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Accelerating minimap2 for long-read sequencing applications on modern CPUs
Saurabh Kalikar1, Chirag Jain2, Md Vasimuddin3
1Intel Labs, Bangalore, India. saurabh.kalikar@intel.com.
Nature Computational Science
|January 4, 2024
Summary
Researchers optimized minimap2, a tool for mapping long sequencing reads, to significantly speed up genomics and transcriptomics workflows. These enhancements reduce mapping time by up to 1.8-fold without affecting accuracy.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Long-read sequencing is a standard tool for genomics and transcriptomics.
- Mapping sequencing reads to a reference genome is a critical but time-consuming step.
Purpose of the Study:
- To accelerate the minimap2 software for faster long-read mapping.
- To improve the efficiency of genomics and transcriptomics data analysis pipelines.
Main Methods:
- Implemented single-instruction multiple-data (SIMD) parallelization.
- Optimized cache utilization for computational modules.
- Integrated a learned index data structure.
- Targeted seeding, chaining, and pairwise sequence alignment modules within minimap2.
Main Results:
- Achieved up to a 1.8-fold reduction in end-to-end mapping time for minimap2.
- Maintained identical output accuracy compared to the unoptimized version.
- Demonstrated significant speedups in key computational steps of the mapping process.
Conclusions:
- The presented optimizations substantially accelerate minimap2 performance.
- These improvements enhance the efficiency of large-scale genomics and transcriptomics studies.
- Faster mapping facilitates quicker analysis of complex genomic data.
