Minimap2: Pairwise alignment for nucleotide sequences

Li, Heng

doi:10.1093/bioinformatics/bty191

Cited by 14,807Genomics and Phylogenetic StudiesAlgorithms and Data CompressionFractal and DNA sequence analysis

Minimap2: Pairwise alignment for nucleotide sequences

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Li, Heng·2018·Bioinformatics

DOI 10.1093/bioinformatics/bty191PMC PMC6137996

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Abstract

Motivation: Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms. Results: Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100 bp in length, ≥1 kb genomic reads at error rate ∼15%, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3–4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment. Availability and implementation: https://github.com/lh3/minimap2 Supplementary information: Supplementary data are available at Bioinformatics online.

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Minimap2's efficiency and speed largely stem from its ability to quickly identify and utilize anchors for alignment.
“The efficiency and speed of Minimap2 largely stem from its ability to quickly identify and utilize these anchors for alignment [44].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [44]
The Materials and methods section describes finding anchors and chaining them for alignment, and §2.1.1 describes a heuristic that accelerates chaining. But the Discussion attributes Minimap2’s performance to both fast base-level alignment and accurate chaining; it identifies base-level alignment as a frequent bottleneck. The claim overstates how much of its speed the paper attributes to identifying and using anchors. (LLM, conf=0.83)
Experimental — accuracy not yet measured
The performance of whole-genome alignment (WGA) algorithms is often evaluated based on their speed, memory usage, scalability, and accuracy.
“The performance of whole-genome alignment (WGA) algorithms is often evaluated based on their speed, memory usage, scalability, and accuracy [44].”
From Whole-Genome Alignment: Methods, Challenges, and Future Directions (2024) · cites this paper as [44]
Section 3.1 (Aligning long genomic reads) evaluates aligners by mapping accuracy, runtime, and peak memory use. Section 3.4 (Aligning long-read assemblies) also compares alignment speed and the precision of resulting variant calls. The copy does not establish scalability as an evaluation criterion, or show that all four criteria are often used to evaluate whole-genome alignment algorithms generally. (LLM, conf=0.84)
Experimental — accuracy not yet measured

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“Among linear genome aligners, minimap2 [ 29 ] uses minimizer sketches to quickly identify candidate alignment regions, thereby reducing the computational time required for sequence alignment, and kraken2 [ 30 ] creates a database of minimizers from reference genomes to rapidly identify the…”

Authors: Li, H.
Affiliations: Broad Institute (US)
Corresponding author: Li, Heng
ORCID: Li, Heng
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2018-05-10
Volume: 34
Issue: 18
Pages: 3094-3100
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/bty191
PMC: PMC6137996
OpenAlex: W2789843538
Cited by: 14,807
References: 6
Citations by year: 2026: 3090 · 2025: 3028 · 2024: 2532 · 2023: 1852 · 2022: 1617 · 2021: 1465 · 2020: 796 · 2019: 336 · 2018: 83 · 2017: 1 · 2016: 1 · 2014: 1 · 2013: 1
FWCI: 242.68
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Bioinformatics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: National Human Genome Research Institute (1R01HG010040-01)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Physical Sciences › Computer Science › Artificial Intelligence › Algorithms and Data Compression
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
Keywords: minimap2, sequence alignment, long-read mapping, reference genome mapping
MeSH terms: Algorithms, Base Sequence, Software, Sequence Analysis, DNA, Genomics, High-Throughput Nucleotide Sequencing

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Minimap2: Pairwise alignment for nucleotide sequences

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