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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 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}
}

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Works referenced in this record:

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text, January 2017


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journal, September 2014


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A phylogenomic and ecological analysis of the globally abundant Marine Group II archaea (Ca. Poseidoniales ord. nov.)
journal, October 2018


Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life
journal, September 2017


Structure and function of the global topsoil microbiome
journal, August 2018


MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets
journal, October 2015


tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence
journal, March 1997


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journal, October 2013


metaSPAdes: a new versatile metagenomic assembler
journal, March 2017


Reconstructing single genomes from complex microbial communities
journal, January 2016


Critical Assessment of Metagenome Interpretation - A benchmark of metagenomics software
text, January 2017


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Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads
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A pipeline for targeted metagenomics of environmental bacteria
journal, February 2020


Optimizing de novo genome assembly from PCR-amplified metagenomes
journal, January 2019


Depth-discrete metagenomics reveals the roles of microbes in biogeochemical cycling in the tropical freshwater Lake Tanganyika
journal, February 2021


Gene Expression Changes and Community Turnover Differentially Shape the Global Ocean Metatranscriptome
journal, November 2019


Characteristics of Wetting-Induced Bacteriophage Blooms in Biological Soil Crust
journal, December 2019


Metagenome-Assembled Genome Sequences of Three Uncultured Planktomarina sp. Strains from the Northeast Atlantic Ocean
journal, March 2020


Metagenome-Assembled Genome Sequences of Five Strains from the Microtus ochrogaster (Prairie Vole) Fecal Microbiome
journal, January 2020


The Signal and the Noise: Characteristics of Antisense RNA in Complex Microbial Communities
journal, February 2020


Simulating metagenomic stable isotope probing datasets with MetaSIPSim
journal, January 2020


An Integrated Metagenome Catalog Reveals New Insights into the Murine Gut Microbiome
text, January 2020