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

By Yu-Wei Wu, Yung-Hsu Tang, Susannah Tringe, Blake A. Simmons, Steven W. Singer. Microbiome, 2014

658 citations recorded by the source corpus.

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/.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f9682ec574e1b1667cb082125e6f04ac5d18f839

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Citations in the corpus

The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, methodology): “Other methods have more than one strategy to support the correct binning of sequences as in the case of MaxBin (Wu et al., 2014) and Amphora2 (Wu and Scott, 2012), which rely on ﬁnding single copy marker genes, k-mer signatures, GC content, and coverage information to perform contig and read…”
Hi-C deconvolution of a human gut microbiome yields high-quality draft genomes and reveals plasmid-genome interactions (2017, methodology): “Resulting clusters smaller than 1000 bp were omitted, as is common in analyzing metagenomic assemblies (Wu et al., 2014).”
Machine learning for metagenomics: methods and tools (2015, methodology): “To circumvent this difficulty, MaxBin can use results from FragGeneScan to identify single-copy marker genes and thus derive estimates for the number of bins.”
MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities (2015, methodology): “172 These software include Canopy (Nielsen et al. 2014), CONCOCT v.0.4.0 (Alneberg et al. 2014), 173 GroopM v.0.3.0 (Imelfort et al. 2014), and MaxBin v.1.4.1 (Wu et al. 2014).”
Current challenges and best-practice protocols for microbiome analysis (2019, background): “considers nucleotide composition and contig abundance information for binning through an expectation–maximization (EM) algorithm that precisely clusters metagenomic contigs into bins consisting of contigs from a single species [104].”
CoverM: read alignment statistics for metagenomics (2025, neutral): “For instance, some use the number of aligned reads divided by contig length (Wu et al. 2014, Nissen et al. 2021, Pan et al. 2022, 2023).”