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.
Full text not available from this repository.Abstract
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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