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dc.contributor.author
Svercel, Miroslav
dc.contributor.author
Filippini, Manuela
dc.contributor.author
Perony, Nicolas
dc.contributor.author
Rossetti, Valentina
dc.contributor.author
Bagheri, Homayoun C.
dc.date.accessioned
2018-07-26T08:21:33Z
dc.date.available
2017-06-10T18:28:02Z
dc.date.available
2018-07-26T08:21:33Z
dc.date.issued
2013-05-31
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0065059
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/68593
dc.identifier.doi
10.3929/ethz-b-000068593
dc.description.abstract
An understanding of bacterial diversity and evolution in any environment requires knowledge of phenotypic diversity. In this study, the underlying factors leading to phenotypic clustering were analyzed and interpreted using a novel approach based on a four-tiered graph. Bacterial isolates were organized into equivalence classes based on their phenotypic profile. Likewise, phenotypes were organized in equivalence classes based on the bacteria that manifest them. The linking of these equivalence classes in a four-tiered graph allowed for a quick visual identification of the phenotypic measurements leading to the clustering patterns deduced from principal component analyses. For evaluation of the method, we investigated phenotypic variation in enzyme production and carbon assimilation of members of the genera Pseudomonas and Serratia, isolated from the Aletsch Glacier in Switzerland. The analysis indicates that the genera isolated produce at least six common enzymes and can exploit a wide range of carbon resources, though some specialist species within the pseudomonads were also observed. We further found that pairwise distances between enzyme profiles strongly correlate with distances based on carbon profiles. However, phenotypic distances weakly correlate with phylogenetic distances. The method developed in this study facilitates a more comprehensive understanding of phenotypic clustering than what would be deduced from principal component analysis alone.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Use of a Four-Tiered Graph to Parse the Factors Leading to Phenotypic Clustering in Bacteria: A Case Study Based on Samples from the Aletsch Glacier
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
ethz.journal.title
PLoS ONE
ethz.journal.volume
8
en_US
ethz.journal.issue
5
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e65059
en_US
ethz.size
8 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
006206116
ethz.publication.place
Lawrence, Kan.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03682 - Schweitzer, Frank / Schweitzer, Frank
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02120 - Dep. Management, Technologie und Ökon. / Dep. of Management, Technology, and Ec.::03682 - Schweitzer, Frank / Schweitzer, Frank
ethz.relation.isReferencedBy
https://doi.org/10.1371/annotation/3f02ad8b-6dac-4460-a969-90b3f5eb1306
ethz.date.deposited
2017-06-10T18:30:48Z
ethz.source
ECIT
ethz.identifier.importid
imp593650bb719a858327
ethz.ecitpid
pub:108934
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-14T15:04:41Z
ethz.rosetta.lastUpdated
2018-11-07T17:23:43Z
ethz.rosetta.versionExported
true
ethz.COinS
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