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Title: 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:
 [1];  [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [3]
  1. USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
  2. Univ. of Science and Technology of China, Hefei (China)
  3. 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 Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1559796
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
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. doi:10.7717/peerj.7359.
Kang, Dongwan D., Li, Feng, Kirton, Edward, Thomas, Ashleigh, Egan, Rob, An, Hong, and Wang, Zhong. Fri . "MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies". United States. doi: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},
journal = {PeerJ},
number = ,
volume = 7,
place = {United States},
year = {2019},
month = {7}
}

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