Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4

Aitor Blanco-Míguez, Francesco Beghini, Fabio Cumbo, Lauren J McIver, Kelsey N Thompson, Moreno Zolfo, Paolo Manghi, Leonard Dubois, Kun D Huang, Andrew Maltez Thomas, William A Nickols, Gianmarco Piccinno, Elisa Piperni, Michal Punčochář, Mireia Valles-Colomer, Adrian Tett, Francesca Giordano, Richard Davies, Jonathan Wolf, Sarah E BerryTim D Spector, Eric A Franzosa, Edoardo Pasolli, Francesco Asnicar, Curtis Huttenhower, Nicola Segata (Corresponding author)

Publications: Contribution to journalArticlePeer Reviewed

Abstract

Metagenomic assembly enables new organism discovery from microbial communities, but it can only capture few abundant organisms from most metagenomes. Here we present MetaPhlAn 4, which integrates information from metagenome assemblies and microbial isolate genomes for more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01 M prokaryotic reference and metagenome-assembled genomes, we define unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains ~20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome and proves more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlights previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mouse microbiomes and shows that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains.

Original languageEnglish
Pages (from-to)1633-1644
Number of pages12
JournalNature Biotechnology
Volume41
Issue number11
Early online date23 Feb 2023
DOIs
Publication statusPublished - Nov 2023

Austrian Fields of Science 2012

  • 106026 Ecosystem research
  • 106022 Microbiology

Keywords

  • data processing
  • Metagenomics

Cite this