Title |
Recovering complete and draft population genomes from metagenome datasets
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Published in |
Microbiome, March 2016
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DOI | 10.1186/s40168-016-0154-5 |
Pubmed ID | |
Authors |
Naseer Sangwan, Fangfang Xia, Jack A. Gilbert |
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. |
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Geographical breakdown
Country | Count | As % |
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United States | 15 | 29% |
France | 5 | 10% |
United Kingdom | 3 | 6% |
Canada | 3 | 6% |
India | 2 | 4% |
Australia | 2 | 4% |
China | 1 | 2% |
Finland | 1 | 2% |
Mexico | 1 | 2% |
Other | 5 | 10% |
Unknown | 13 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 31 | 61% |
Members of the public | 19 | 37% |
Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 1% |
Brazil | 5 | <1% |
Canada | 5 | <1% |
Germany | 2 | <1% |
Sweden | 2 | <1% |
Spain | 2 | <1% |
Portugal | 2 | <1% |
Australia | 1 | <1% |
Egypt | 1 | <1% |
Other | 6 | <1% |
Unknown | 697 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 182 | 25% |
Researcher | 145 | 20% |
Student > Master | 114 | 16% |
Student > Bachelor | 65 | 9% |
Student > Doctoral Student | 35 | 5% |
Other | 97 | 13% |
Unknown | 93 | 13% |
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Biochemistry, Genetics and Molecular Biology | 158 | 22% |
Environmental Science | 47 | 6% |
Computer Science | 40 | 5% |
Immunology and Microbiology | 36 | 5% |
Other | 65 | 9% |
Unknown | 118 | 16% |