Species classifier choice is a key consideration when analysing low-complexity food microbiome data

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dc.contributor.author Walsh, Aaron M.
dc.contributor.author Crispie, Fiona
dc.contributor.author O'Sullivan, Orla
dc.contributor.author Finnegan, Laura
dc.contributor.author Claesson, Marcus J.
dc.contributor.author Cotter, Paul D.
dc.date.accessioned 2018-05-02T10:16:22Z
dc.date.available 2018-05-02T10:16:22Z
dc.date.issued 2018
dc.identifier.citation Walsh, A. M., Crispie, F., O’Sullivan, O., Finnegan, L., Claesson, M. J. and Cotter, P. D. (2018) 'Species classifier choice is a key consideration when analysing low-complexity food microbiome data', Microbiome, 6(1), 50 (15pp). doi: 10.1186/s40168-018-0437-0 en
dc.identifier.volume 6
dc.identifier.issn 2049-2618
dc.identifier.uri http://hdl.handle.net/10468/5931
dc.identifier.doi 10.1186/s40168-018-0437-0
dc.description.abstract Background: The use of shotgun metagenomics to analyse low-complexity microbial communities in foods has the potential to be of considerable fundamental and applied value. However, there is currently no consensus with respect to choice of species classification tool, platform, or sequencing depth. Here, we benchmarked the performances of three high-throughput short-read sequencing platforms, the Illumina MiSeq, NextSeq 500, and Ion Proton, for shotgun metagenomics of food microbiota. Briefly, we sequenced six kefir DNA samples and a mock community DNA sample, the latter constructed by evenly mixing genomic DNA from 13 food-related bacterial species. A variety of bioinformatic tools were used to analyse the data generated, and the effects of sequencing depth on these analyses were tested by randomly subsampling reads. Results: Compositional analysis results were consistent between the platforms at divergent sequencing depths. However, we observed pronounced differences in the predictions from species classification tools. Indeed, PERMANOVA indicated that there was no significant differences between the compositional results generated by the different sequencers (p = 0.693, R-2 = 0.011), but there was a significant difference between the results predicted by the species classifiers (p = 0.01, R-2 = 0.127). The relative abundances predicted by the classifiers, apart from MetaPhlAn2, were apparently biased by reference genome sizes. Additionally, we observed varying false-positive rates among the classifiers. MetaPhlAn2 had the lowest false-positive rate, whereas SLIMM had the greatest false-positive rate. Strain-level analysis results were also similar across platforms. Each platform correctly identified the strains present in the mock community, but accuracy was improved slightly with greater sequencing depth. Notably, PanPhlAn detected the dominant strains in each kefir sample above 500,000 reads per sample. Again, the outputs from functional profiling analysis using SUPER-FOCUS were generally accordant between the platforms at different sequencing depths. Finally, and expectedly, metagenome assembly completeness was significantly lower on the MiSeq than either on the NextSeq (p = 0.03) or the Proton (p = 0.011), and it improved with increased sequencing depth. Conclusions: Our results demonstrate a remarkable similarity in the results generated by the three sequencing platforms at different sequencing depths, and, in fact, the choice of bioinformatics methodology had a more evident impact on results than the choice of sequencer did. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher BioMed Central Ltd. en
dc.relation.uri https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-018-0437-0
dc.rights © 2018, the Author(s). Open Access 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/
dc.subject Shotgun metagenomics en
dc.subject Sequencing platform comparison en
dc.subject Low-complexity microbiome en
dc.title Species classifier choice is a key consideration when analysing low-complexity food microbiome data en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Marcus Claesson, Microbiology, University College Cork, Cork, Ireland. +353-21-490-3000 Email: m.claesson@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Science Foundation Ireland
dc.description.status Peer reviewed en
dc.identifier.journaltitle Microbiome en
dc.internal.IRISemailaddress m.claesson@ucc.ie en
dc.identifier.articleid 50
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2273/IE/Alimentary Pharmabiotic Centre (APC) - Interfacing Food & Medicine/
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Starting Investigator Research Grant (SIRG)/13/SIRG/2160/IE/Investigating the impact of high intensity exercise and/or protein intake levels on gut microbial diversity./
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Principal Investigator Programme (PI)/11/PI/1137/IE/Obesibiotics/


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© 2018, the Author(s). Open Access 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 © 2018, the Author(s). Open Access 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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