Application of machine learning techniques for creating urban microbial fingerprints

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dc.contributor.author Ryan, Feargal J.
dc.date.accessioned 2019-12-10T11:40:48Z
dc.date.available 2019-12-10T11:40:48Z
dc.date.issued 2019-08-16
dc.identifier.citation Ryan, F. J. (2019) 'Application of machine learning techniques for creating urban microbial fingerprints', Biology Direct, 14(1), 13 (13pp). doi: 10.1186/s13062-019-0245-x en
dc.identifier.volume 14 en
dc.identifier.issued 1 en
dc.identifier.startpage 1 en
dc.identifier.endpage 13 en
dc.identifier.uri http://hdl.handle.net/10468/9377
dc.identifier.doi 10.1186/s13062-019-0245-x en
dc.description.abstract Background: Research has found that human associated microbial communities play a role in homeostasis and the disruption of these communities may be important in an array of medical conditions. However outside of the human body many of these communities remain poorly studied. The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is characterizing the microbiomes of urban environments with the aim to improve design of mass transit systems. As part of the CAMDA 2018 MetaSUB Forensics Challenge 311 city microbiome samples were provided to create urban microbial fingerprints, as well as a further 3 mystery datasets for validation. Results: MetaSUB samples were clustered using t-SNE in an unsupervised fashion to almost discrete groups, which upon inspection represented city of origin. Based on this clustering, geographically close metropolitan areas appear to display similar microbial profiles such as those of Auckland and Hamilton. Mystery unlabeled samples were provided part of the challenge. A random forest classifier built on the initial dataset of 311 samples was capable of correctly classifying 83.3% of the mystery samples to their city of origin. Random Forest analyses also identified features with the highest discriminatory power, ranking bacterial species such as Campylobacter jejuni and Staphylococcus argenteus as highly predictive of city of origin. The surface from which the sample was collected displayed little detectable impact on the microbial profiles in the data generated here. The proportion of reads classified per sample varied greatly and so de-novo assembly was applied to recover genomic fragments representing organisms not captured in reference databases. Conclusions: Current methods can differentiate urban microbiome profiles from each other with relative ease. De-novo assembly indicated that the MetaSUB metagenomic data contains adequate depth to recover metagenomic assembled genomes and that current databases are not sufficient to fully characterize urban microbiomes. Profiles found here indicate there may be a relationship between geographical distance between areas and the urban microbiome composition although this will need further research. The impact of these different profiles on public health is currently unknown but the MetaSUB consortium is uniquely suited to evaluate these and provide a roadmap for the inclusion of urban microbiome information for city planning and public health policy. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer Nature en
dc.rights © 2019, The Author. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Microbiome en
dc.subject Machine learning en
dc.subject Public health en
dc.subject Urban en
dc.subject Bioinformatics en
dc.subject Microbiota en
dc.title Application of machine learning techniques for creating urban microbial fingerprints en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Fergal J. Ryan, APC Microbiome Ireland, University College Cork, Cork, Ireland. +353-21-490-3000 en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Biology Direct en
dc.identifier.articleid 13 en
dc.identifier.eissn 1745-6150


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© 2019, The Author. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Except where otherwise noted, this item's license is described as © 2019, The Author. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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