Advancing Genome Processing Using Embedded Hardware in a Resource Constrained Environment

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 data. This pipeline is utilised 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, expensive, and internet dependent 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 thesis, for the first time, I 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 typically available in tiny processing systems. An optimised version of BWA-MEM targeting alignment using partitioned reference genome in distributed system architecture is also detailed in this thesis and is implemented on the proposed SWARAM system. Furthermore, I present a GPU based implementation using tiny processing systems with onboard GPU. The proposed GPU based system named SWARAM-GPU is low-cost, scalable, portable, offline and energy-efficient. The SWARAM-GPU system uses an advanced distributed system and data partitioning approach to exploit small memory sizes typically available in tiny processing systems with onboard GPU. 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 and SWARAM-GPU was competitive to that of a 32-core Intel Xeon server with similar accuracy, costing less than a tenth, and consuming less than 40% of the energy of the server system. The SWARAM-GPU system is faster than the SWARAM due to the advanced task distribution used for CPU-GPU codesign