GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes
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.
- Authors:
-
- Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia
- Publication Date:
- Research Org.:
- Univ. of Arizona, Tucson, AZ (United States); The Ohio State Univ., Columbus, OH (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1439479
- Alternate Identifier(s):
- OSTI ID: 1502447
- Grant/Contract Number:
- SC0004632; SC0010580; SC0016440
- Resource Type:
- Published Article
- Journal Name:
- Nucleic Acids Research
- Additional Journal Information:
- Journal Name: Nucleic Acids Research Journal Volume: 46 Journal Issue: 10; Journal ID: ISSN 0305-1048
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Boyd, Joel A., Woodcroft, Ben J., and Tyson, Gene W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. United Kingdom: N. p., 2018.
Web. doi:10.1093/nar/gky174.
Boyd, Joel A., Woodcroft, Ben J., & Tyson, Gene W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. United Kingdom. https://doi.org/10.1093/nar/gky174
Boyd, Joel A., Woodcroft, Ben J., and Tyson, Gene W. Mon .
"GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes". United Kingdom. https://doi.org/10.1093/nar/gky174.
@article{osti_1439479,
title = {GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes},
author = {Boyd, Joel A. and Woodcroft, Ben J. and Tyson, Gene W.},
abstractNote = {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.},
doi = {10.1093/nar/gky174},
journal = {Nucleic Acids Research},
number = 10,
volume = 46,
place = {United Kingdom},
year = {Mon Mar 19 00:00:00 EDT 2018},
month = {Mon Mar 19 00:00:00 EDT 2018}
}
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Cited by: 69 works
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Figures / Tables:
Figure 1: Schematic of the GraftM pipeline, outlining the create, search and classify stages.Within the search step, the red arrow indicates the amino acid pipeline and the blue arrow indicates the nucleic acid pipeline.
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