MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

Kang, Dongwan D.; Li, Feng; Kirton, Edward; Thomas, Ashleigh; Egan, Rob; An, Hong; Wang, Zhong

doi:10.7717/peerj.7359

Title: MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies

Journal Article · 26 July 2019 · PeerJ

DOI: https://doi.org/10.7717/peerj.7359 · OSTI ID:1559796

Kang, Dongwan D. ^[1]; Li, Feng ^[2];

^[1];

^[1]; An, Hong ^[2];

^[3]

USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
Univ. of Science and Technology of China, Hefei (China)
USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California at Merced, Merced, CA (United States)

We previously reported on MetaBAT, an automated metagenome binning software tool to reconstruct single genomes from microbial communities for subsequent analyses of uncultivated microbial species. MetaBAT has become one of the most popular binning tools largely due to its computational efficiency and ease of use, especially in binning experiments with a large number of samples and a large assembly. MetaBAT requires users to choose parameters to fine-tune its sensitivity and specificity. If those parameters are not chosen properly, binning accuracy can suffer, especially on assemblies of poor quality. Here, we developed MetaBAT 2 to overcome this problem. MetaBAT 2 uses a new adaptive binning algorithm to eliminate manual parameter tuning. We also performed extensive software engineering optimization to increase both computational and memory efficiency. Comparing MetaBAT 2 to alternative software tools on over 100 real world metagenome assemblies shows superior accuracy and computing speed. Binning a typical metagenome assembly takes only a few minutes on a single commodity workstation. We therefore recommend the community adopts MetaBAT 2 for their metagenome binning experiments. MetaBAT 2 is open source software and available at https://bitbucket.org/berkeleylab/metabat.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1559796

Journal Information:: PeerJ, Vol. 7; ISSN 2167-8359

Publisher:: PeerJ Inc.Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 1042 works

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Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle Pasolli, Edoardo; Asnicar, Francesco; Manara, Serena Cell, Vol. 176, Issue 3 https://doi.org/10.1016/j.cell.2019.01.001	journal	January 2019
Ten years of next-generation sequencing technology van Dijk, Erwin L.; Auger, Hélène; Jaszczyszyn, Yan Trends in Genetics, Vol. 30, Issue 9 https://doi.org/10.1016/j.tig.2014.07.001	journal	September 2014
Metagenomics: DNA sequencing of environmental samples Tringe, Susannah Green; Rubin, Edward M. Nature Reviews Genetics, Vol. 6, Issue 11 https://doi.org/10.1038/nrg1709	journal	October 2005
A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.) Rinke, Christian; Rubino, Francesco; Messer, Lauren F. The ISME Journal, Vol. 13, Issue 3 https://doi.org/10.1038/s41396-018-0282-y	journal	October 2018
Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life Parks, Donovan H.; Rinke, Christian; Chuvochina, Maria Nature Microbiology, Vol. 2, Issue 11 https://doi.org/10.1038/s41564-017-0012-7	journal	September 2017
Structure and function of the global topsoil microbiome Bahram, Mohammad; Hildebrand, Falk; Forslund, Sofia K. Nature, Vol. 560, Issue 7717 https://doi.org/10.1038/s41586-018-0386-6	journal	August 2018
Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes Lin, Hsin-Hung; Liao, Yu-Chieh Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep24175	journal	April 2016
MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets Wu, Yu-Wei; Simmons, Blake A.; Singer, Steven W. Bioinformatics, Vol. 32, Issue 4 https://doi.org/10.1093/bioinformatics/btv638	journal	October 2015
COCACOLA: binning metagenomic contigs using sequence COmposition, read CoverAge, CO-alignment and paired-end read LinkAge Lu, Yang Young; Chen, Ting; Fuhrman, Jed A. Bioinformatics https://doi.org/10.1093/bioinformatics/btw290	journal	June 2016
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IMG 4 version of the integrated microbial genomes comparative analysis system Markowitz, Victor M.; Chen, I-Min A.; Palaniappan, Krishna Nucleic Acids Research, Vol. 42, Issue D1 https://doi.org/10.1093/nar/gkt963	journal	October 2013
metaSPAdes: a new versatile metagenomic assembler Nurk, Sergey; Meleshko, Dmitry; Korobeynikov, Anton Genome Research, Vol. 27, Issue 5 https://doi.org/10.1101/gr.213959.116	journal	March 2017
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MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities Kang, Dongwan D.; Froula, Jeff; Egan, Rob PeerJ, Vol. 3 https://doi.org/10.7717/peerj.1165	journal	January 2015

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