Title |
DESMAN: a new tool for de novo extraction of strains from metagenomes
|
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Published in |
Genome Biology, September 2017
|
DOI | 10.1186/s13059-017-1309-9 |
Pubmed ID | |
Authors |
Christopher Quince, Tom O. Delmont, Sébastien Raguideau, Johannes Alneberg, Aaron E. Darling, Gavin Collins, A. Murat Eren |
Abstract |
We introduce DESMAN for De novo Extraction of Strains from Metagenomes. Large multi-sample metagenomes are being generated but strain variation results in fragmentary co-assemblies. Current algorithms can bin contigs into metagenome-assembled genomes but are unable to resolve strain-level variation. DESMAN identifies variants in core genes and uses co-occurrence across samples to link variants into haplotypes and abundance profiles. These are then searched for against non-core genes to determine the accessory genome of each strain. We validated DESMAN on a complex 50-species 210-genome 96-sample synthetic mock data set and then applied it to the Tara Oceans microbiome. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 31 | 20% |
United Kingdom | 18 | 11% |
Germany | 7 | 4% |
Australia | 5 | 3% |
Mexico | 4 | 3% |
France | 4 | 3% |
Austria | 3 | 2% |
Chile | 3 | 2% |
India | 3 | 2% |
Other | 32 | 20% |
Unknown | 47 | 30% |
Demographic breakdown
Type | Count | As % |
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Scientists | 85 | 54% |
Members of the public | 65 | 41% |
Science communicators (journalists, bloggers, editors) | 6 | 4% |
Practitioners (doctors, other healthcare professionals) | 1 | <1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 364 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 93 | 26% |
Researcher | 73 | 20% |
Student > Master | 43 | 12% |
Student > Doctoral Student | 18 | 5% |
Student > Bachelor | 18 | 5% |
Other | 52 | 14% |
Unknown | 67 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 121 | 33% |
Biochemistry, Genetics and Molecular Biology | 68 | 19% |
Computer Science | 28 | 8% |
Immunology and Microbiology | 24 | 7% |
Environmental Science | 15 | 4% |
Other | 29 | 8% |
Unknown | 79 | 22% |