These run Giraffe, VG-MAP, and BWA-MEM and create an allele balance plot to assess mapping reference allele bias.
To run them, you will need a machine with the aws command configured.
You will also need kubectl set up to access a Kubernetes cluster which has a service account named vg-svc and a secret named shared-s3-credentials with the contents of a ~/.aws directory that grants single-factor access to AWS. You may need to adjust the scripts to reference a different service account or secret name if you cannot provide resources with these names. The Kubernetes cluster will need to be able to fulfil requests for up to 24 cores, 250 Gi of memory, and 300 Gi of ephemeral storage.
These scripts expect to write to the s3://vg-k8s bucket. You will need to have write access to this bucket, or you will need to replace these writes with writes to a bucket where you intend to store your generated artifacts.
The Kubernetes script can be run:
./kubernetes_pileup_call
This script will produce a vcf file all_calls.vcf.gz that is copied to an s3 bucket.
The plotting script can then be run on the unzipped vcf file:
To produce the allele balance plot in plot.svg