Genetic disease is a major contributor to critical care hospitalization, especially in younger patients. While early genetic diagnosis can guide clinical management, the turnaround time for whole genome based diagnostic testing has traditionally been measured in months. Recent programs in neonatal populations have reduced turnaround time into the range of days and shown that rapid genetic diagnosis enhances patient care and reduces healthcare costs. Yet, most decisions in critical care need to be made on hourly timescales. 

We developed a whole genome sequencing approach designed to provide a genetic diagnosis within 8 hours. Optimized highly parallel nanopore sequencing was coupled to a high-performance cloud compute system to implement near real-time basecalling and alignment followed by accelerated central and graphics processor unit variant calling. A custom scheme for variant prioritization took only minutes to rank variants most likely to be deleterious allowing efficient manual review and classification according to American College of Medical Genetics and Genomics guidelines. We performed whole genome sequencing on 12 patients from the critical care units of Stanford hospitals. A pathogenic or likely pathogenic variant was identified in five out of 12 patients (42%). 

Pairwise Whole Genome Alignment (WGA) is a crucial first step to understanding evolution at the DNA sequence-level. Pairwise WGA of thousands of currently available species genomes could help make biological discoveries, however, computing them for even a fraction of the millions of possible pairs is prohibitive – WGA of a single pair of vertebrate genomes (human-mouse) takes 11 hours on a 96-core Amazon Web Services (AWS) instance (c5.24xlarge). SegAlign – a scalable, GPU-accelerated system for computing pairwise WGA. SegAlign is based on the standard seed-filter-extend heuristic, in which the filtering stage dominates the runtime (e.g. 98% for human-mouse WGA), and is accelerated using GPU(s). Using three vertebrate genome pairs, we show that SegAlign provides a speedup of up to 14x on an 8-GPU, 64-core AWS instance (p3.16xlarge) for WGA and nearly 2.3x reduction in dollar cost. SegAlign also allows parallelization over multiple GPU nodes and scales efficiently.

Whole genome alignment (WGA) is an indispensable tool in comparative genomics to study how different life forms have been shaped by evolution at the molecular level. Existing software whole genome aligners require several CPU weeks to compare a pair of mammalian genomes and still miss several biologically-meaningful, high-scoring alignment regions. These aligners are based on the seed-filter-and-extend paradigm with an ungapped filtering stage. Ungapped filtering is responsible for the low sensitivity of these aligners but is used because it is 200x faster than performing gapped alignment, using dynamic programming, in software. We show that replacing ungapped filtering with gapped filtering increases the number of matching base pairs in alignments by up to 3x. Our accelerator, Darwin-WGA, is the first hardware accelerator for whole genome alignment and accelerates the gapped filtering stage. Implemented on an FPGA, Darwin-WGA provides up to 24x improvement (performance/$) in WGA over iso-sensitive software. An ASIC implementation of the proposed architecture on TSMC 40nm technology achieves up to 10x performance/watt improvement on whole genome alignments over state-of-the-art software at higher sensitivity, and up to 1,500x performance/watt improvement compared to iso-sensitive software.