Learn about preprocessing VCF data before using it in an analysis.
It may be necessary to preprocess, or harmonize, the data before you load them.
- The raw data is expected to be a set of gVCF files -- one file per sample in the cohort.
dx run app-glnexus -i common.gvcf_manifest=<manifest_file_id> -i common.config=gatk_unfiltered -i common.targets_bed=<bed_target_ranges>
dx run workflow-glnexus -i common.gvcf_manifest=<manifest_file_id> -i common.config=gatk_unfiltered -i common.targets_bed=<bed_target_ranges> -i unify.shards_bed=<bed_genomic_partition_ranges> -i etl.shards=<num_sample_partitions>
The VCF data can include variant annotations. Of particular interest are SnpEff annotations, which are included in VCFs as
INFO/ANNtags--SnpEff annotations, if present, are loaded into databases. If desired, then you may pre-annotate your VCF data to include SnpEff annotations after harmonizing your data -- just pass your pVCF to any standard SnpEff annotator. If your pVCF is especially large, it may be advantageous to rely on the internal annotation step in the VCF loader instead of annotating the pVCF yourself. The VCF loader annotation step annotates the pVCF in a distributed, massively parallel way.
Note that the VCF loader does not persist the intermediate, annotated pVCF as a file, so if you want to have access to the annotated file up front, you should annotate it yourself.
VCF annotation flows. In (a) the annotation step is external to the VCF loader, whereas in (b) the annotation step is internal. In any case, SnpEff annotations present as
INFO/ANNtags are loaded into the database by the VCF loader.