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arXiv · 2205.05883

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

Damla Senol Cali·Konstantinos Kanellopoulos·Joel Lindegger·Zülal Bingöl·Gurpreet S. Kalsi·Ziyi Zuo·Can Firtina·Meryem Banu Cavlak·Jeremie Kim·Nika Mansouri Ghiasi·Gagandeep Singh·Juan Gómez-Luna·Nour Almadhoun Alserr·Mohammed Alser·Sreenivas Subramoney·Can Alkan·Saugata Ghose·Onur Mutlu
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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-to-sequence 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 difficult 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 find 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. To this end, we propose SeGraM, a universal algorithm/hardware co-designed genomic mapping accelerator that can effectively and efficiently support both sequence-to-graph mapping and sequence-to-sequence mapping, for both short and long reads. To our knowledge, SeGraM is the first algorithm/hardware co-design for accelerating sequence-to-graph mapping. SeGraM consists of two main components: (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 multiple steps of the sequence-to-graph and sequence-to-sequence mapping pipelines.

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Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger, Zülal Bingöl, Gurpreet S. Kalsi, Ziyi Zuo, Can Firtina, Meryem Banu Cavlak, Jeremie Kim, Nika Mansouri Ghiasi, Gagandeep Singh, Juan Gómez-Luna, Nour Almadhoun Alserr, Mohammed Alser, Sreenivas Subramoney, Can Alkan, Saugata Ghose, Onur Mutlu. 2022-05-31. SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping. https://doi.org/10.1145/3470496.3527436

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