SnpEff (v4.3) is run as an Azure Databricks job. Most likely, an Azure Databricks solutions architect will set up the initial job for you. The necessary details are:
- The cluster configuration should use Databricks Runtime HLS.
- The task should be the SnpEffAnnotationPipeline notebook that has been imported into your workspace via the link below.
The pipeline has been tested on 85.2 million variant sites from the 1000 Genomes project using the following cluster configurations:
- Driver: Standard_DS13_v2
- Workers: Standard_D32s_v3 * 7 (224 cores)
- Runtime: 2.5 hours
The pipeline accepts a number of parameters that control its behavior. The most important and commonly changed parameters are documented here; the rest can be found in the SnpEff Annotation notebook. All parameters can be set for all runs or per-run.
|manifest||n/a||Path of csv file describing the input, with file_path and sample_id headers|
|output||n/a||The path where pipeline output should be written.|
|exportVCF||false||If true, the pipeline writes results in VCF as well as Delta Lake.|
|exportVCFAsSingleFile||false||If true, exports VCF as single file|
In addition, you must configure the reference genome using environment variables. To use Grch37, set the environment variable:
To use Grch38 instead, set an environment variable like this:
The manifest is a CSV file describing where to find the input VCF files. An example:
file_path,sample_id dbfs:/mnt/vcf/HG001.vcf,HG001 dbfs:/mnt/vcf/HG002.vcf,HG002