Minimap2: pairwise alignment for nucleotide sequences

Clicks: 2
ID: 289170
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #404 of 829 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Motivation: Recent advances in sequencing technologies promise ultra-long reads of $\sim$100 kilo bases (kb) in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 mega bases (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 $\ge$100bp in length, $\ge$1kb genomic reads at error rate $\sim$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 (INDELs) and introduces new heuristics to reduce spurious alignments. It is 3-4 times faster than mainstream short-read mappers at comparable accuracy and $\ge$30 times faster at higher accuracy for both genomic and mRNA reads, surpassing most aligners specialized in one type of alignment. Availability and implementation: https://github.com/lh3/minimap2 Contact: hengli@broadinstitute.org
Reference Key
openalex_W2789843538 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Heng Li
Journal BMC Bioinformatics
Year 2018
DOI
10.1093/bioinformatics/bty191
URL
https://academic.oup.com/bioinformatics/article-pdf/34/18/3094/25731859/bty191.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.