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Revisiting high throughput sequencing data used for plant virus detection in order to find evidence of nonviralplant pathogens and pests

hal.structure.identifierResearch Institute for Agricultural, Fisheries and Food [ILVO]
dc.contributor.authorHAEGEMAN, Annelies
hal.structure.identifierResearch Institute for Agricultural, Fisheries and Food [ILVO]
dc.contributor.authorFOUCART, Yoika
hal.structure.identifierResearch Institute for Agricultural, Fisheries and Food [ILVO]
dc.contributor.authorDE JONGHE, Kris
hal.structure.identifierResearch Institute for Agricultural, Fisheries and Food [ILVO]
dc.contributor.authorGOEDEFROIT, Thomas
hal.structure.identifierUniversity of California [Davis] [UC Davis]
dc.contributor.authorAL RWAHNIH, Maher
hal.structure.identifierNewcastle University [Newcastle]
dc.contributor.authorBOONHAM, Neil
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorCANDRESSE, Thierry
hal.structure.identifierCanadian Food Inspection Agency [CFIA]
dc.contributor.authorGAAFAR, Yahya
hal.structure.identifierUnited States Department of Agriculture [USDA]
dc.contributor.authorHURTADO-GONZALES, Oscar
hal.structure.identifierNational Institute of Biology [Ljubljana] [NIB]
hal.structure.identifierJozef Stefan International Postgraduate School [Ljubljana, Slovenia]
dc.contributor.authorKOGEJ ZWITTER, Zala
hal.structure.identifierNational Institute of Biology [Ljubljana] [NIB]
dc.contributor.authorKUTNJAK, Denis
hal.structure.identifierAgricultural Institute of Slovenia
dc.contributor.authorLAMOVŠEK, Janja
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorLEFEBVRE, Marie
hal.structure.identifierUSDA Animal and Plant Health Inspection Service [USDA-APHIS]
dc.contributor.authorMALAPI, Martha
hal.structure.identifierAgricultural Institute of Slovenia
dc.contributor.authorMAVRIC PLESKO, Irena
hal.structure.identifierEskisehir Osmangazi University
dc.contributor.authorÖNDER, Serkan
hal.structure.identifierAgroscope
dc.contributor.authorREYNARD, Jean-Sébastien
hal.structure.identifierNewcastle University [Newcastle]
dc.contributor.authorSALAVERT PAMBLANCO, Ferran
hal.structure.identifierAgroscope
dc.contributor.authorSCHUMPP, Olivier
hal.structure.identifierUniversity of California [Davis] [UC Davis]
dc.contributor.authorSTEVENS, Kristian
hal.structure.identifierZespri International Limited
dc.contributor.authorPAL, Chandan
hal.structure.identifierGénétique et Amélioration des Fruits et Légumes [GAFL]
hal.structure.identifierUnité de Pathologie Végétale [PV]
dc.contributor.authorTAMISIER, Lucie
hal.structure.identifierNiğde Ömer Halisdemir University
dc.contributor.authorULUBAŞ SERÇE, Çiğdem
hal.structure.identifierDutch General Inspection Service
dc.contributor.authorVAN DUIVENBODE, Inge
hal.structure.identifierUniversity of Auckland [Auckland]
dc.contributor.authorWAITE, David
hal.structure.identifierUSDA Animal and Plant Health Inspection Service [USDA-APHIS]
dc.contributor.authorHU, Xiaojun
hal.structure.identifierJulius Kühn-Institut [JKI]
dc.contributor.authorZIEBELL, Heiko
hal.structure.identifierUnité de recherche TERRA [Gembloux]
dc.contributor.authorMASSART, Sébastien
dc.date.conference2023-08-20
dc.description.abstractEnHigh-throughput sequencing (HTS), more specifically RNA-seq of plant tissue, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases only, which lead to our hypothesis that they might be missing possible traces of other pathogens in the data. In this study, we set up a community effort to re-analyze existing RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests (>100 reads per million). The most observed organism categories were fungi (15/37 datasets), insects (13/37) and mites (9/37). Nematodes were not observed and only a few samples showed the presence of plant pathogenic phytoplasmas (1/37), bacteria (3/37) and oomycetes (4/37). In conclusion, we were able to show that it is possible to detect non-viral pathogens or pests in these metatranscriptomics datasets, in this case primarily fungi, insects and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (bacteriology, mycology, entomology) as well.
dc.language.isoen
dc.title.enRevisiting high throughput sequencing data used for plant virus detection in order to find evidence of nonviralplant pathogens and pests
dc.typeCommunication dans un congrès
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale/Phytopathologie et phytopharmacie
bordeaux.page1259-1260
bordeaux.conference.title12. International congress of plant pathology (ICPP). Satellite event: high-throughput Sequencing in plant virology: from Discovery to diagnostics
bordeaux.countryFR
bordeaux.conference.cityLyon
bordeaux.peerReviewednon
hal.identifierhal-05202673
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.organizerSFP
hal.conference.organizerISPP
hal.conference.end2023-08-20
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-05202673v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.spage=1259-1260&rft.epage=1259-1260&rft.au=HAEGEMAN,%20Annelies&FOUCART,%20Yoika&DE%20JONGHE,%20Kris&GOEDEFROIT,%20Thomas&AL%20RWAHNIH,%20Maher&rft.genre=unknown


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