Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps

Alexander T. Dilthey (), Chirag Jain, Sergey Koren and Adam M. Phillippy
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Alexander T. Dilthey: Heinrich-Heine-University Düsseldorf
Chirag Jain: National Human Genome Research Institute
Sergey Koren: National Human Genome Research Institute
Adam M. Phillippy: National Human Genome Research Institute

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: Abstract Metagenomic sequence classification should be fast, accurate and information-rich. Emerging long-read sequencing technologies promise to improve the balance between these factors but most existing methods were designed for short reads. MetaMaps is a new method, specifically developed for long reads, capable of mapping a long-read metagenome to a comprehensive RefSeq database with >12,000 genomes in 94% accuracy for species-level read assignment and r2 > 0.97 for the estimation of sample composition on both simulated and real data when the sample genomes or close relatives are present in the classification database. To address novel species and genera, which are comparatively harder to predict, MetaMaps outputs mapping locations and qualities for all classified reads, enabling functional studies (e.g. gene presence/absence) and detection of incongruities between sample and reference genomes.

Date: 2019
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DOI: 10.1038/s41467-019-10934-2

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