Split Multiallelic Variants¶
Splitting multiallelic variants to biallelic variants is a transformation sometimes required before further downstream analysis. Glow provides the
split_multiallelics transformer to be applied on a variant DataFrame to split multiallelic variants in the DataFrame to biallelic variants. This transformer is able to handle any number of
ALT alleles and any ploidy.
The splitting logic used by the
split_multiallelics transformer is the same as the one used by the vt decompose tool of the vt package with option
-s (note that the example provided at vt decompose user manual page does not reflect the behavior of
vt decompose -s completely correctly).
The precise behavior of the
split_multiallelics transformer is presented below:
A given multiallelic row with \(n\)
ALTalleles is split to \(n\) biallelic rows, each with one of the
ALTalleles of the original multiallelic row. The
REFallele in all split rows is the same as the
REFallele in the multiallelic row.
INFOfield is appropriately split among split rows if it has the same number of elements as number of
ALTalleles, otherwise it is repeated in all split rows. The boolean
splitFromMultiAllelicis added/modified to reflect whether the new row is the result of splitting a multiallelic row through this transformation or not. A new
OLD_MULTIALLELICis added to the DataFrame, which for each split row, holds the
CHROM:POS:REF/ALTof its original multiallelic row. Note that the
INFOfield must be flattened (as explained here) in order to be split by this transformer. Unflattened
INFOfields (such as those inside an
attributesfield) will not be split, but just repeated in whole across all split rows.
Genotype fields for each sample are treated as follows: The
GTfield becomes biallelic in each row, where the original
ALTalleles that are not present in that row are replaced with no call. The fields with number of entries equal to number of
ALTalleles, are properly split into rows, where in each split row, only entries corresponding to the
REFallele as well as the
ALTallele present in that row are kept. The fields which follow colex order (e.g.,
GP) are properly split between split rows where in each row only the elements corresponding to genotypes comprising of the
ALTalleles in that row are listed. Other genotype fields are just repeated over the split rows.
Any other field in the DataFrame is just repeated across the split rows.
As an example (shown in VCF file format), the following multiallelic row
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE1 20 101 . A ACCA,TCGG . PASS VC=INDEL;AC=3,2;AF=0.375,0.25;AN=8 GT:AD:DP:GQ:PL 0/1:2,15,31:30:99:2407,0,533,697,822,574
will be split into the following two biallelic rows:
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE1 20 101 . A ACCA . PASS VC=INDEL;AC=3;AF=0.375;AN=8;OLD_MULTIALLELIC=20:101:A/ACCA/TCGG GT:AD:DP:GQ:PL 0/1:2,15:30:99:2407,0,533 20 101 . A TCGG . PASS VC=INDEL;AC=2;AF=0.25;AN=8;OLD_MULTIALLELIC=20:101:A/ACCA/TCGG GT:AD:DP:GQ:PL 0/.:2,31:30:99:2407,697,574
df_original is a variable of type DataFrame which contains the genomic variant records, an example of using this transformer for splitting multiallelic variants is:
df_split = glow.transform("split_multiallelics", df_original)
df_split = Glow.transform("split_multiallelics", df_original)
split_multiallelics transformer is often significantly faster if the whole-stage code generation feature of Spark Sql is turned off. Therefore, it is recommended that you temporarily turn off this feature using the following command before using this transformer.
Remember to turn this feature back on after your split DataFrame is materialized.