Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees

Abstract

Motivation: Read alignment is a time-consuming step in genome sequence analysis. In the read alignment software BWA-MEM and the recently published faster version BWA-MEM2, the seeding step is a major bottleneck, for instance, contributing 38% to the overall execution time in BWA-MEM2 when aligning single-end whole human genome reads from the Platinum Genomes dataset. This is because both BWA-MEM and BWA-MEM2 use a compressed index structure called the FMD-Index, which results in high memory bandwidth requirements for seeding, primarily due to its character-by-character processing of reads. Results: We propose a memory bandwidth-aware data structure for maximal-exact-match seeding called Enumerated Radix Tree (ERT). ERT trades off memory capacity to improve seeding performance (∼60 GB index for human genome). Together with optimizations to the seeding algorithm and mate-rescue step, ERT when integrated into BWA-MEM2 speeds up overall read alignment by 1.28× and provides up to 2.1× higher seeding performance while guaranteeing identical output to the original software. Furthermore, we prototype an FPGA implementation of ERT on Amazon EC2 F1 cloud and observe 1.6× higher seeding throughput over a 48-thread optimized CPU-ERT implementation. Availability and implementation: https://github.com/arun-sub/bwa-mem2

SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping

“…Sequence-to-Sequence Accelerators. Even though there are several hardware accelerators designed to alleviate bottlenecks in several steps of traditional sequence-to-sequence (S2S) mapping (e.g., pre-alignment filtering [72,73,75,76,94,[140][141][142][143][144][145][146][147][148], sequenceto-sequence alignment [68-70, 129-132, 149-151]), none of these designs can be directly employed for the sequence-to-graph (S2G) mapping problem. This is because S2S mapping is a special case of S2G mapping, where all nodes have only one edge (Figure 3a).…”

“…Existing hardware accelerators for genome sequence analysis focus on accelerating only the traditional sequence-to-sequence mapping pipeline, and cannot support genome graphs as their inputs. For example, GenStore [142], ERT [144], GenCache [143], NEST [145], MEDAL [146], SaVI [147], SMEM++ [148], Shifted Hamming Distance [94], GateKeeper [72], MAGNET [140], Shouji [141], and SneakySnake [73,76] accelerate the seeding and/or filtering steps of sequence-to-sequence mapping.…”