Virus detection by high-throughput sequencing of small RNAs: large-scale performance testing of sequence analysis strategies

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cg.authorship.typesCGIAR single centreen
cg.contributor.affiliationUniversity of Liègeen
cg.contributor.affiliationInstitute for Agricultural and Fisheries Research, Belgiumen
cg.contributor.affiliationFood and Environment Research Agency, United Kingdomen
cg.contributor.affiliationCzech Academy of Sciencesen
cg.contributor.affiliationNational Institute of Biology, Sloveniaen
cg.contributor.affiliationAristotle University of Thessalonikien
cg.contributor.affiliationAgricultural Research Council, South Africaen
cg.contributor.affiliationWalloon Agricultural Research Centeren
cg.contributor.affiliationInstituto Valenciano de Investigaciones Agrariasen
cg.contributor.affiliationInstitut National de la Recherche Agronomique, Franceen
cg.contributor.affiliationUniversity of Baselen
cg.contributor.affiliationSwiss Federal Research Station Agroscopeen
cg.contributor.affiliationPalacký University Olomoucen
cg.contributor.affiliationNatural Resources Institute, Finlanden
cg.contributor.affiliationInternational Potato Centeren
cg.contributor.donorEuropean Unionen
cg.creator.identifierSebastien Massart: 0000-0002-7153-737X
cg.creator.identifierMichela Chiumenti: 000-0002-8412-3037
cg.creator.identifierKris De Jonghe: 0000-0003-1763-5654
cg.creator.identifierRachel Glover: 0000-0001-6526-8954
cg.creator.identifierAnnelies Haegeman: 0000-0002-8192-5368
cg.creator.identifierIgor Koloniuk: 0000-0002-5893-6683
cg.creator.identifierJan Kreuze: 0000-0002-6116-9200
cg.creator.identifierDenis Kutnjak: 0000-0002-5327-0587
cg.creator.identifierLeonidas Lotos: 0000-0003-0652-1790
cg.creator.identifierVarvara Maliogka: 0000-0001-5714-2710
cg.creator.identifierHans J Maree: 0000-0001-9639-4558
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1094/phyto-02-18-0067-ren
cg.isijournalISI Journalen
cg.issn0031-949Xen
cg.issue3en
cg.journalPhytopathologyen
cg.reviewStatusPeer Reviewen
cg.subject.cipPOTATOESen
cg.subject.cipPOTATO AGRI-FOOD SYSTEMSen
cg.subject.cipCROP PROTECTIONen
cg.volume109en
dc.contributor.authorMassart, S.en
dc.contributor.authorChiumenti, M.en
dc.contributor.authorJonghe, K.en
dc.contributor.authorGlover, R.en
dc.contributor.authorHaegeman, A.en
dc.contributor.authorKoloniuk, I.en
dc.contributor.authorKominek, P.en
dc.contributor.authorKreuze, Jan F.en
dc.contributor.authorKutnjak, D.en
dc.contributor.authorLotos, L.en
dc.contributor.authorMaclot, F.en
dc.contributor.authorMaliogka, V.en
dc.contributor.authorMaree, Hans J.en
dc.date.accessioned2019-04-23T21:20:08Zen
dc.date.available2019-04-23T21:20:08Zen
dc.identifier.urihttps://hdl.handle.net/10568/100881
dc.titleVirus detection by high-throughput sequencing of small RNAs: large-scale performance testing of sequence analysis strategiesen
dcterms.abstractRecent developments in high-throughput sequencing (HTS), also called next-generation sequencing (NGS), technologies and bioinformatics have drastically changed research on viral pathogens and spurred growing interest in the field of virus diagnostics. However, the reliability of HTS-based virus detection protocols must be evaluated before adopting them for diagnostics. Many different bioinformatics algorithms aimed at detecting viruses in HTS data have been reported but little attention has been paid thus far to their sensitivity and reliability for diagnostic purposes. Therefore, we compared the ability of 21 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 12 plant viruses through a double-blind large-scale performance test using 10 datasets of 21- to 24-nucleotide small RNA (sRNA) sequences from three different infected plants. The sensitivity of virus detection ranged between 35 and 100% among participants, with a marked negative effect when sequence depth decreased. The false-positive detection rate was very low and mainly related to the identification of host genome-integrated viral sequences or misinterpretation of the results. Reproducibility was high (91.6%). This work revealed the key influence of bioinformatics strategies for the sensitive detection of viruses in HTS sRNA datasets and, more specifically (i) the difficulty in detecting viral agents when they are novel or their sRNA abundance is low, (ii) the influence of key parameters at both assembly and annotation steps, (iii) the importance of completeness of reference sequence databases, and (iv) the significant level of scientific expertise needed when interpreting pipeline results. Overall, this work underlines key parameters and proposes recommendations for reliable sRNA-based detection of known and unknown viruses.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.audienceAcademicsen
dcterms.audienceCGIARen
dcterms.bibliographicCitationMassart, S.; Chiumenti, M.; Jonghe, K.; Glover, R.; Haegeman, A.; Koloniuk, I.; Kominek, P.; Kreuze, J.F.; Kutnjak, D.; Lotos, L.; Maclot, F.; Maliogka, V.; Maree, Hans J.; Koloniuk, I. 2019. Virus detection by high-throughput sequencing of small RNAs: large-scale performance testing of sequence analysis strategies. Phytopathology. ISSN 0031-949X. 109:3. pp. 488-497.en
dcterms.extent488-497en
dcterms.issued2019-03
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherScientific Societiesen
dcterms.subjectdiagnosisen
dcterms.subjectviroidsen
dcterms.subjectpotatoesen
dcterms.subjectvirusesen
dcterms.subjectrnaen
dcterms.typeJournal Article

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