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

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 <16 GB or RAM on a laptop computer. Integrating approximate mapping with probabilistic scoring and EM-based estimation of sample composition, MetaMaps achieves >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 ad

SUBMITTER: Dilthey AT 

PROVIDER: S-EPMC6624308 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature