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VirHunter: A Deep Learning-Based Method for Detection of Novel RNA Viruses in Plant Sequencing Data

hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
dc.contributor.authorSUKHORUKOV, Grigorii
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorKHALILI, Maryam
hal.structure.identifierInstitut de Systématique, Evolution, Biodiversité [ISYEB ]
dc.contributor.authorGASCUEL, Olivier
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorCANDRESSE, Thierry
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorMARAIS, Armelle
hal.structure.identifierInstitut de biochimie et génétique cellulaires [IBGC]
hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
dc.contributor.authorNIKOLSKI, Macha
dc.date.issued2022-05-13
dc.description.abstractEnHigh-throughput sequencing has provided the capacity of broad virus detection for both known and unknown viruses in a variety of hosts and habitats. It has been successfully applied for novel virus discovery in many agricultural crops, leading to the current drive to apply this technology routinely for plant health diagnostics. For this, efficient and precise methods for sequencing-based virus detection and discovery are essential. However, both existing alignment-based methods relying on reference databases and even more recent machine learning approaches are not efficient enough in detecting unknown viruses in RNAseq datasets of plant viromes. We present VirHunter, a deep learning convolutional neural network approach, to detect novel and known viruses in assemblies of sequencing datasets. While our method is generally applicable to a variety of viruses, here, we trained and evaluated it specifically for RNA viruses by reinforcing the coding sequences’ content in the training dataset. Trained on the NCBI plant viruses data for three different host species (peach, grapevine, and sugar beet), VirHunter outperformed the state-of-the-art method, DeepVirFinder, for the detection of novel viruses, both in the synthetic leave-out setting and on the 12 newly acquired RNAseq datasets. Compared with the traditional tBLASTx approach, VirHunter has consistently exhibited better results in the majority of leave-out experiments. In conclusion, we have shown that VirHunter can be used to streamline the analyses of plant HTS-acquired viromes and is particularly well suited for the detection of novel viral contigs, in RNAseq datasets.
dc.language.isoen
dc.publisherFrontiers Media
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.subject.ennovel virus detection
dc.subject.enRNA viruses
dc.subject.enplant virome
dc.subject.enalignment-free method
dc.subject.endeep learning
dc.subject.enartificial neural network
dc.title.enVirHunter: A Deep Learning-Based Method for Detection of Novel RNA Viruses in Plant Sequencing Data
dc.typeArticle de revue
dc.identifier.doi10.3389/fbinf.2022.867111
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]
dc.subject.halSciences du Vivant [q-bio]
bordeaux.journalFrontiers in Bioinformatics
bordeaux.page867111
bordeaux.volume2
bordeaux.peerReviewedoui
hal.identifierhal-03671482
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03671482v1
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