Darwin-WGA: A Co-processor Provides Increased Sensitivity in Whole Genome Alignments with High Speedup

Whole genome alignment (WGA) is an indispensable tool in comparative genomics to study how different life-Wforms 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. In this paper, we show that both performance and sensitivity can be greatly improved by using a hardware accelerator for WGA. Using the genomes of two roundworms (C. elegans and C. Briggsae) and four fruit flies (D. melanogaster, D. simulans, D. yakuba, and D. pseudoobscura), 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. Darwin-WGA also employs GACT-X, a novel algorithm used in the extension stage to align arbitrarily long genome sequences using a small on-chip memory, that provides better quality alignments at 2x improvement in memory and speed over the previously published GACT algorithm. 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 takes around 43W power with 36mm(2) area. It 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. Darwin-WGA is released under open-source MIT license and is available from https://github.com/gsneha26/Darwin-WGA.