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RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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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.

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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.