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

Journal Article · · Microbiome
 [1];  [2];  [3];  [4];  [5]
  1. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physical Biosciences Division
  2. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); City College of San Francisco, CA (United States)
  3. USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Genomics Division
  4. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States). Biological and Materials Sciences Center
  5. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division
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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/.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1511393
Journal Information:
Microbiome, Vol. 2; ISSN 2049-2618
Publisher:
BioMed CentralCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 359 works
Citation information provided by
Web of Science

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Cited By (11)

Refining the phylum Chlorobi by resolving the phylogeny and metabolic potential of the representative of a deeply branching, uncultivated lineage journal September 2015
Anaerobic degradation of hexadecane and phenanthrene coupled to sulfate reduction by enriched consortia from northern Gulf of Mexico seafloor sediment journal February 2019
Intensive allochthonous inputs along the Ganges River and their effect on microbial community composition and dynamics journal November 2018
IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses journal December 2016
VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data journal January 2015
Intermediate-Salinity Systems at High Altitudes in the Peruvian Andes Unveil a High Diversity and Abundance of Bacteria and Viruses journal November 2019
Metagenomic reconstructions of gut microbial metabolism in weanling pigs journal March 2019
Phylogenetic, genomic, and biogeographic characterization of a novel and ubiquitous marine invertebrate-associated Rickettsiales parasite, Candidatus Aquarickettsia rohweri, gen. nov., sp. nov journal August 2019
Accurate and complete genomes from metagenomes journal March 2020
KBase Narrative - Metagenome-assembled genomes from Amazonian soil microbial consortia dataset January 2022
Machine learning for metagenomics: methods and tools preprint January 2015

Figures / Tables (10)

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59 BASIC BIOLOGICAL SCIENCES
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binning
metagenomics
expectation-maximization algorithm