VAFator 1.1.3

Annotate a VCF file with AF, AD and DP from tumor and normal BAMs

VAFator

VAFator annotates the variants in a VCF file with technical annotations from multiple BAM files. Supports annotating somatic variant calls with the annotations from the normal and the tumor samples; although it can also be used for germline variant calls.

Annotations:

• Allele frequency (AF): ratio of reads supporting the alternate allele.
• Allele count (AC): count of reads supporting the alternate allele.
• Depth of coverage (DP): number of reads covering the position of the variant

How to install

Install from PyPI (pip install vafator) or from bioconda (conda install bioconda::vafator).

When installaing from PyPI there are some system dependencies that will need to be met:

• libcurl
• libz
• liblzma
• htslib=1.14

How to run

Run as follows:

vafator --input-vcf /path/yo/your.vcf \
--output-vcf /path/yo/your_vafator.vcf \ 
--bam normal /path/to/your_normal.bam \
--bam primary /path/to/your_primary_tumor.bam \
--bam metastasis /path/to/your_metastasis_tumor.bam

This will add annotations for each of the three samples normal, primary and metastasis: normal_ac, normal_dp, normal_af, primary_ac, primary_dp, primary_af, metastasis_ac, metastasis_dp and metastasis_af.

If more than one BAM for the same sample is provided then the annotations are calculated across all BAMs and for also each of them separately (eg: primary_af provides the allele frequency across all primary tumor BAMs, primary_af_1 and primary_af_2 provide the allele frequency on the first and second BAM respectively).

vafator --input-vcf /path/yo/your.vcf \
--output-vcf /path/yo/your_vafator.vcf \ 
--bam primary /path/to/your_primary_tumor_1.bam \
--bam primary /path/to/your_primary_tumor_2.bam

Alternatively, you can use --normal-bams and/or --tumor-bams and the sample names will be predefined to normal and tumorrespectively.

vafator --input-vcf /path/yo/your.vcf \
--output-vcf /path/yo/your_vafator.vcf \ 
--normal-bams /path/to/your_normal.bam \
--tumor-bams /path/to/your_tumor_1.bam,/path/to/your_tumor_2.bam

Use the parameters --mapping-quality and --base-call-quality to define the minimum quality values for each read. All reads with quality values velow these thresholds will be filtered out.

Overlapping reads from read pairs are not double counted. The read with the highest base call quality is chosen.

Understanding the output

The output is a VCF with the some new annotations in the INFO field for the provided sample names. The example below contains vafator annotations for two samples named normal and tumor.

chr1    12345       .       A       G       .       PASS  tumor_af=0.0;tumor_ac=0;tumor_dp=89;normal_af=0.0196;normal_ac=1;normal_dp=51
chr2    12345       .       A       G,T       .       PASS  tumor_af=0.2,0.2;tumor_ac=2,2;tumor_dp=10;normal_af=0.0,0.0;normal_ac=0,0;normal_dp=10

NOTE: notice that VAFator does not annotate samples in the FORMAT field, but in the INFO field

Filter for multi-allelic variants

Multi-allelic variants are those that have more than one alternative allele (e.g.: A>C,G). This tool allows to select the allele with the highest allele frequency and filter out the lower frequency allele.

Run as follows:

multiallelics-filter --input-vcf /path/to/your_vafator.vcf \
--output-vcf /path/to/your_vafator_filtered.vcf \
--tumor-sample-name <SAMPLE> 

The above will look for the annotation <SAMPLE>_af and for multi-allelic variants it will filter out those with lower frequencies. Beware, that if the multi-allelic variants are split into more than one line in the VCF nothing will be filtered out.

Run as a Nextflow pipeline

VAFator is available as a Nextflow pipeline for convenience.

Run as follows:

nextflow run tron-bioinformatics/vafator -r 1.1.0 -profile conda --input_files /path/to/your.tsv

where --input_files expects three tab-separated columns without a header:

Patient name	VCF	Sample name:BAM
patient_1	/path/to/sample_1.vcf	my_tumor:/path/to/sample_1_tumor_1.bam
patient_1	/path/to/sample_1.vcf	my_tumor:/path/to/sample_1_tumor_2.bam
patient_1	/path/to/sample_1.vcf	my_metastasis:/path/to/sample_1_metastasis_1.bam
patient_1	/path/to/sample_1.vcf	my_normal:/path/to/sample_1_normal.bam
patient_2	/path/to/sample_2.vcf	my_tumor:/path/to/sample_2_tumor.bam
patient_2	/path/to/sample_2.vcf	my_normal:/path/to/sample_1_normal.bam

The above corresponds to two patients, patient_1 and patient_2. Where for patient_1 we have four samples of which two replicates for a sample named my_tumor and two additional samples named my_metastasis and my_normal.

Optional parameters:

• --output: the folder where to publish output
• --skip_multiallelic_filter: skip the filtering of multiallelics by frequency in the tumor (only highest frequency variant at the same position is kept)
• --base_call_quality: minimum base call quality, reads with values below will be filtered out (default: 30)
• --mapping_quality: minimum mapping quality, reads with values below will be filtered out (default: 1)

Support for indels

VAFator provides equivalent annotations for indels. Depth of coverage and allele frequency are calculated on the position immediately before the indel. Only insertions and deletions as recorded in the CIGAR matching the respective coordinates and sequence from the VCF file are taken into account. Any read supporting a similar but not equal indel will not be counted. Also, multiallelic mutations are not supported for indels.

Support for MNVs

Not supported