MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies
Abstract
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.
- Authors:
-
- 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)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1559796
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- PeerJ
- Additional Journal Information:
- Journal Volume: 7; Journal ID: ISSN 2167-8359
- Publisher:
- PeerJ Inc.
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Metagenomics; Metagenome binning; Clustering
Citation Formats
Kang, Dongwan D., Li, Feng, Kirton, Edward, Thomas, Ashleigh, Egan, Rob, An, Hong, and Wang, Zhong. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. United States: N. p., 2019.
Web. doi:10.7717/peerj.7359.
Kang, Dongwan D., Li, Feng, Kirton, Edward, Thomas, Ashleigh, Egan, Rob, An, Hong, & Wang, Zhong. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. United States. https://doi.org/10.7717/peerj.7359
Kang, Dongwan D., Li, Feng, Kirton, Edward, Thomas, Ashleigh, Egan, Rob, An, Hong, and Wang, Zhong. 2019.
"MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies". United States. https://doi.org/10.7717/peerj.7359. https://www.osti.gov/servlets/purl/1559796.
@article{osti_1559796,
title = {MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies},
author = {Kang, Dongwan D. and Li, Feng and Kirton, Edward and Thomas, Ashleigh and Egan, Rob and An, Hong and Wang, Zhong},
abstractNote = {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.},
doi = {10.7717/peerj.7359},
url = {https://www.osti.gov/biblio/1559796},
journal = {PeerJ},
issn = {2167-8359},
number = ,
volume = 7,
place = {United States},
year = {Fri Jul 26 00:00:00 EDT 2019},
month = {Fri Jul 26 00:00:00 EDT 2019}
}
Web of Science
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