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

Abstract

A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e., sequence-tosequence mapping). Recent works replace the linear reference sequence with a graph-based representation of the reference genome, which captures the genetic variations and diversity across many individuals in a population. Mapping reads to the graph-based reference genome (i.e., sequence-to-graph mapping) results in notable quality improvements in genome analysis. Unfortunately, while sequence-to-sequence mapping is well studied with many available tools and accelerators, sequence-to-graph mapping is a more difcult computational problem, with a much smaller number of practical software tools currently available. We analyze two state-of-the-art sequence-to-graph mapping tools and reveal four key issues. We fnd that there is a pressing need to have a specialized, high-performance, scalable, and low-cost algorithm/hardware co-design that alleviates bottlenecks in both the seeding and alignment steps of sequence-to-graph mapping. Since sequence-to-sequence mapping can be treated as a special case of sequence-to-graph mapping, we aim to design an accelerator that is efcient for both linear and graph-based read mapping. To this end, we propose SeGraM, a universal algorithm/hardware co-designed genomic mapping accelerator that can effectively and efciently support both sequence-to-graph mapping and sequenceto-sequence mapping, for both short and long reads. To our knowledge, SeGraM is the frst algorithm/hardware co-design for accelerating sequence-to-graph mapping. SeGraM consists of two main components: (1) MinSeed, the frst minimizer-based seeding accelerator, which fnds the candidate locations in a given genome graph; and (2) BitAlign, the frst bitvector-based sequence-to-graph alignment accelerator, which performs alignment between a given read and the subgraph identifed by MinSeed. We couple SeGraM with high-bandwidth memory to exploit low latency and highlyparallel memory access, which alleviates the memory bottleneck.