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Prediction of effective genome size in metagenomic samples

Overview of attention for article published in Genome Biology, January 2007
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Title
Prediction of effective genome size in metagenomic samples
Published in
Genome Biology, January 2007
DOI 10.1186/gb-2007-8-1-r10
Pubmed ID
Authors

Jeroen Raes, Jan O Korbel, Martin J Lercher, Christian von Mering, Peer Bork

Abstract

We introduce a novel computational approach to predict effective genome size (EGS; a measure that includes multiple plasmid copies, inserted sequences, and associated phages and viruses) from short sequencing reads of environmental genomics (or metagenomics) projects. We observe considerable EGS differences between environments and link this with ecologic complexity as well as species composition (for instance, the presence of eukaryotes). For example, we estimate EGS in a complex, organism-dense farm soil sample at about 6.3 megabases (Mb) whereas that of the bacteria therein is only 4.7 Mb; for bacteria in a nutrient-poor, organism-sparse ocean surface water sample, EGS is as low as 1.6 Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 443 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 16 4%
France 6 1%
Germany 5 1%
Brazil 4 <1%
United Kingdom 3 <1%
Sweden 3 <1%
Netherlands 2 <1%
South Africa 2 <1%
Poland 2 <1%
Other 14 3%
Unknown 386 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 123 28%
Researcher 108 24%
Student > Master 47 11%
Student > Doctoral Student 29 7%
Student > Bachelor 27 6%
Other 72 16%
Unknown 37 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 248 56%
Biochemistry, Genetics and Molecular Biology 45 10%
Environmental Science 32 7%
Computer Science 21 5%
Immunology and Microbiology 14 3%
Other 36 8%
Unknown 47 11%