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Assembly-Driven Community Genomics of a Hypersaline Microbial Ecosystem

Overview of attention for article published in PLOS ONE, April 2013
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Title
Assembly-Driven Community Genomics of a Hypersaline Microbial Ecosystem
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0061692
Pubmed ID
Authors

Sheila Podell, Juan A. Ugalde, Priya Narasingarao, Jillian F. Banfield, Karla B. Heidelberg, Eric E. Allen

Abstract

Microbial populations inhabiting a natural hypersaline lake ecosystem in Lake Tyrrell, Victoria, Australia, have been characterized using deep metagenomic sampling, iterative de novo assembly, and multidimensional phylogenetic binning. Composite genomes representing habitat-specific microbial populations were reconstructed for eleven different archaea and one bacterium, comprising between 0.6 and 14.1% of the planktonic community. Eight of the eleven archaeal genomes were from microbial species without previously cultured representatives. These new genomes provide habitat-specific reference sequences enabling detailed, lineage-specific compartmentalization of predicted functional capabilities and cellular properties associated with both dominant and less abundant community members, including organisms previously known only by their 16S rRNA sequences. Together, these data provide a comprehensive, culture-independent genomic blueprint for ecosystem-wide analysis of protein functions, population structure, and lifestyles of co-existing, co-evolving microbial groups within the same natural habitat. The "assembly-driven" community genomic approach demonstrated in this study advances our ability to push beyond single gene investigations, and promotes genome-scale reconstructions as a tangible goal in the quest to define the metabolic, ecological, and evolutionary dynamics that underpin environmental microbial diversity.

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Geographical breakdown

Country Count As %
United States 10 9%
Germany 1 <1%
Australia 1 <1%
Chile 1 <1%
Estonia 1 <1%
Saudi Arabia 1 <1%
Unknown 101 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 31 27%
Student > Ph. D. Student 29 25%
Student > Master 15 13%
Student > Bachelor 9 8%
Professor 5 4%
Other 11 9%
Unknown 16 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 52%
Biochemistry, Genetics and Molecular Biology 11 9%
Environmental Science 8 7%
Immunology and Microbiology 6 5%
Computer Science 5 4%
Other 9 8%
Unknown 17 15%