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

hal.structure.identifierGembloux Agro-Bio Tech [Faculté universitaire des sciences agronomiques de Gembloux] [[FUSAGx]]
dc.contributor.authorTAMISIER, Lucie
dc.contributor.authorHAEGEMAN, Annelies
dc.contributor.authorFOUCART, Yoika
dc.contributor.authorFOUILLIEN, Nicolas
dc.contributor.authorAL RWAHNIH, Maher
dc.contributor.authorBUZKAN, Nihal
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorCANDRESSE, Thierry
dc.contributor.authorCHIUMENTI, Michela
dc.contributor.authorDE JONGHE, Kris
dc.contributor.authorLEFEBVRE, Marie
dc.contributor.authorMARGARIA, Paolo
dc.contributor.authorREYNARD, Jean Sébastien
dc.contributor.authorSTEVENS, Kristian
dc.contributor.authorKUTNJAK, Denis
dc.contributor.authorMASSART, Sébastien
dc.date.accessioned2025-01-08T03:06:20Z
dc.date.available2025-01-08T03:06:20Z
dc.date.issued2021
dc.identifier.issn2804-3871
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/204175
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.publisherPeer Community In
dc.subject.enHTS
dc.subject.enBioinformatics
dc.subject.enPlant virus detection
dc.subject.enBenchmark
dc.title.enSemi-artificial datasets as a resource for validation of bioinformatics pipelines for plant virus detection
dc.typeArticle de revue
dc.identifier.doi10.24072/pcjournal.62
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalPeer Community Journal
bordeaux.pagee53
bordeaux.volume1
bordeaux.hal.laboratoriesBiologie du Fruit & Pathologie (BFP) - UMR 1332*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionINRAE
bordeaux.peerReviewedoui
hal.identifierhal-04107125
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04107125v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Peer%20Community%20Journal&rft.date=2021&rft.volume=1&rft.spage=e53&rft.epage=e53&rft.eissn=2804-3871&rft.issn=2804-3871&rft.au=TAMISIER,%20Lucie&HAEGEMAN,%20Annelies&FOUCART,%20Yoika&FOUILLIEN,%20Nicolas&AL%20RWAHNIH,%20Maher&rft.genre=article


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