Frontiers in Microbiology (May 2023)

Managing the deluge of newly discovered plant viruses and viroids: an optimized scientific and regulatory framework for their characterization and risk analysis

  • Nuria Fontdevila Pareta,
  • Maryam Khalili,
  • Maryam Khalili,
  • Ayoub Maachi,
  • Mark Paul S. Rivarez,
  • Mark Paul S. Rivarez,
  • Johan Rollin,
  • Johan Rollin,
  • Ferran Salavert,
  • Coline Temple,
  • Miguel A. Aranda,
  • Neil Boonham,
  • Marleen Botermans,
  • Thierry Candresse,
  • Adrian Fox,
  • Adrian Fox,
  • Yolanda Hernando,
  • Denis Kutnjak,
  • Armelle Marais,
  • Françoise Petter,
  • Maja Ravnikar,
  • Ilhem Selmi,
  • Rachid Tahzima,
  • Rachid Tahzima,
  • Charlotte Trontin,
  • Thierry Wetzel,
  • Sebastien Massart,
  • Sebastien Massart

DOI
https://doi.org/10.3389/fmicb.2023.1181562
Journal volume & issue
Vol. 14

Abstract

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The advances in high-throughput sequencing (HTS) technologies and bioinformatic tools have provided new opportunities for virus and viroid discovery and diagnostics. Hence, new sequences of viral origin are being discovered and published at a previously unseen rate. Therefore, a collective effort was undertaken to write and propose a framework for prioritizing the biological characterization steps needed after discovering a new plant virus to evaluate its impact at different levels. Even though the proposed approach was widely used, a revision of these guidelines was prepared to consider virus discovery and characterization trends and integrate novel approaches and tools recently published or under development. This updated framework is more adapted to the current rate of virus discovery and provides an improved prioritization for filling knowledge and data gaps. It consists of four distinct steps adapted to include a multi-stakeholder feedback loop. Key improvements include better prioritization and organization of the various steps, earlier data sharing among researchers and involved stakeholders, public database screening, and exploitation of genomic information to predict biological properties.

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