If you happen to see a question you know the answer to, please do chime in and help your fellow community members. We appreciate your help!
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.
Runtime Performance of UnifiedGenotyper for Increasing Data Size
I ran GATK's UnifiedGenotyper of version 2.7-4 (SNP calling) with multiple data sets that where growing in size (10 sets ranging from 8.5 GB to 88GB) with 36 cpu threads and 8 GB heap assigned. I noticed that the UnifiedGenotyper performed much better than linear with a runtime increase of 4.6 times for the largest data set, although this was 10 times larger than the smallest.
Now I wonder how this is achieved by GATK? What part of the processing makes it scale so well to be even better than linear here? Is it the SNP calling that is so performant, or how the data is preprocessed/filtered?