Treatment of patients using high-quality precision medicine requires a thorough understanding of the genetic composition of a patient. Ideally, the identification of unique variations in an individual’s genome is needed for specifying the necessary treatment. Variant calling workflow is a pipeline of tools, integrating state of the art software systems aimed at alignment, sorting and variant calling for the whole genome sequencing (WGS) data. This pipeline is utilized for identifying unique variations in an individual’s genome (compared to a reference genome). Currently, such a workflow is implemented on high-performance computers (with additional GPUs or FPGAs) or in cloud computers. Such systems are large, have a high cost, and rely on the internet for genome data transfer which makes the system unusable in remote locations unequipped with internet connectivity. It further raises privacy concerns due to processing being carried out in a different facility.

To overcome such limitations, in this paper, for the first time, we present a cost-efficient, offline, scalable, portable, and energy-efficient computing system named SWARAM for variant calling workflow processing. The system uses novel architecture and algorithms to match against partial reference genomes to exploit smaller memory sizes which are typically available in tiny processing systems. Extensive tests on a standard benchmark data-set (NA12878 Illumina platinum genome) confirm that the time consumed for the data transfer and completing variant calling workflow on SWARAM was competitive to that of a 32-core Intel Xeon server with similar accuracy, but costs less than a fifth, and consumes less than 40% of the energy of the server system. The original scripts and code we developed for executing the variant calling workflow on SWARAM are available in the associated Github repository https://github.com/Rammohanty/swaram.