VARIANT_VCF: the location and filename of the variant calling output. A corresponding index file will be created. The tool will output a compressed file by using .gz extension.
ML_MODEL: the location of the machine learning model file. In the CNVscope command the model will be used to determine the settings used in variant calling.

The following inputs are optional for the command:

INTERVAL_BED: the location of a BED file containing intervals for variant calling.

The final output VCF file uses CN annotation for the copy-number state for each region called by CNVscope machine-learning model. Possible copy-number states called by CNVscope are from 0 to 4, with CN=4 representing copy number states equal to or larger than 4.

Using CNVscope with male samples

CNVscope will perform diploid variant calling across the whole genome or the genomic regions in the supplied --interval file. In human male samples, variant calling should be performed separately across diploid and haploid chromosomes. This can be accomplished by running CNVscope and CNVModelApply twice, once across the diploid autosomes and once across the haploid X and Y chromosomes using the --interval argument.

 sentieon driver --interval AUTOSOMES_BED -t NUMBER_THREADS \
   -r REFERENCE -i DEDUPED_BAM --algo CNVscope \
   --model ML_MODEL/cnv.model TMP_DIPLOID_VCF

 sentieon driver --interval  -t NUMBER_THREADS \
   -r REFERENCE --algo CNVModelApply --model ML_MODEL/cnv.model \
   -v TMP_DIPLOID_VCF DIPLOID_VCF

sentieon driver --interval HAPLOID_BED -t NUMBER_THREADS \
   -r REFERENCE -i DEDUPED_BAM --algo CNVscope \
   --model ML_MODEL/cnv.model TMP_HAPLOID_VCF

 sentieon driver --interval  -t NUMBER_THREADS \
   -r REFERENCE --algo CNVModelApply --model ML_MODEL/cnv.model \
   -v TMP_HAPLOID_VCF HAPLOID_VCF

The following inputs are required for the command:

AUTOSOMES_BED: the location of a BED file containing the diploid autosomes.
HAPLOID_BED: the location of a BED file containing the haploid chromosomes.

After generating VCFs containing haploid and diploid calls, the two VCF files can be combined using bcftools.

bcftools bcftools -aD DIPLOID_VCF HAPLOID_VCF | \
  sentieon util vcfconvert - VARIANT_VCF

Limitations of CNVscope machine learning model

Currently, CNVscope is trained on diploid WGS samples with CNV event size above 5000 base pairs and it should not be used to identify smaller CNVs. When performing diploid variant calling across haploid samples, intermediate copy-number states (CN=1 and CN=3) are undefined.