MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities

By Dongwan D. Kang, Jeff L. Froula, Rob Egan, Zhong Wang. PeerJ, 2015

1,765 citations recorded by the source corpus.

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Because of the complex nature of these communities, existing metagenome binning methods often miss a large number of microbial species. In addition, most of the tools are not scalable to large datasets. Here we introduce automated software called MetaBAT that integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency for accurate metagenome binning. MetaBAT outperforms alternative methods in accuracy and computational efficiency on both synthetic and real metagenome datasets. It automatically forms hundreds of high quality genome bins on a very large assembly consisting millions of contigs in a matter of hours on a single node. MetaBAT is open source software and available at https://bitbucket.org/berkeleylab/metabat.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4e3a97dc04f1d122c889302368bd7f24f5f95123

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Citations in the corpus

Benchmarking Metagenomics Tools for Taxonomic Classification. (2019, background): “…should not be confused with a distinct class of assembly-based tools for analysis of metagenomic sequencing data that cluster contigs de novo without the aid of any reference databases, an approach known as reference-free binning (Alneberg et al., 2014; Kang et al., 2015; Wu et al., 2016).”
Marine Animal Microbiomes: Toward Understanding Host–Microbiome Interactions in a Changing Ocean (2017, methodology): “There are also a number of new tools for metagenomicsbased analysis, including advancements in binning genomes from complex samples (Kang et al., 2015; Graham et al., 2017) as well as new visualization methods for comparing genomes (Eren et al., 2015; Wagner et al., 2017).”
The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities (2020, methodology): “Starting in 2018, IMG incorporates the results of automated metagenome binning using MetaBAT (35) along with CheckM (36) and other quality assessment metrics for ensuring bin data quality (32).”
Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT (2019, neutral): “, [28, 29]), CAT classification can be considered as”
Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions (2017, methodology): “Most methods use integrative binning methods, which result in more accurate genome grouping but require summarizing acrossmultiple samples (Kang et al., 2015; Wu, Simmons, and Singer, 2016).”
Machine learning for metagenomics: methods and tools (2015, methodology): “MetaBat (Kang et al., 2015) is a binning method that uses a probabilistic distance based on the tetranucleotide frequency and coverage of contigs.”
IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes (2018, methodology): “MetaBAT (version 0.32.4) was run with a 3000 bp minimum contig cutoff, contig coverage information, and parameter ‘-superspecific’ for maximum specificity.”
A practical guide to amplicon and metagenomic analysis of microbiome data (2020, methodology): “Available binning tools include CONCOCT (Al-neberg et al., 2014), MaxBin 2 (Wu et al., 2015), and MetaBAT2 (Kang et al., 2015).”
SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline (2018, positive): “Currently, Maxbin (Wu et al., 2015) and Metabat2 (Kang et al., 2015) are supported.”