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Test-drive the GATK tools and Best Practices pipelines on Terra
Check out this blog post to learn how you can get started with GATK and try out the pipelines in preconfigured workspaces (with a user-friendly interface!) without having to install anything.
We will be out of the office on November 11th and 13th 2019, due to the U.S. holiday(Veteran's day) and due to a team event(Nov 13th). We will return to monitoring the GATK forum on November 12th and 14th respectively. Thank you for your patience.
How to make results from GAT4.beta.3 "HaplotypeCaller" comparable to GATK2.7's "UnifiedGenotyper"?
We have been using “UnifiedGenotyper” of GATK2.7 for SNV calling, with "EMIT_ALL_SITES" mode, which always generate great results. We recently learnt GATK4 is in-development, with UnifiedGenotyper discontinued & HaplotypeCaller recommended. We thus test performance of HaplotypeCaller on our data with “-ERC GVCF” (didn't include the GenotypeGVCFs step). We found the amount of SNV identified decreased dramatically, with ~80%-90% reduction, in comparison with what's found by “UnifiedGenotyper”.
Our samples are from single cells, shallow sequenced. Paired-reads are 150bp each. Reads are supposed to align with short regions of 30-200bp across human genome, thus 99% of genome won’t be covered with reads. We’re not interested in arbitrary SNV, and don’t have target region or any window; we only care for mapping SNV across our samples. Based on quite a few experiments analyzed with UnifiedGenotyper, we found that even with low coverage, the short regions we aligned to always have highly reproducible base calls, and we could always identify SNV within these regions. We usually process five single cell samples each time, thus most region should have identical sequence and thus only 1 or 2 major alleles.
To our understanding, the “HaplotypeCaller” call variant based on de-novo assembly of active regions; if there are large amount of missing data in surrounding regions of our 30-200bp alignments, will it result in failure of haplotype identification, and lead to failure of SNV calling? Is the algorithm required certain sample size to work well? Is there any “HaplotypeCaller” parameters or discovery mode we could use to serve SNV calling with our current experiment design, or at least bring SNV calling rate to a level comparable to what identified by “UnifiedGenotyper”?
Greatly appreciate your advice!