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dc.contributor.authorMäki, Anita
dc.contributor.authorSalmi, Pauliina
dc.contributor.authorMikkonen, Anu
dc.contributor.authorKremp, Anke
dc.contributor.authorTiirola, Marja
dc.date.accessioned2017-10-24T09:14:43Z
dc.date.available2017-10-24T09:14:43Z
dc.date.issued2017
dc.identifier.citationMäki, A., Salmi, P., Mikkonen, A., Kremp, A., & Tiirola, M. (2017). Sample Preservation, DNA or RNA Extraction and Data Analysis for High-Throughput Phytoplankton Community Sequencing. <i>Frontiers in Microbiology</i>, <i>8</i>, Article 1848. <a href="https://doi.org/10.3389/fmicb.2017.01848" target="_blank">https://doi.org/10.3389/fmicb.2017.01848</a>
dc.identifier.otherCONVID_27289313
dc.identifier.otherTUTKAID_75356
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/55676
dc.description.abstractPhytoplankton is the basis for aquatic food webs and mirrors the water quality. Conventionally, phytoplankton analysis has been done using time consuming and partly subjective microscopic observations, but next generation sequencing (NGS) technologies provide promising potential for rapid automated examination of environmental samples. Because many phytoplankton species have tough cell walls, methods for cell lysis and DNA or RNA isolation need to be efficient to allow unbiased nucleic acid retrieval. Here, we analyzed how two phytoplankton preservation methods, three commercial DNA extraction kits and their improvements, three RNA extraction methods, and two data analysis procedures affected the results of the NGS analysis. A mock community was pooled from phytoplankton species with variation in nucleus size and cell wall hardness. Although the study showed potential for studying Lugol-preserved sample collections, it demonstrated critical challenges in the DNA-based phytoplankton analysis in overall. The 18S rRNA gene sequencing output was highly affected by the variation in the rRNA gene copy numbers per cell, while sample preservation and nucleic acid extraction methods formed another source of variation. At the top, sequence-specific variation in the data quality introduced unexpected bioinformatics bias when the sliding-window method was used for the quality trimming of the Ion Torrent data. While DNA-based analyses did not correlate with biomasses or cell numbers of the mock community, rRNA-based analyses were less affected by different RNA extraction procedures and had better match with the biomasses, dry weight and carbon contents, and are therefore recommended for quantitative phytoplankton analyses.
dc.language.isoeng
dc.publisherFrontiers Research Foundation
dc.relation.ispartofseriesFrontiers in Microbiology
dc.subject.othernext generation sequencing
dc.subject.otherphytoplankton
dc.subject.othercell lysis
dc.subject.otheroperational taxonomic units
dc.subject.otherLugol
dc.titleSample Preservation, DNA or RNA Extraction and Data Analysis for High-Throughput Phytoplankton Community Sequencing
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201710164008
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.contributor.oppiaineAkvaattiset tieteetfi
dc.contributor.oppiaineYmpäristötiedefi
dc.contributor.oppiaineAquatic Sciencesen
dc.contributor.oppiaineEnvironmental Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2017-10-16T15:15:05Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.relation.issn1664-302X
dc.relation.numberinseries0
dc.relation.volume8
dc.type.versionpublishedVersion
dc.rights.copyright© 2017 Mäki, Salmi, Mikkonen, Kremp and Tiirola. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber615146
dc.relation.grantnumber615146
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/615146/EU//
dc.subject.ysoplankton
dc.subject.ysoDNA-analyysi
jyx.subject.urihttp://www.yso.fi/onto/yso/p3053
jyx.subject.urihttp://www.yso.fi/onto/yso/p25695
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3389/fmicb.2017.01848
dc.relation.funderEuroopan komissiofi
dc.relation.funderEuropean Commissionen
jyx.fundingprogramEU:n 7. puiteohjelma (FP7)fi
jyx.fundingprogramFP7 (EU's 7th Framework Programme)en
jyx.fundinginformationThe study was supported by the funding from the Academy of Finland (grant 260797) and European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP/2007-2013, grant agreement No. 615146) both awarded to MT, and Academy of Finland grant 251564 supported the contribution of AK.


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© 2017 Mäki, Salmi, Mikkonen, Kremp and Tiirola. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY).
Except where otherwise noted, this item's license is described as © 2017 Mäki, Salmi, Mikkonen, Kremp and Tiirola. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).