lunes, 13 de octubre de 2014

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

full-text ►

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



Microbiome



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

Yu-Wei Wu12*Yung-Hsu Tang13Susannah G Tringe45Blake A Simmons16 and Steven W Singer17
1Joint BioEnergy Institute, Emeryville, CA 94608, USA
2Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
3City College of San Francisco, San Francisco, CA 94112, USA
4Joint Genome Institute, Walnut Creek, CA 94598, USA
5Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
6Biological and Materials Sciences Center, Sandia National Laboratories, Livermore, CA 94551, USA
7Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
For all author emails, please log on.
Microbiome 2014, 2:26  doi:10.1186/2049-2618-2-26

The electronic version of this article is the complete one and can be found online at:http://www.microbiomejournal.com/content/2/1/26

Received:4 March 2014
Accepted:4 June 2014
Published:1 August 2014
© 2014 Wu et al.; licensee BioMed Central Ltd. 
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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 athttps://sourceforge.net/projects/maxbin/ webcite.
Keywords: 
Binning; Metagenomics; Expectation-maximization algorithm

No hay comentarios:

Publicar un comentario