Accelerating long-read analysis on modern CPUs
bioRxiv(2022)
Abstract
Long read sequencing is now routinely used at scale for genomics and transcriptomics applications. Mapping of 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 mapping. We present multiple optimizations using SIMD parallelization, efficient cache utilization and a learned index data structure to accelerate its three main computational modules, i.e., seeding, chaining and pairwise sequence alignment. These result in reduction of end-to-end mapping time of minimap2 by up to 1.8 × while maintaining identical output.
### Competing Interest Statement
SK, VM and SM are employees of Intel Corporation
MoreTranslated text
Key words
long-read
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined