ArticleNucleic acids research2018
GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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94 citing papers in PubMed.
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- Fermentative nitrite ammonifiers are abundant in soils and ecologically distinct from NrfA-dependent ammonifiers.ISME communications · 2026Article
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- Denitrification is a community trait with partial pathways dominating across microbial genomes and biomes.Nature communications · 2025Article
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- Leveraging core enzyme structures for microbiota targeted functional regulation: Urease as an example.iMeta · 2025Article
- Characterisation of the thermophilic P450 CYP116B305 identified using metagenomics-derived sequence data from an Australian hot spring.Applied microbiology and biotechnology · 2025Article
- Metagenomic insights into taxonomic and functional patterns in shallow coastal and deep subseafloor sediments in the Western Pacific.Microbial genomics · 2025Article
- Methane trapping in permafrost soils: a biogeochemical dataset across Alaskan boreal-Arctic gradient.Scientific data · 2025Article
34 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale metagenomic datasets enable the recovery of hundreds of population genomes from environmental samples. However, these genomes do not typically represent the full diversity of complex microbial communities. Gene-centric approaches can be used to gain a comprehensive view of diversity by examining each read independently, but traditional pairwise comparison approaches typically over-classify taxonomy and scale poorly with increasing metagenome and database sizes. Here we introduce GraftM, a tool that uses gene specific packages to rapidly identify gene families in metagenomic data using hidden Markov models (HMMs) or DIAMOND databases, and classifies these sequences using placement into pre-constructed gene trees. The speed and accuracy of GraftM was benchmarked with in silico and in vitro mock communities using taxonomic markers, and was found to have higher accuracy at the family level with a processing time 2.0-3.7× faster than currently available software. Exploration of a wetland metagenome using 16S rRNA- and methyl-coenzyme M reductase (McrA)-specific gpkgs revealed taxonomic and functional shifts across a depth gradient. Analysis of the NCBI nr database using the McrA gpkg allowed the detection of novel sequences belonging to phylum-level lineages. A growing collection of gpkgs is available online (https://github.com/geronimp/graftM_gpkgs), where curated packages can be uploaded and exchanged.
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