RAiSD-X

Abstract

Detecting traces of positive selection in genomes carries theoretical significance and has practical applications from shedding light on the forces that drive adaptive evolution to the design of more effective drug treatments. The size of genomic datasets currently grows at an unprecedented pace, fueled by continuous advances in DNA sequencing technologies, leading to ever-increasing compute and memory requirements for meaningful genomic analyses. The majority of existing methods for positive selection detection either are not designed to handle whole genomes or scale poorly with the sample size; they inevitably resort to a runtime versus accuracy tradeoff, raising an alarming concern for the feasibility of future large-scale scans. To this end, we present RAiSD-X, a high-performance system that relies on a decoupled access-execute processing paradigm for efficient FPGA acceleration and couples a novel, to our knowledge, sliding-window algorithm for the recently introduced μ statistic with a mutation-driven hashing technique to rapidly detect patterns in the data. RAiSD-X achieves up to three orders of magnitude faster processing than widely used software implementations, and more importantly, it can exhaustively scan thousands of human chromosomes in minutes, yielding a scalable full-system solution for future studies of positive selection in species of flora and fauna.