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.contributor.funder	Science Foundation Ireland
dc.date.accessioned	2018-05-02T10:16:22Z
dc.date.available	2018-05-02T10:16:22Z
dc.date.issued	2018
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.description.status	Peer reviewed	en
dc.description.version	Published Version	en
dc.format.mimetype	application/pdf	en
dc.identifier.articleid	50
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.doi	10.1186/s40168-018-0437-0
dc.identifier.issn	2049-2618
dc.identifier.journaltitle	Microbiome	en
dc.identifier.uri	https://hdl.handle.net/10468/5931
dc.identifier.volume	6
dc.language.iso	en	en
dc.publisher	BioMed Central Ltd.	en
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/
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

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

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