The Science

Genomes reconstructed directly from DNA sequences sampled from natural environments have revolutionized scientific understanding of microbial diversity and evolution. While this process can be difficult, a new automated method called DAS Tool integrates a flexible number of binning algorithms to calculate an optimized, non-redundant set of bins from a single assembly, thereby greatly improving the recovery of genomes from natural environments.

The Impact

The recovery of genomes, especially from complex environments such as soil, will be facilitated by the new automated DAS Tool.

Summary

Understanding of the metabolic capacities of microorganisms in natural environments is critical to prediction of ecosystem function. Analysis of organism-specific metabolic pathways and reconstruction of community interaction networks requires high-quality genomes. However, existing binning methods often fail to reconstruct a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and environment types. A dereplication, aggregation, and scoring strategy, DAS Tool, was developed. This algorithm combines the strengths of a flexible set of established binning algorithms. DAS Tool applied to a constructed community generated more accurate bins than any automated method. Indeed, when applied to environmental and host-associated samples of different complexity, DAS Tool recovered substantially more near-complete genomes, including those for organisms from previously unreported lineages, than any single binning method alone. The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.

Principal Investigator

Jillian Banfield
University of California, Berkeley
jbanfield@berkeley.edu

Program Manager

Paul Bayer
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
paul.bayer@science.doe.gov

Funding

This work was supported in part by the Office of Biological and Environmental Research within the U.S. Department of Energy Office of Science.

References