# Introduction¶

Avocado is a variant caller built on top of Apache Spark to allow rapid variant calling on cluster/cloud computing environments. Avocado is built on ADAM’s APIs, and achieves variant calling accuracy that is similar to state-of-the-art tools while being able to drop variant calling latency to approximately 15 minutes when running on a 1,024 core cluster.

# Workflows Supported¶

Avocado is run through the avocado-submit command line:

./bin/avocado-submit

Using SPARK_SUBMIT=/usr/local/bin/spark-2.2.1-bin-hadoop2.7/bin/spark-submit

Usage: avocado-submit [<spark-args> --] <avocado-args> [-version]

Choose one of the following commands:

biallelicGenotyper : Call variants under a biallelic model
discover : Discover variants in reads
jointer : Joint call and annotate variants.
mergeDiscovered : Merge variants discovered from reads of multiple samples
reassemble : Reassemble reads to canonicalize variants
trioGenotyper : Call variants in a trio under a biallelic model


The avocado-submit script follows the same conventions as the adam-submit command line, whose documentation can be found here. As a result, just like ADAM, Avocado can be deployed on a local machine, on AWS, an in-house cluster running YARN or SLURM, or using Toil.

Avocado supports several workflows:

• Single sample germline variant calling: Avocado’s BiallelicGenotyper runs on a single sample at a time, and can generate both variants-only (VCF) and all-sites (gVCF) output.
• Joint variant calling: Avocado supports jointly calling variants from a collection of gVCF-styled inputs.

Avocado also contains code to reassemble variants, and a pedigree variant caller. However, this code is experimental and is thus unsupported.

For genotyping, Avocado uses a probabilistic model that assumes that sites are biallelic. This model is derived from the biallelic model used by the Mpileup variant caller, but modified to better call multiallelic sites. When used with the INDEL realigner from ADAM, Avocado has >99% accuracy when genotyping SNPs, and >96% accuracy when genotyping INDELs.

Installation