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dc.contributor.authorBaizan-Edge, Amanda
dc.contributor.authorCock, Peter
dc.contributor.authorMacFarlane, Stuart
dc.contributor.authorMcGavin, Wendy
dc.contributor.authorTorrance, Lesley
dc.contributor.authorJones, Susan
dc.date.accessioned2019-03-12T13:30:08Z
dc.date.available2019-03-12T13:30:08Z
dc.date.issued2019-03-01
dc.identifier.citationBaizan-Edge , A , Cock , P , MacFarlane , S , McGavin , W , Torrance , L & Jones , S 2019 , ' Kodoja : a workflow for virus detection in plants using k-mer analysis of RNA-sequencing data ' , Journal of General Virology , vol. 100 , pp. 533-542 . https://doi.org/10.1099/jgv.0.001210en
dc.identifier.issn0022-1317
dc.identifier.otherPURE: 257678907
dc.identifier.otherPURE UUID: 83bad0ed-ed18-4ed1-bca4-c3511c315060
dc.identifier.otherPubMed: 30676315
dc.identifier.otherScopus: 85062389560
dc.identifier.otherWOS: 000460158100017
dc.identifier.urihttp://hdl.handle.net/10023/17261
dc.descriptionThis work was supported by the Biotechnology and Biological Sciences Research Council [BB/N023293/1]. The work of L.T., S.J., S.M. and P.C.was additionally supported by the Scottish Government’s Rural and Environment Science and Analytical Services division (RESAS)en
dc.description.abstractRNA-sequencing of plant material allows for hypothesis-free detection of multiple viruses simultaneously. This methodology relies on bioinformatics workflows for virus identification. Most workflows are designed for human clinical data, and few go beyond sequence mapping for virus identification. We present a new workflow (Kodoja) for the detection of plant virus sequences in RNA-sequence data. Kodoja uses k-mer profiling at the nucleotide level and sequence mapping at the protein level by integrating two existing tools Kraken and Kaiju. Kodoja was tested on three existing RNA-seq datasets from grapevine, and two new RNA-seq datasets from raspberry. For grapevine, Kodoja was shown to be more sensitive than a method based on contig building and blast alignments (27 viruses detected compared to 19). The application of Kodoja to raspberry, showed that field-grown raspberries were infected by multiple viruses, and that RNA-seq can identify lower amounts of virus material than reverse transcriptase PCR. This work enabled the design of new PCR-primers for detection of Raspberry yellow net virus and Beet ringspot virus. Kodoja is a sensitive method for plant virus discovery in field samples and enables the design of more accurate primers for detection. Kodoja is available to install through Bioconda and as a tool within Galaxy.
dc.language.isoeng
dc.relation.ispartofJournal of General Virologyen
dc.rights© 2019 The Authors | Published by the Microbiology Society. This work has been made available online in accordance with the publisher’s policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at: https://doi.org/10.1099/jgv.0.001210en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectQR355 Virologyen
dc.subjectDASen
dc.subject.lccQA75en
dc.subject.lccQH301en
dc.subject.lccQR355en
dc.titleKodoja : a workflow for virus detection in plants using k-mer analysis of RNA-sequencing dataen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Biomedical Sciences Research Complexen
dc.identifier.doihttps://doi.org/10.1099/jgv.0.001210
dc.description.statusPeer revieweden


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