MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm
Abstract
Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizingmore »
- Authors:
-
- Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physical Biosciences Division
- Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); City College of San Francisco, CA (United States)
- USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division
- Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States). Biological and Materials Sciences Center
- Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division
- 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:
- 1511393
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Microbiome
- Additional Journal Information:
- Journal Volume: 2; Journal ID: ISSN 2049-2618
- Publisher:
- BioMed Central
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; binning; metagenomics; expectation-maximization algorithm
Citation Formats
Wu, Yu-Wei, Tang, Yung-Hsu, Tringe, Susannah G., Simmons, Blake A., and Singer, Steven W. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. United States: N. p., 2014.
Web. doi:10.1186/2049-2618-2-26.
Wu, Yu-Wei, Tang, Yung-Hsu, Tringe, Susannah G., Simmons, Blake A., & Singer, Steven W. MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm. United States. https://doi.org/10.1186/2049-2618-2-26
Wu, Yu-Wei, Tang, Yung-Hsu, Tringe, Susannah G., Simmons, Blake A., and Singer, Steven W. Fri .
"MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm". United States. https://doi.org/10.1186/2049-2618-2-26. https://www.osti.gov/servlets/purl/1511393.
@article{osti_1511393,
title = {MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm},
author = {Wu, Yu-Wei and Tang, Yung-Hsu and Tringe, Susannah G. and Simmons, Blake A. and Singer, Steven W.},
abstractNote = {Recovering individual genomes from metagenomic datasets allows access to uncultivated microbial populations that may have important roles in natural and engineered ecosystems. Understanding the roles of these uncultivated populations has broad application in ecology, evolution, biotechnology and medicine. Accurate binning of assembled metagenomic sequences is an essential step in recovering the genomes and understanding microbial functions. We have developed a binning algorithm, MaxBin, which automates the binning of assembled metagenomic scaffolds using an expectation-maximization algorithm after the assembly of metagenomic sequencing reads. Binning of simulated metagenomic datasets demonstrated that MaxBin had high levels of accuracy in binning microbial genomes. MaxBin was used to recover genomes from metagenomic data obtained through the Human Microbiome Project, which demonstrated its ability to recover genomes from real metagenomic datasets with variable sequencing coverages. Application of MaxBin to metagenomes obtained from microbial consortia adapted to grow on cellulose allowed genomic analysis of new, uncultivated, cellulolytic bacterial populations, including an abundant myxobacterial population distantly related to Sorangium cellulosum that possessed a much smaller genome (5 MB versus 13 to 14 MB) but has a more extensive set of genes for biomass deconstruction. For the cellulolytic consortia, the MaxBin results were compared to binning using emergent self-organizing maps (ESOMs) and differential coverage binning, demonstrating that it performed comparably to these methods but had distinct advantages in automation, resolution of related genomes and sensitivity. The automatic binning software that we developed successfully classifies assembled sequences in metagenomic datasets into recovered individual genomes. The isolation of dozens of species in cellulolytic microbial consortia, including a novel species of myxobacteria that has the smallest genome among all sequenced aerobic myxobacteria, was easily achieved using the binning software. This work demonstrates that the processes required for recovering genomes from assembled metagenomic datasets can be readily automated, an important advance in understanding the metabolic potential of microbes in natural environments. MaxBin is available at https://sourceforge.net/projects/maxbin/.},
doi = {10.1186/2049-2618-2-26},
journal = {Microbiome},
number = ,
volume = 2,
place = {United States},
year = {Fri Aug 01 00:00:00 EDT 2014},
month = {Fri Aug 01 00:00:00 EDT 2014}
}
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Cited by: 359 works
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Figures / Tables:
Figure 1: The general workflow of MaxBin. Tetranucleotude frequencies, scaffold coverage levels, and single-copy marker genes are collected from metagenomic scaffolds. The collected information is computed by an expectation-maximization algorithm to bin sequences.
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