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 · Fri Jul 26 00:00:00 EDT 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: 738 works

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