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Semi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection

hal.structure.identifierUniversité de Liège - Gembloux
dc.contributor.authorTAMISIER, Lucie
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.identifierUniversité de Liège - Gembloux
dc.contributor.authorFOUILLIEN, Nicolas
hal.structure.identifierUniversity of California [Davis] [UC Davis]
dc.contributor.authorAL RWAHNIH, Maher
hal.structure.identifierUniversity of Sütçü Imam
dc.contributor.authorBUZKAN, Nihal
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorCANDRESSE, Thierry
hal.structure.identifierCNR Istituto per la Protezione Sostenibile delle Piante [Torino, Italia] [IPSP]
dc.contributor.authorCHIUMENTI, Michela
hal.structure.identifierResearch Institute for Agricultural, Fisheries and Food [ILVO]
dc.contributor.authorDE JONGHE, Kris
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorLEFEBVRE, Marie
hal.structure.identifierLeibniz-Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH / Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures [DSMZ]
dc.contributor.authorMARGARIA, Paolo
hal.structure.identifierAgroscope
dc.contributor.authorSÉBASTIEN REYNARD, Jean
hal.structure.identifierUniversity of California [Davis] [UC Davis]
dc.contributor.authorSTEVENS, Kristian
hal.structure.identifierNational Institute of Biology [Ljubljana] [NIB]
dc.contributor.authorKUTNJAK, Denis
hal.structure.identifierUniversité de Liège - Gembloux
dc.contributor.authorMASSART, Sébastien
dc.date.issued2021-04-02
dc.description.abstractEnThe widespread use of High-Throughput Sequencing (HTS) for detection of plant viruses and sequencing of plant virus genomes has led to the generation of large amounts of data and of bioinformatics challenges to process them. Many bioinformatics pipelines for virus detection are available, making the choice of a suitable one difficult. A robust benchmarking is needed for the unbiased comparison of the pipelines, but there is currently a lack of reference datasets that could be used for this purpose. We present 7 semi-artificial datasets composed of real RNA-seq datasets from virus-infected plants spiked with artificial virus reads. Each dataset addresses challenges that could prevent virus detection. We also present 3 real datasets showing a challenging virus composition as well as 8 completely artificial datasets to test haplotype reconstruction software. With these datasets that address several diagnostic challenges, we hope to encourage virologists, diagnosticians and bioinformaticians to evaluate and benchmark their pipeline(s).
dc.language.isoen
dc.subject.enHigh-Throughput Sequencing
dc.subject.enReference data
dc.subject.enSemi-artificial dataset
dc.subject.enPlant virus detection
dc.subject.enBioinformatics pipelines
dc.subject.enHaplotype reconstruction
dc.title.enSemi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection
dc.typeDocument de travail - Pré-publication
dc.typePrepublication/Preprint
dc.identifier.doi10.5281/zenodo.4273791
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale/Phytopathologie et phytopharmacie
hal.identifierhal-03369187
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03369187v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2021-04-02&rft.au=TAMISIER,%20Lucie&HAEGEMAN,%20Annelies&FOUCART,%20Yoika&FOUILLIEN,%20Nicolas&AL%20RWAHNIH,%20Maher&rft.genre=preprint&unknown


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