Comparing apples and oranges?: Next generation sequencing and its impact on microbiome analysis

dc.contributor.authorClooney, Adam G.
dc.contributor.authorFouhy, Fiona
dc.contributor.authorSleator, Roy D.
dc.contributor.authorO'Driscoll, Aisling
dc.contributor.authorStanton, Catherine
dc.contributor.authorCotter, Paul D.
dc.contributor.authorClaesson, Marcus J.
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Commissionen
dc.contributor.funderSeventh Framework Programmeen
dc.description.abstractRapid advancements in sequencing technologies along with falling costs present widespread opportunities for microbiome studies across a vast and diverse array of environments. These impressive technological developments have been accompanied by a considerable growth in the number of methodological variables, including sampling, storage, DNA extraction, primer pairs, sequencing technology, chemistry version, read length, insert size, and analysis pipelines, amongst others. This increase in variability threatens to compromise both the reproducibility and the comparability of studies conducted. Here we perform the first reported study comparing both amplicon and shotgun sequencing for the three leading next-generation sequencing technologies. These were applied to six human stool samples using Illumina HiSeq, MiSeq and Ion PGM shotgun sequencing, as well as amplicon sequencing across two variable 16S rRNA gene regions. Notably, we found that the factor responsible for the greatest variance in microbiota composition was the chosen methodology rather than the natural inter-individual variance, which is commonly one of the most significant drivers in microbiome studies. Amplicon sequencing suffered from this to a large extent, and this issue was particularly apparent when the 16S rRNA V1-V2 region amplicons were sequenced with MiSeq. Somewhat surprisingly, the choice of taxonomic binning software for shotgun sequences proved to be of crucial importance with even greater discriminatory power than sequencing technology and choice of amplicon. Optimal N50 assembly values for the HiSeq was obtained for 10 million reads per sample, whereas the applied MiSeq and PGM sequencing depths proved less sufficient for shotgun sequencing of stool samples. The latter technologies, on the other hand, provide a better basis for functional gene categorisation, possibly due to their longer read lengths. Hence, in addition to highlighting methodological biases, this study demonstrates the risks associated with comparing data generated using different strategies. We also recommend that laboratories with particular interests in certain microbes should optimise their protocols to accurately detect these taxa using different techniques.en
dc.description.sponsorshipScience Foundation Ireland (SFI under Grant Number SFI/12/RC/2273 and 11/PI/1137)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.identifier.citationClooney, A. G., Fouhy, F., Sleator, R. D., O’ Driscoll, A., Stanton, C., Cotter, P. D. and Claesson, M. J. (2016) 'Comparing Apples and Oranges?: Next Generation Sequencing and Its Impact on Microbiome Analysis', PLOS ONE, 11(2), pp. e0148028. doi:10.1371/journal.pone.0148028en
dc.identifier.journaltitlePlos Oneen
dc.publisherPublic Library of Scienceen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::HEALTH/603038/EU/Cystic Fibrosis Microbiome-determined Antibiotic Therapy Trial in Exacerbations: Results Stratified./CFMATTERSen
dc.rights© 2016 The Authors. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.en
dc.subjectShotgun sequencingen
dc.subjectRibosomal RNAen
dc.subjectSequence assembly toolsen
dc.subjectGene sequencingen
dc.subjectPolymerase chain reactionen
dc.subjectMicrobial taxonomyen
dc.titleComparing apples and oranges?: Next generation sequencing and its impact on microbiome analysisen
dc.typeArticle (peer-reviewed)en
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