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dc.contributor.authorLax, Simon  Concept link
dc.contributor.authorHampton-Marcell, Jarrad T.  Concept link
dc.contributor.authorGibbons, Sean M.  Concept link
dc.contributor.authorColares, Georgia Barguil  Concept link
dc.contributor.authorSmith, Daniel  Concept link
dc.contributor.authorEisen, Jonathan A.  Concept link
dc.contributor.authorGilbert, Jack A.  Concept link
dc.date.accessioned2015-05-19T14:05:10Z
dc.date.available2015-05-19T14:05:10Z
dc.date.issued2015-05-12
dc.identifier.citationMicrobiome 3 (2015): 21en_US
dc.identifier.urihttps://hdl.handle.net/1912/7295
dc.description© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Microbiome 3 (2015): 21, doi:10.1186/s40168-015-0082-9.en_US
dc.description.abstractMicrobial interaction between human-associated objects and the environments we inhabit may have forensic implications, and the extent to which microbes are shared between individuals inhabiting the same space may be relevant to human health and disease transmission. In this study, two participants sampled the front and back of their cell phones, four different locations on the soles of their shoes, and the floor beneath them every waking hour over a 2-day period. A further 89 participants took individual samples of their shoes and phones at three different scientific conferences. Samples taken from different surface types maintained significantly different microbial community structures. The impact of the floor microbial community on that of the shoe environments was strong and immediate, as evidenced by Procrustes analysis of shoe replicates and significant correlation between shoe and floor samples taken at the same time point. Supervised learning was highly effective at determining which participant had taken a given shoe or phone sample, and a Bayesian method was able to determine which participant had taken each shoe sample based entirely on its similarity to the floor samples. Both shoe and phone samples taken by conference participants clustered into distinct groups based on location, though much more so when an unweighted distance metric was used, suggesting sharing of low-abundance microbial taxa between individuals inhabiting the same space. Correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment.en_US
dc.description.sponsorshipThis work was enabled by the generous support of the Alfred P Sloan foundation. This work was supported in part by the U.S. Dept. of Energy under Contract DE-AC02-06CH11357. S.M.G. was supported by an EPA STAR Graduate Fellowship and by a National Institutes of Health Training Grant 5 T-32 EB-009412.en_US
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/vnd.ms-excel
dc.format.mimetypeimage/png
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.urihttps://doi.org/10.1186/s40168-015-0082-9
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectForensic microbiologyen_US
dc.subjectSource-sink dynamicsen_US
dc.subjectShoe microbiomeen_US
dc.subjectPhone microbiomeen_US
dc.subjectMicrobial time seriesen_US
dc.titleForensic analysis of the microbiome of phones and shoesen_US
dc.typeArticleen_US
dc.identifier.doi10.1186/s40168-015-0082-9


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International