A probabilistic model to recover individual genomes from metagenomes
- Published
- Accepted
- Subject Areas
- Bioinformatics, Computational Biology, Data Science
- Keywords
- binning, metagenomics
- Copyright
- © 2016 Dröge et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. A probabilistic model to recover individual genomes from metagenomes. PeerJ Preprints 4:e2626v3 https://doi.org/10.7287/peerj.preprints.2626v3
Abstract
Shotgun metagenomics of microbial communities reveals information about strains of relevance for applications in medicine, biotechnology and ecology. Recovering their genomes is a crucial, but very challenging step, due to the complexity of the underlying biological system and technical factors. Microbial communities are heterogeneous, with oftentimes hundreds of present genomes deriving from different species or strains, all at varying abundances and with different degrees of similarity to each other and reference data. We present a versatile probabilistic model for genome recovery and analysis, which aggregates three types of information that are commonly used for genome recovery from metagenomes. As potential applications we showcase metagenome contig classification, genome sample enrichment and genome bin comparisons. The open source implementation MGLEX is available via the Python Package Index and on GitHub and can be embedded into metagenome analysis workflows and programs.
Author Comment
We re-calculated and updated table 1 with MGLEX v.0.1.1