Description

Build a recalibration model to score variant quality for filtering purposes. It is highly recommended to follow GATK best practices when using this module, the gaussian mixture model requires a large number of samples to be used for the tool to produce optimal results. For example, 30 samples for exome data. For more details see https://gatk.broadinstitute.org/hc/en-us/articles/4402736812443-Which-training-sets-arguments-should-I-use-for-running-VQSR-

Input

Name
Description
Pattern

0 ()

1 ()

2 ()

resource_vcf (file)

all resource vcf files that are used with the corresponding '--resource' label

*.vcf.gz

resource_tbi (file)

all resource tbi files that are used with the corresponding '--resource' label

*.vcf.gz.tbi

labels (string)

necessary arguments for GATK VariantRecalibrator. Specified to directly match the resources provided. More information can be found at https://gatk.broadinstitute.org/hc/en-us/articles/5358906115227-VariantRecalibrator

fasta (file)

The reference fasta file

*.fasta

fai (file)

Index of reference fasta file

fasta.fai

dict (file)

GATK sequence dictionary

*.dict

Output

Name
Description
Pattern

0 ()

0 ()

0 ()

0 ()

0 ()

Tools

gatk4 Documentation

Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size.

doi: 10.1158/1538-7445.AM2017-3590
Authors