Towards integrating the detection of genetic variants into an in-memory database
Next-generation sequencing enables whole genome sequencing within a few hours at a minimum of cost, entailing advanced medical applications such as personalized treatments. However, this recent technology imposes new challenges to alignment and variant calling as subsequent analysis steps. Compared to former sequencing, both must deal with an increasing amount of data to process at a significantly lower data quality — and are currently not capable of that. In this work, we focus on addressing these challenges for identifying Single Nucleotide Polymorphisms, i.e. SNP calling, in genome data as one subtask of variant calling. We propose the application of a column-store in-memory database for efficient data processing and apply the statistical model that is provided by the Genome Analysis Toolkit's UnifiedGenotyper. Comparisons with the UnifiedGenotyper show that our approach can exploit all computational resources available and accelerates SNP calling up to a factor of 22x.