In general most GATK tools don't care about ploidy. The major exception is, of course, at the variant calling step: the variant callers need to know what ploidy is assumed for a given sample in order to perform the appropriate calculations.
As of version 3.3, the HaplotypeCaller and GenotypeGVCFs are able to deal with non-diploid organisms (whether haploid or exotically polyploid). In the case of HaplotypeCaller, you need to specify the ploidy of your non-diploid sample with the
-ploidy argument. HC can only deal with one ploidy at a time, so if you want to process different chromosomes with different ploidies (e.g. to call X and Y in males) you need to run them separately. On the bright side, you can combine the resulting files afterward. In particular, if you’re running the -ERC GVCF workflow, you’ll find that both CombineGVCFs and GenotypeGVCFs are able to handle mixed ploidies (between locations and between samples). Both tools are able to correctly work out the ploidy of any given sample at a given site based on the composition of the GT field, so they don’t require you to specify the -ploidy argument.
For earlier versions (all the way to 2.0) the fallback option is UnifiedGenotyper, which also accepts the
For normal organism ploidy, you just set the
-ploidy argument to the desired number of chromosomes per organism. In the case of pooled sequencing experiments, this argument should be set to the number of chromosomes per barcoded sample, i.e.
(Ploidy per individual) * (Individuals in pool).
Several variant annotations are not appropriate for use with non-diploid cases. In particular, InbreedingCoeff will not be annotated on non-diploid calls. Annotations that do work and are supported in non-diploid use cases are the following:
AF, and Genotype annotations such as
You should also be aware of the fundamental accuracy limitations of high ploidy calling. Calling low-frequency variants in a pool or in an organism with high ploidy is hard because these rare variants become almost indistinguishable from sequencing errors.
As I know GATK is worked fine for Genome SNP Calling. Can I know it work well for Transcriptome SNP calling as well? I can't find much info regarding Transcriptome SNP calling.
If yes, can I know the step for Transcriptome SNP calling by GATK is same as what we did for Genome SNP calling? eg. alignment raw read to reference transcriptome, marking/remove PCR duplicates, local realignment around indel and quality score re-calibration (if know dbSNP is available).
Thanks and looking forward to hear from you.