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Variant-Annotation.md

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Section 3: Variant-based Annotation

This section explains how to annotate a VCF/mVCF table against any number of variant-based annotation datasets.

mVCF/VCF

After successfully importing the mVCF/VCF files (refer to Section 1), they can be run against a number of annotation datasets by specifying the following key parameters (to list annotation datasets, refer to Section 2):

  • --bigQueryDatasetId: Specify the BigQuery dataset ID that will contain the output annotated VCF table.
  • --VCFTables: Specify the BigQuery address of the mVCF/VCF table on BigQuery
  • --VCFCanonicalizeRefNames: Specify the prefix for the reference field in the VCF tables (e.g, "chr"). AnnotationHive automatically canonicalizes the VCF table by removing the prefix in its calculation.
  • --projectId: Specify the project ID that has access to the VCF tables.
  • --bucketAddrAnnotatedVCF: Specify the full bucket and name address to the output VCF file (e.g., gs://mybucket/outputVCF.vcf).
  • --variantAnnotationTables: Specify the address of the Variant annotation tables (e.g., gbsc-gcp-project-cba:AnnotationHive.hg19_UCSC_snp144).
  • --createVCF: If you wish to obtain a VCF file, then set this flag true (default value is false, and it creates a table).

AnnotationHive VCF Table

If you imported the mVCF/VCF file using AnnotationHive's API, then modify and run the following command:

mvn compile exec:java -Dexec.mainClass=com.google.cloud.genomics.cba.StartAnnotationHiveEngine -Dexec.args="BigQueryAnnotateVariants --projectId=<YOUR_Google_Cloud_Project_ID> --runner=DataflowRunner --bigQueryDatasetId=<Your_BigQuery_DatasetId>  --outputBigQueryTable=<Output_VCF_Table_Name> --variantAnnotationTables=<ProjectID>:<DatasetID>.<AnnotationTableID>:<Field1>:<Field2>:...:<FieldN>  --VCFTables=<ProjectID>:<DatasetID>.<VCF/mVCF_Table_ID> --stagingLocation=gs://<Your_Google_Cloud_Bucket_Name>/<Staging_Address>/" -Pdataflow-runner

For our test example:

mvn compile exec:java -Dexec.mainClass=com.google.cloud.genomics.cba.StartAnnotationHiveEngine -Dexec.args="BigQueryAnnotateVariants --projectId=<YOUR_Project_ID> --runner=DataflowRunner --bigQueryDatasetId=test  --outputBigQueryTable=annotate_variant_test_chr17 --variantAnnotationTables=<YOUR_Project_ID>:test.sample_variant_annotation_chr17:alleleFreq:dbsnpid  --VCFTables=<YOUR_Project_ID>:test.NA12877_chr17 --stagingLocation=gs://<Your_Google_Cloud_Bucket_Name>/staging" -Pdataflow-runner

Google VCF Table

If the VCF table was imported using Google APIs, then set --googleVCF=true. Here is a test example for the 1000 Genomes Project mVCF file imported by Google Genomics.

mvn compile exec:java -Dexec.mainClass=com.google.cloud.genomics.cba.StartAnnotationHiveEngine -Dexec.args="BigQueryAnnotateVariants --projectId=<YOUR_Project_ID> --runner=DataflowRunner --bigQueryDatasetId=test  --outputBigQueryTable=annotate_variant_Google_1000_test_chr17 --variantAnnotationTables=<YOUR_Project_ID>:test.sample_variant_annotation_chr17:alleleFreq:dbsnpid  --VCFTables=genomics-public-data:1000_genomes_phase_3.variants --stagingLocation=gs://<Your_Google_Cloud_Bucket_Name>/staging --googleVCF=true" -Pdataflow-runner

Now, if you want AnnotationHive to calculate the number of samples that have variants, then set --numberSamples=true. Here is a test example for the 1000 Genomes Project mVCF file imported by Google Genomics (Total number of samples in the mVCF file: 2,504). AnnotationHive will find the number of samples that have variants:

mvn compile exec:java -Dexec.mainClass=com.google.cloud.genomics.cba.StartAnnotationHiveEngine -Dexec.args="BigQueryAnnotateVariants --projectId=YOUR_Project_ID --runner=DataflowRunner --bigQueryDatasetId=test  --outputBigQueryTable=annotate_variant_Google_1000_test_chr17_with_num_samples --variantAnnotationTables=<YOUR_Project_ID>:test.sample_variant_annotation_chr17:alleleFreq:dbsnpid  --VCFTables=genomics-public-data:1000_genomes_phase_3.variants --stagingLocation=gs://<Your_Google_Cloud_Bucket_Name>/staging --googleVCF=true --numberSamples=true" -Pdataflow-runner