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A critical step of genome sequence analysis is the mapping of sequenced DNAfragments (i.e., reads) collected from an individual to a known linearreference genome sequence (i.e., sequence-to-sequence mapping). Recent worksreplace the linear reference sequence with a graph-based representation of thereference genome, which captures the genetic variations and diversity acrossmany individuals in a population. Mapping reads to the graph-based referencegenome (i.e., sequence-to-graph mapping) results in notable qualityimprovements in genome analysis. Unfortunately, while sequence-to-sequencemapping is well studied with many available tools and accelerators,sequence-to-graph mapping is a more difficult computational problem, with amuch smaller number of practical software tools currently available.  We analyze two state-of-the-art sequence-to-graph mapping tools and revealfour key issues. We find that there is a pressing need to have a specialized,high-performance, scalable, and low-cost algorithm/hardware co-design thatalleviates bottlenecks in both the seeding and alignment steps ofsequence-to-graph mapping.  To this end, we propose SeGraM, a universal algorithm/hardware co-designedgenomic mapping accelerator that can effectively and efficiently support bothsequence-to-graph mapping and sequence-to-sequence mapping, for both short andlong reads. To our knowledge, SeGraM is the first algorithm/hardware co-designfor accelerating sequence-to-graph mapping. SeGraM consists of two maincomponents: (1) MinSeed, the first minimizer-based seeding accelerator; and (2)BitAlign, the first bitvector-based sequence-to-graph alignment accelerator.  We demonstrate that SeGraM provides significant improvements for multiplesteps of the sequence-to-graph and sequence-to-sequence mapping pipelines.

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