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Microbiome. 2016 Mar 8;4:8. doi: 10.1186/s40168-016-0154-5.

Recovering complete and draft population genomes from metagenome datasets.

Sangwan N1,2, Xia F3, Gilbert JA4,5,6,7.

Author information

1
Biosciences Division (BIO), Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL, 60439, USA. nikki1018sangwan@gmail.com.
2
Department of Surgery, University of Chicago, 5841 South Maryland Avenue, MC 5029, Chicago, IL, 60637, USA. nikki1018sangwan@gmail.com.
3
Computing, Environment and Life Sciences, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL, 60439, USA. fangfang@anl.gov.
4
Biosciences Division (BIO), Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL, 60439, USA. gilbertjack@gmail.com.
5
Department of Ecology and Evolution, University of Chicago, 1101 E 57th Street, Chicago, IL, 60637, USA. gilbertjack@gmail.com.
6
Department of Surgery, University of Chicago, 5841 South Maryland Avenue, MC 5029, Chicago, IL, 60637, USA. gilbertjack@gmail.com.
7
Marine Biological Laboratory, 7 MBL Street, Woods Hole, MA, 02543, USA. gilbertjack@gmail.com.

Abstract

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution.

PMID:
26951112
PMCID:
PMC4782286
DOI:
10.1186/s40168-016-0154-5
[Indexed for MEDLINE]
Free PMC Article
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