minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
bioRxiv, 10.1101/2023.05.30.542681
@article{liyanage2023minimap2,
title={minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping},
author={Liyanage, Kisaru and Samarakoon, Hiruna and Parameswaran, Sri and Gamaarachchi, Hasindu},
journal={bioRxiv},
pages={2023–05},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}
minimap2 is the gold-standard software for reference-based sequence mapping in third-generation long-read sequencing. While minimap2 is relatively fast, further speedup is desirable, especially when processing a multitude of large datasets. In this work, we present minimap2-fpga, a hardware-accelerated version of minimap2 that speeds up the mapping process by integrating an FPGA kernel optimised for chaining. We demonstrate speed-ups in end-to-end run-time for data from both Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio). minimap2-fpga is up to 79% and 53% faster than minimap2 for ~30x ONT and ~50x PacBio datasets respectively, when mapping without base-level alignment. When mapping with base-level alignment, minimap2-fpga is up to 62% and 10% faster than minimap2 for ~30x ONT and ~50x PacBio datasets respectively. The accuracy is near-identical to that of original minimap2 for both ONT and PacBio data, when mapping both with and without base-level alignment. minimap2-fpga is supported on Intel FPGA-based systems (evaluations performed on an on-premise system) and Xilinx FPGA-based systems (evaluations performed on a cloud system). We also provide a well-documented library for the FPGA-accelerated chaining kernel to be used by future researchers developing sequence alignment software with limited hardware background.
June 11, 2023 by hgpu