Creating a phased VCF of proximal variants

By default, pVACseq will evaluate all somatic variants in the input VCF in isolation. As a result, if a somatic variant of interest has other somatic or germline variants in proximity, the calculated wildtype and mutant protein sequences might be incorrect because the amino acid changes of those proximal variants were not taken into account.

To solve this problem, we added a new option to pVACseq in the pvactools release 1.1. This option, --phased-proximal-variants-vcf, can be used to provide the path to a phased VCF of proximal variants in addition to the normal input VCF. This VCF is then used to incorporate amino acid changes of nearby variants that are in-phase to a somatic variant of interest. This results in corrected mutant and wildtype protein sequences that account for proximal variants.

At this time, this option only handles missense proximal variants but we are working on a more comprehensive approach to this problem.

Note that if you do not perform the proximal variants step, you should manually review the sequence data for all candidates (e.g. in IGV) for proximal variants and either account for these manually, or eliminate these candidates. Failure to do so may lead to inclusion of incorrect peptide sequences.

How to create the phased VCF of proximal variants

Input files

• tumor.bam: A BAM file of tumor reads

• somatic.vcf: A VCF of somatic variants

• germline.vcf: A VCF of germline variants

• reference.fa: The reference FASTA file

Required tools

• Picard

• GATK

• bgzip

• tabix

Create the reference dictionary

java -jar picard.jar CreateSequenceDictionary \
R=reference.fa \
O=reference.dict

Update sample names

The sample names in the tumor.bam, the somatic.vcf, and the germline.vcf need to match. If they don’t you need to edit the sample names in the VCF files to match the tumor BAM file.

Combine somatic and germline variants using GATK’s CombineVariants

/usr/bin/java -Xmx16g -jar /opt/GenomeAnalysisTK.jar \
-T CombineVariants \
-R reference.fa \
--variant germline.vcf \
--variant somatic.vcf \
-o combined_somatic_plus_germline.vcf \
--assumeIdenticalSamples

Sort combined VCF using Picard

/usr/bin/java -Xmx16g -jar /opt/picard/picard.jar SortVcf \
I=combined_somatic_plus_germline.vcf \
O=combined_somatic_plus_germline.sorted.vcf \
SEQUENCE_DICTIONARY=reference.dict

Phase variants using GATK’s ReadBackedPhasing

/usr/bin/java -Xmx16g -jar /opt/GenomeAnalysisTK.jar \
-T ReadBackedPhasing \
-R reference.fa \
-I tumor.bam \
--variant combined_somatic_plus_germline.sorted.vcf \
-L combined_somatic_plus_germline.sorted.vcf \
-o phased.vcf

Annotate VCF with VEP

./vep \
--input_file <phased.vcf> --output_file <phased.annotated.vcf> \
--format vcf --vcf --symbol --terms SO --tsl\
--hgvs --fasta <reference build FASTA file location> \
--offline --cache [--dir_cache <VEP cache directory>] \
--plugin Downstream --plugin Wildtype \
[--dir_plugins <VEP_plugins directory>] [--pick] [--transcript_version]

bgzip and index the phased VCF

bgzip -c phased.annotated.vcf > phased.annotated.vcf.gz
tabix -p vcf phased.annotated.vcf.gz

The resulting phased.vcf.gz file can be used as the input to the --phased-proximal-variants-vcf option.

bgzip and index the pVACseq main input VCF

In order to use the --phased-proximal-variants-vcf option you will also need to bgzip and index the VCF you plan on using as the main input VCF to pVACseq. This step would be done after all the required and optional preprocessing steps (e.g. VEP annotation, adding readcount and expression data).

bgzip -c input.vcf > input.vcf.gz
tabix -p vcf input.vcf.gz