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Microbiome. 2014 Aug 1;2:26. doi: 10.1186/2049-2618-2-26. eCollection 2014.

MaxBin: an automated binning method to recover individual genomes from metagenomes using an expectation-maximization algorithm.

Author information

1
Joint BioEnergy Institute, Emeryville, CA 94608, USA ; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
2
Joint BioEnergy Institute, Emeryville, CA 94608, USA ; City College of San Francisco, San Francisco, CA 94112, USA.
3
Joint Genome Institute, Walnut Creek, CA 94598, USA ; Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.
4
Joint BioEnergy Institute, Emeryville, CA 94608, USA ; Biological and Materials Sciences Center, Sandia National Laboratories, Livermore, CA 94551, USA.
5
Joint BioEnergy Institute, Emeryville, CA 94608, USA ; Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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/.

KEYWORDS:

Binning; Expectation-maximization algorithm; Metagenomics

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