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Accelerating minimap2 for long-read sequencing applications on modern CPUs

Kalikar, S and Jain, C and Vasimuddin, M and Misra, S (2022) Accelerating minimap2 for long-read sequencing applications on modern CPUs. In: Nature Computational Science, 2 (2). pp. 78-83.

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Official URL: https://doi.org/10.1038/s43588-022-00201-8


Long-read sequencing is now routinely used at scale for genomics and transcriptomics applications. Mapping long reads or a draft genome assembly to a reference sequence is often one of the most time-consuming steps in these applications. Here we present techniques to accelerate minimap2, a widely used software for this task. We present multiple optimizations using single-instruction multiple-data parallelization, efficient cache utilization and a learned index data structure to accelerate the three main computational modules of minimap2: seeding, chaining and pairwise sequence alignment. These optimizations result in an up to 1.8-fold reduction of end-to-end mapping time of minimap2 while maintaining identical output. © 2022, The Author(s), under exclusive licence to Springer Nature America, Inc.

Item Type: Journal Article
Publication: Nature Computational Science
Publisher: Springer Nature
Additional Information: The copyright for this article belongs to Springer Nature
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 13 May 2022 16:25
Last Modified: 13 May 2022 16:30
URI: https://eprints.iisc.ac.in/id/eprint/71641

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