Skip to content

The HPC is changing

We will soon be switching to a new High Performance Cluster, called Double Helix. This will mean that some of the commands you use to connect to the HPC and call modules will change. We will inform you by email when you are switching over, allowing you to make the necessary changes to your scripts. Please check our HPC changeover notes for more details on what will change.

Querying aggregate VCF files to find participants by genotypes

Give us feedback on this tutorial

The aggregate VCFs, AggV2 for germline variants and somAgg for somatic variants, are aggregates of gVCF files. You can query these aggregates for genomic loci.

Identifying the correct chunk

As these aggregrates are split into 1371 chunks, all queries for loci must begin by identifying the correct chunk in AggV2 or somAgg.

There are BED files listing all the chunks, allowing you to use bedtools to intersect against your own BED files. Create a BED file listing your genomic loci of interest, for example:

chr2 213006985 213006985 variant

You can then intersect against the AggV2 or somAgg chunk bed files:

AggV2
module load bio/BEDTools/2.27.1-foss-2018b

bedtools intersect -wo -a my_regions.bed -b /gel_data_resources/main_programme/aggregation/aggregate_gVCF_strelka/aggV2/additional_data/chunk_names/aggV2_chunk_names.bed | cut -f 1-4,10-11
somAgg
module load bio/BEDTools/2.27.1-foss-2018b

bedtools intersect -wo -a my_regions.bed -b /gel_data_resources/main_programme/aggregation/aggregated_somatic_strelka/somAgg/v0.2/additional_data/chunk_names/somAgg_chunk_names.bed | cut -f 1-4,10-11

This will give you a tsv file listing the correct chunk to use, for example:

AggV2
chr2 213006985 213006985 variant /gel_data_resources/main_programme/aggregation/aggregate_gVCF_strelka/aggV2/genomic_data/gel_mainProgramme_aggV2_chr2_211052166_213676386.vcf.gz /gel_data_resources/main_programme/aggregation/aggregate_gVCF_strelka/aggV2/functional_annotation/VEP/gel_mainProgramme_aggV2_chr2_211052166_213676386_VEPannot.vcf.gz
SomAgg
chr2 213006985 213006985 variant /gel_data_resources/main_programme/aggregation/aggregated_somatic_strelka/somAgg/v0.2/genomic_data/somAgg_dr12_chr2_211052166_213676386.vcf.gz

Query the VCF

You can then use bcftools to query for your loci in AggV2 or somAgg.

AggV2
module load bio/BCFtools/1.10.2-GCC-8.3.0

bcftools query -r chr2:213006985 \
-f '[%SAMPLE\t%CHROM\t%POS\t%REF\t%ALT\t%INFO/OLD_MULTIALLELIC\t%INFO/OLD_CLUMPED\t%FILTER\t%GT\n]' \
/gel_data_resources/main_programme/aggregation/aggregate_gVCF_strelka/aggV2/genomic_data/gel_mainProgramme_aggV2_chr2_211052166_213676386.vcf.gz > variant_genotypes.tsv
somAgg
module load bio/BCFtools/1.10.2-GCC-8.3.0

bcftools query -r chr2:213006985 \
-f '[%SAMPLE\t%CHROM\t%POS\t%REF\t%ALT\t%INFO/OLD_MULTIALLELIC\t%INFO/OLD_CLUMPED\t%FILTER\t%GT\n]' \
/gel_data_resources/main_programme/aggregation/aggregated_somatic_strelka/somAgg/v0.2/
/genomic_data/somAgg_dr12_chr2_211052166_213676386.vcf.gz > somatic_variant_genotypes.tsv

This will give an output like:

LP1234567-DNA_A01       chr2    213006985        G       A       .       .       PASS    0/0
LP2345678-DNA_B02       chr2    213006985        G       A       .       .       PASS    0/1
LP3456789-DNA_C03       chr2    213006985        G       A       .       .       PASS    1/1
LP4567890-DNA_D04       chr2    213006985        G       A       .       .       PASS    0/0
LP5678901-DNA_E05       chr2    213006985        G       A       .       .       PASS    0/1
LP6789012-DNA_F06       chr2    213006985        G       A       .       .       PASS    0/0

AggV2 was created in release 8 and completed in release 10, whereas somAgg was created in release 12. This means that neither contain participants who were added to the dataset after those releases and both include some participants who have since withdrawn consent. This means that you should always cross reference any results against the current release to ensure there are no participants in your results who are no longer consented.

Both aggregates only contain genomes that were aligned to GRCh38.