minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping

Liyanage, K.; Samarakoon, H.; Parameswaran, S.; Gamaarachchi, H.

doi:10.1101/2023.05.30.542681

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bioRxiv · 10.1101/2023.05.30.542681

minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping

Liyanage, K.·Samarakoon, H.·Parameswaran, S.·Gamaarachchi, H.

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Abstract

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.

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Liyanage, K., Samarakoon, H., Parameswaran, S., Gamaarachchi, H.. 2023-06-02. minimap2-fpga: Integrating hardware-accelerated chaining for efficient end-to-end long-read sequence mapping. https://doi.org/10.1101/2023.05.30.542681

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