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Harp: Leveraging Quasi-Sequential Characteristics to Accelerate Sequence-to-Graph Mapping of Long Reads

Published: 27 April 2024 Publication History

Abstract

Read mapping is a crucial task in computational genomics. Recently, there has been a significant paradigm shift from sequence-to-sequence mapping (S2S) to sequence-to-graph mapping (S2G). The S2G mapping incurs high graph processing overheads and leads to an unnoticed shift of performance hotspots. This presents a substantial challenge to current software implementations and hardware accelerators.
This paper introduces Harp, a novel S2G mapping acceleration system. Harp leverages the structural similarities between genome graphs and sequences, significantly reducing graph processing overhead by exploiting their quasi-sequential characteristic. The Harp accelerator is co-designed with two main algorithmic components: (1) HarpTree, a compact data structure that explicitly reveals the quasi-sequential characteristic, enabling simplifications of graph processing algorithms in S2G mapping, and (2) HarpExt, a multi-stage seed-extension algorithm that mitigates graph operation overhead while maintaining the sight of S2G mapping. Harp achieves an average 140× speedup over the latest S2G mapping software and outperforms the state-of-the-art S2G accelerator by 23.6× while reducing the chip area by 72% in S2G mapping of long reads.

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      cover image ACM Conferences
      ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3
      April 2024
      1106 pages
      ISBN:9798400703867
      DOI:10.1145/3620666
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Published: 27 April 2024

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      1. hardware accelerator
      2. algorithm-hardware co-design
      3. genomics
      4. pangenome analysis
      5. genome graph
      6. read mapping
      7. co-linear chaining

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