1887

Abstract

RNA-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.

Loading

Article metrics loading...

/content/journal/jgv/10.1099/jgv.0.001210
2019-01-24
2019-10-19
Loading full text...

Full text loading...

/deliver/fulltext/jgv/100/3/533.html?itemId=/content/journal/jgv/10.1099/jgv.0.001210&mimeType=html&fmt=ahah

References

  1. Nicaise V. Crop immunity against viruses: outcomes and future challenges. Front Plant Sci 2014;5:1–18 [CrossRef][PubMed]
    [Google Scholar]
  2. Valkonen JPT. Viruses: economical losses and biotechnological potential. Potato Biology and Biotechnology: Advances and Perspectives Elsevier; 2007; pp.619–642
    [Google Scholar]
  3. Sasaya T, Nakazono-Nagaoka E, Saika H, Aoki H, Hiraguri A et al. Transgenic strategies to confer resistance against viruses in rice plants. Front Microbiol 2014;4:1–11 [CrossRef][PubMed]
    [Google Scholar]
  4. Lamichhane JR, Venturi V. Synergisms between microbial pathogens in plant disease complexes: a growing trend. Front Plant Sci 2015;6:1–12 [CrossRef][PubMed]
    [Google Scholar]
  5. Martin RR, Macfarlane S, Sabanadzovic S, Quito D, Poudel B et al. Blackberry Yellow Vein Disease (BYVD) Complex and Associated Viruses. Plant Dis 2013;97:168–182
    [Google Scholar]
  6. Mumford R, Boonham N, Tomlinson J, Barker I. Advances in molecular phytodiagnostics–new solutions for old problems. Eur J Plant Pathol 2006;116:1–19 [CrossRef]
    [Google Scholar]
  7. Boonham N, Kreuze J, Winter S, van der Vlugt R, Bergervoet J et al. Methods in virus diagnostics: from ELISA to next generation sequencing. Virus Res 2014;186:20–31 [CrossRef][PubMed]
    [Google Scholar]
  8. Wu Q, Luo Y, Lu R, Lau N, Lai EC et al. Virus discovery by deep sequencing and assembly of virus-derived small silencing RNAs. Proc Natl Acad Sci USA 2010;107:1606–1611 [CrossRef][PubMed]
    [Google Scholar]
  9. Jones S, Baizan-Edge A, Macfarlane S, Torrance L. Viral diagnostics in plants using next generation sequencing: computational analysis in practice. Front Plant Sci 2017;8:1–10 [CrossRef][PubMed]
    [Google Scholar]
  10. Wilson MR, Naccache SN, Samayoa E, Biagtan M, Bashir H et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N Engl J Med 2014;370:2408–2417 [CrossRef][PubMed]
    [Google Scholar]
  11. Flygare S, Simmon K, Miller C, Qiao Y, Kennedy B et al. Taxonomer: an interactive metagenomics analysis portal for universal pathogen detection and host mRNA expression profiling. Genome Biol 2016;17:111 [CrossRef][PubMed]
    [Google Scholar]
  12. Zheng Y, Gao S, Padmanabhan C, Li R, Galvez M et al. VirusDetect: an automated pipeline for efficient virus discovery using deep sequencing of small RNAs. Virology 2017;500:130–138 [CrossRef][PubMed]
    [Google Scholar]
  13. Trifonov V, Rabadan R. Frequency analysis techniques for identification of viral genetic data. mBio 2010;1:1–8 [CrossRef][PubMed]
    [Google Scholar]
  14. Dröge J, Gregor I, Mchardy AC. Taxator-tk: precise taxonomic assignment of metagenomes by fast approximation of evolutionary neighborhoods. Bioinformatics 2015;31:817–824 [CrossRef][PubMed]
    [Google Scholar]
  15. Malhotra S, Sowdhamini R. Genome-wide survey of DNA-binding proteins in Arabidopsis thaliana: analysis of distribution and functions. Nucleic Acids Res 2013;41:7212–7219 [CrossRef][PubMed]
    [Google Scholar]
  16. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014;15:R46 [CrossRef][PubMed]
    [Google Scholar]
  17. Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun 2016;7:1–9 [CrossRef][PubMed]
    [Google Scholar]
  18. Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods 2018;15:475–476 [CrossRef][PubMed]
    [Google Scholar]
  19. Afgan E, Baker D, van den Beek M, Blankenberg D, Bouvier D et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res 2016;44:gkw343 [CrossRef][PubMed]
    [Google Scholar]
  20. O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res 2016;44:D733–D745 [CrossRef][PubMed]
    [Google Scholar]
  21. Mihara T, Nishimura Y, Shimizu Y, Nishiyama H, Yoshikawa G et al. Linking Virus Genomes with Host Taxonomy. Viruses 2016;8:10–5 [CrossRef][PubMed]
    [Google Scholar]
  22. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–2120 [CrossRef][PubMed]
    [Google Scholar]
  23. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–1760 [CrossRef][PubMed]
    [Google Scholar]
  24. da Silva C, Zamperin G, Ferrarini A, Minio A, dal Molin A et al. The high polyphenol content of grapevine cultivar tannat berries is conferred primarily by genes that are not shared with the reference genome. Plant Cell 2013;25:4777–4788 [CrossRef][PubMed]
    [Google Scholar]
  25. Jo Y, Choi H, Cho JK, Yoon JY, Choi SK et al. In silico approach to reveal viral populations in grapevine cultivar Tannat using transcriptome data. Sci Rep 2015;5:15841 [CrossRef][PubMed]
    [Google Scholar]
  26. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 2011;29:644–652 [CrossRef][PubMed]
    [Google Scholar]
  27. Silvester N, Alako B, Amid C, Cerdeño-Tarrága A, Clarke L et al. The European nucleotide archive in 2017. Nucleic Acids Res 2018;46:D36–D40 [CrossRef][PubMed]
    [Google Scholar]
  28. Vanburen R, Bryant D, Bushakra JM, Vining KJ, Edger PP et al. The genome of black raspberry (Rubus occidentalis). Plant J 2016;87:535–547 [CrossRef][PubMed]
    [Google Scholar]
  29. Macfarlane S, Mcgavin W, Tzanetakis I. Virus Testing by PCR and RT-PCR Amplifi cation in Berry Fruit. In Lacomme C. (editor) Plant Pathology: Techniques and Protocols Springer; 2015; pp.227–248
    [Google Scholar]
  30. Alkowni R, Zhang YP, Rowhani A, Uyemoto JK, Minafra A. Biological, molecular, and serological studies of a novel strain of grapevine leafroll-associated virus 2. Virus Genes 2011;43:102–110 [CrossRef][PubMed]
    [Google Scholar]
  31. Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ et al. GenBank. Nucleic Acids Res 2013;41:D36–D42 [CrossRef]
    [Google Scholar]
  32. Goodacre N, Aljanahi A, Nandakumar S, Mikailov M, Khan AS. A Reference Viral Database (RVDB) To enhance bioinformatics analysis of high-throughput sequencing for novel virus detection. mSphere 2018;3:1–18 [CrossRef][PubMed]
    [Google Scholar]
  33. Martin RR, Polashock JJ, Tzanetakis IE. New and emerging viruses of blueberry and cranberry. Viruses 2012;4:2831–2852 [CrossRef][PubMed]
    [Google Scholar]
  34. Harrison BD. Relationship between beet ringspot, potato bouquet and tomato black ring viruses. J Gen Microbiol 1958;18:450–460 [CrossRef][PubMed]
    [Google Scholar]
  35. Kis S, Salamon P, Kis V, Szittya G. Molecular characterization of a beet ringspot nepovirus isolated from Begonia ricinifolia in Hungary. Arch Virol 2017;162:3559–3562 [CrossRef][PubMed]
    [Google Scholar]
  36. Niu X, Sun Y, Chen Z, Li R, Padmanabhan C et al. Using Small RNA-seq data to detect siRNA duplexes induced by plant viruses. Genes 2017;8:163–168 [CrossRef][PubMed]
    [Google Scholar]
  37. Barrero RA, Napier KR, Cunnington J, Liefting L, Keenan S et al. An internet-based bioinformatics toolkit for plant biosecurity diagnosis and surveillance of viruses and viroids. BMC Bioinformatics 2017;18:26 [CrossRef][PubMed]
    [Google Scholar]
  38. Federhen S. The NCBI taxonomy database. Nucleic Acids Res 2012;40:D136–D143 [CrossRef][PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/jgv/10.1099/jgv.0.001210
Loading
/content/journal/jgv/10.1099/jgv.0.001210
Loading

Data & Media loading...

Most Cited This Month

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error