# Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities

Justine Charon, Ingrida Olendraite, Marco Forgia, Li Chuin Chong, Luke S Hillary, Simon Roux, Anne Kupczok, Humberto Debat, Shoichi Sakaguchi, Rachid Tahzima, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Justine Charon, Ingrida Olendraite, Marco Forgia, Li Chuin Chong, Luke S Hillary, et al. Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities. Frontiers in Virology, 2024, 4, 10.3389/fviro.2024.1371958. [https://doi.org/10.3389/fviro.2024.1371958](https://doi.org/10.3389/fviro.2024.1371958). [hal-04612201](https://HAL.inrae.fr/hal-04612201)

HAL Id: hal-04612201

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Submitted on 14 Jun 2024

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<figure><img src="image_2.png" /></figure>
# Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities

Justine Charon, Ingrida Olendraite, Marco Forgia, Li Chuin Chong, Luke S Hillary, Simon Roux, Anne Kupczok, Humberto Debat, Shoichi Sakaguchi, Rachid Tahzima, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Justine Charon, Ingrida Olendraite, Marco Forgia, Li Chuin Chong, Luke S Hillary, et al. Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities. Frontiers in Virology, 2024, 4, 10.3389/fviro.2024.1371958. [https://doi.org/10.3389/fviro.2024.1371958](https://doi.org/10.3389/fviro.2024.1371958). [hal-04612201](https://HAL.inrae.fr/hal-04612201v1)

HAL Id: hal-04612201

https://hal.inrae.fr/hal-04612201v1

Submitted on 14 Jun 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
<figure><img src="image_3.png" /></figure>

Check for updates

## OPEN ACCESS

### EDITED BY

Jie Cui,
Fudan University, China

### REVIEWED BY

Akio Adachi,
Tokushima University, Japan

### *CORRESPONDENCE

Uri Neri

<figure><img src="image_4.png" /></figure>

uri.neri@gmail.com

RECEIVED 17 January 2024

ACCEPTED 15 February 2024

PUBLISHED 04 April 2024

### CITATION

Charon J, Olendraite I, Forgia M, Chong LC, Hillary LS, Roux S, Kupczok A, Debat H, Sakaguchi S, Tahzima R, Nakagawa S, Babaian A, Abroi A, Bejerman N, Ben Mansour K, Brown K, Butkovic A, Cervera A, Charriat F, Chen G, Chiba Y, De Coninck L, Demina T, Dominguez-Huerta G, Dubrulle J, Gutierrez S, Harvey E, Jayaraj Mallika FR, Karapliafis D, Lim SJ, Kasibhatla SM, Mifsud JCO, Nishimura Y, Ortiz-Baez AS, Raco M, Rivero R, Sadiq S, Saghaei S, San JE, Shaikh HM, Sieradzki ET, Sullivan MB, Sun Y, Wille M, Wolf YI, Zrelovs N and Neri U (2024) Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities.
*Front. Virol.* 4:1371958.
doi: 10.3389/fviro.2024.1371958

### COPYRIGHT

© 2024 Charon, Olendraite, Forgia, Chong, Hillary, Roux, Kupczok, Debat, Sakaguchi, Tahzima, Nakagawa, Babaian, Abroi, Bejerman, Ben Mansour, Brown, Butkovic, Cervera, Charriat, Chen, Chiba, De Coninck, Demina, Dominguez-Huerta, Dubrulle, Gutierrez, Harvey, Jayaraj Mallika, Karapliafis, Lim, Kasibhatla, Mifsud, Nishimura, Ortiz-Baez, Raco, Rivero, Sadiq, Saghaei, San, Shaikh, Sieradzki, Sullivan, Sun, Wille, Wolf, Zrelovs and Neri. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities

Justine Charon<fnref n="1" />, Ingrida Olendraite², Marco Forgia³,
Li Chuin Chong⁴, Luke S. Hillary⁵, Simon Roux⁶,
Anne Kupczok⁷, Humberto Debat⁸,⁹, Shoichi Sakaguchi¹⁰,
Rachid Tahzima¹¹,¹², So Nakagawa¹³, Artem Babaian¹⁴,¹⁵,
Aare Abroi¹⁶, Nicolas Bejerman⁸,⁹, Karima Ben Mansour¹⁷,¹⁸,
Katherine Brown², Anamarija Butkovic¹⁹, Amelia Cervera²⁰,
Florian Charriat²¹, Guowei Chen²², Yuto Chiba²³,²⁴,
Lander De Coninck²⁵, Tatiana Demina²⁶,
Guillermo Dominguez-Huerta²⁷, Jeremy Dubrulle²⁸,
Serafin Gutierrez²¹,²⁹, Erin Harvey³⁰,
Fhilmar Raj Jayaraj Mallika³¹, Dimitris Karapliafis⁷,
Shen Jean Lim³², Sunitha Manjari Kasibhatla³³,³⁴,
Jonathon C. O. Mifsud³⁰, Yosuke Nishimura²⁴,
Ayda Susana Ortiz-Baez³⁰, Milica Raco³⁵, Ricardo Rivero³⁶,
Sabrina Sadiq³⁰, Shahram Saghaei³⁷, James Emmanuel San³⁸,³⁹,
Hisham Mohammed Shaikh⁴⁰,⁴¹, Ella Tali Sieradzki⁴²,
Matthew B. Sullivan⁴³, Yanni Sun²², Michelle Wille⁴⁴,
Yuri I. Wolf⁴⁵, Nikita Zrelovs⁴⁶ and Uri Neri⁴⁷*

<footnote n="1">¹Fruit Biology and Pathology Unit, University of Bordeaux, INRAE, Bordeaux, France, ²Division of Virology, Department of Pathology, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom, ³Instituto per la Protezione Sostenibile delle Piante, CNR, Torino, Italy, ⁴Institute for Experimental Virology, TWINCORE Centre for Experimental and Clinical Infection Research, a Joint Venture Between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany, ⁵Department of Plant Pathology, University of California, Davis, Davis, CA, United States, ⁶DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, United States, ⁷Bioinformatics Group, Wageningen University, Wageningen, Netherlands, ⁸Instituto de Patología Vegetal, Centro de Investigaciones Agropecuarias, Instituto Nacional de Tecnología Agropecuaria (IPAVE, CIAP, INTA), Córdoba, Argentina, ⁹Unidad de Fitopatología y Modelización Agrícola, Consejo Nacional de Investigaciones Científicas y Técnicas, Córdoba, Argentina, ¹⁰Department of Microbiology and Infection Control, Faculty of Medicine, Osaka Medical and Pharmaceutical University, Osaka, Japan, ¹¹University of Liège, Gembloux AgroBioTech - TERRA, Gembloux, Belgium, ¹²Flanders Research Institute for Agriculture, Fisheries and Food, Plant Sciences Unit - Virology, Ghent, Belgium, ¹³Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Japan, ¹⁴Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, ¹⁵The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, ¹⁶Institute of Technology, University of Tartu, Tartu, Estonia, ¹⁷Ecology, Diagnostics and Genetic Resources of Agriculturally Important Viruses, Fungi and Phytoplasmas, Crop Research Institute, Prague, Czechia, ¹⁸Department of Plant Protection, Faculty of Agrobiology, Food and Natural Resources Czech University of Life Sciences, Prague, Czechia, ¹⁹Archaeal Virology Unit, Institut Pasteur, Université Paris Cité, CNRS, Paris, France, ²⁰Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas (CSIC) - Universitat Politècnica de València (UPV), València, Spain, ²¹ASTRE, CIRAD, INRAE, University of Montpellier, Montpellier, France, ²²Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China, ²³School of Agriculture, Meiji University, Kawasaki, Japan, ²⁴Research Centre for Bioscience and Nanoscience, Japan Agency for Marine-Earth Science and</footnote>
<figure><img src="image_3.png" /></figure>

Check for updates

## OPEN ACCESS

### EDITED BY

Jie Cui,
Fudan University, China

### REVIEWED BY

Akio Adachi,
Tokushima University, Japan

### *CORRESPONDENCE

URI Neri

<figure><img src="image_4.png" /></figure>

uri.neri@gmail.com

RECEIVED 17 January 2024

ACCEPTED 15 February 2024

PUBLISHED 04 April 2024

### CITATION

Charon J, Olendraite I, Forgia M, Chong LC, Hillary LS, Roux S, Kupczok A, Debat H, Sakaguchi S, Tahzima R, Nakagawa S, Babaian A, Abroi A, Bejerman N, Ben Mansour K, Brown K, Butkovic A, Cervera A, Charriat F, Chen G, Chiba Y, De Coninck L, Demina T, Dominguez-Huerta G, Dubrulle J, Gutierrez S, Harvey E, Jayaraj Mallika FR, Karaplafis D, Lim SJ, Kasibhatla SM, Mifsud JCO, Nishimura Y, Ortiz-Baez AS, Raco M, Rivero R, Sadiq S, Saghaei S, San JE, Shaikh HM, Sieradzki ET, Sullivan MB, Sun Y, Wille M, Wolf YI, Zrelovs N and Neri U (2024) Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities.
*Front. Virol.* 4:1371958.
doi: 10.3389/fviro.2024.1371958

### COPYRIGHT

© 2024 Charon, Olendraite, Forgia, Chong, Hillary, Roux, Kupczok, Debat, Sakaguchi, Tahzima, Nakagawa, Babaian, Abroi, Bejerman, Ben Mansour, Brown, Butkovic, Cervera, Charriat, Chen, Chiba, De Coninck, Demina, Dominguez-Huerta, Dubrulle, Gutierrez, Harvey, Jayaraj Mallika, Karaplafis, Lim, Kasibhatla, Mifsud, Nishimura, Ortiz-Baez, Raco, Rivero, Sadiq, Saghaei, San, Shaikh, Sieradzki, Sullivan, Sun, Wille, Wolf, Zrelovs and Neri. This is an open-access article distributed under the terms of the [Creative Commons Attribution License (CC BY)](https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Consensus statement from the first RdRp Summit: advancing RNA virus discovery at scale across communities

Justine Charon<fnref n="1" />, Ingrida Olendraite², Marco Forgia³,
Li Chuin Chong⁴, Luke S. Hillary⁵, Simon Roux⁶,
Anne Kupczok⁷, Humberto Debat⁸,⁹, Shoichi Sakaguchi¹⁰,
Rachid Tahzima¹¹,¹², So Nakagawa¹³, Artem Babaian¹⁴,¹⁵,
Aare Abroi¹⁶, Nicolas Bejerman⁸,⁹, Karima Ben Mansour¹⁷,¹⁸,
Katherine Brown², Anamarija Butkovic¹⁹, Amelia Cervera²⁰,
Florian Charriat²¹, Guowei Chen²², Yuto Chiba²³,²⁴,
Lander De Coninck²⁵, Tatiana Demina²⁶,
Guillermo Dominguez-Huerta²⁷, Jeremy Dubrulle²⁸,
Serafin Gutierrez²¹,²⁹, Erin Harvey³⁰,
Fhilmar Raj Jayaraj Mallika³¹, Dimitris Karaplafis⁷,
Shen Jean Lim³², Sunitha Manjari Kasibhatla³³,³⁴,
Jonathon C. O. Mifsud³⁰, Yosuke Nishimura²⁴,
Ayda Susana Ortiz-Baez³⁰, Milica Raco³⁵, Ricardo Rivero³⁶,
Sabrina Sadiq³⁰, Shahram Saghaei³⁷, James Emmanuel San³⁸,³⁹,
Hisham Mohammed Shaikh⁴⁰,⁴¹, Ella Tali Sieradzki⁴²,
Matthew B. Sullivan⁴³, Yanni Sun²², Michelle Wille⁴⁴,
Yuri I. Wolf⁴⁵, Nikita Zrelovs⁴⁶ and Uri Neri⁴⁷*

<footnote n="1">¹Fruit Biology and Pathology Unit, University of Bordeaux, INRAE, Bordeaux, France, ²Division of Virology, Department of Pathology, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom, ³Instituto per la Protezione Sostenibile delle Piante, CNR, Torino, Italy, ⁴Institute for Experimental Virology, TWINCORE Centre for Experimental and Clinical Infection Research, a Joint Venture Between the Hannover Medical School (MHH) and the Helmholtz Centre for Infection Research (HZI), Hannover, Germany, ⁵Department of Plant Pathology, University of California, Davis, Davis, CA, United States, ⁶DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, United States, ⁷Bioinformatics Group, Wageningen University, Wageningen, Netherlands, ⁸Instituto de Patología Vegetal, Centro de Investigaciones Agropecuarias, Instituto Nacional de Tecnología Agropecuaria (IPAVE, CIAP, INTA), Córdoba, Argentina, ⁹Unidad de Fitopatología y Modelización Agrícola, Consejo Nacional de Investigaciones Científicas y Técnicas, Córdoba, Argentina, ¹⁰Department of Microbiology and Infection Control, Faculty of Medicine, Osaka Medical and Pharmaceutical University, Osaka, Japan, ¹¹University of Liège, Gembloux AgroBioTech - TERRA, Gembloux, Belgium, ¹²Flanders Research Institute for Agriculture, Fisheries and Food, Plant Sciences Unit - Virology, Ghent, Belgium, ¹³Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Japan, ¹⁴Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, ¹⁵The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada, ¹⁶Institute of Technology, University of Tartu, Tartu, Estonia, ¹⁷Ecology, Diagnostics and Genetic Resources of Agriculturally Important Viruses, Fungi and Phytoplasmas, Crop Research Institute, Prague, Czechia, ¹⁸Department of Plant Protection, Faculty of Agrobiology, Food and Natural Resources Czech University of Life Sciences, Prague, Czechia, ¹⁹Archaeal Virology Unit, Institut Pasteur, Université Paris Cité, CNRS, Paris, France, ²⁰Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas (CSIC) - Universitat Politècnica de València (UPV), València, Spain, ²¹ASTRE, CIRAD, INRAE, University of Montpellier, Montpellier, France, ²²Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China, ²³School of Agriculture, Meiji University, Kawasaki, Japan, ²⁴Research Centre for Bioscience and Nanoscience, Japan Agency for Marine-Earth Science and</footnote>
Technology (JAMSTEC), Yokosuka, Japan, ²⁵KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Division of Clinical and Epidemiological Virology, Laboratory of Viral Metagenomics, Leuven, Belgium, ²⁶Department of Microbiology, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland, ²⁷Department of Oceanography and Global Change, Centro Oceanográfico de Málaga (IEO-CSIC), Fuengirola, Spain, ²⁸Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand, ²⁹Department of Virology, Montpellier University Hospital, Montpellier, France, ³⁰Sydney Institute for Infectious Diseases, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia, ³¹Molecular Virology Laboratory, ICAR-National Research Centre for Banana, Tiruchirappalli, Tamil Nadu, India, ³²College of Marine Science, University of South Florida, St Petersburg, FL, United States, ³³Bioinformatics Centre, SP Pune University, Pune, India, ³⁴High Performance Computing (HPC)-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing, Pune, India, ³⁵Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States, ³⁶Paul G. Allen School for Global Health, Washington State University, Pullman, WA, United States, ³⁷Bioinformatics/High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany, ³⁸KwaZulu-Natal Research and Innovation Sequencing Platform (KRISP), UKZN, Durban, South Africa, ³⁹School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa, ⁴⁰Research Department, Flanders Marine Institute (VLIZ), InnovOcean Site, Ostend, Belgium, ⁴¹Department of Microbiology and Biochemistry, Faculty of Sciences, Ghent University, Ghent, Belgium, ⁴²Laboratoire Ampère, École Centrale de Lyon, Écully, France, ⁴³Departments of Microbiology and Civil, Environmental, and Geodetic Engineering, and Center of Microbiome Science, Ohio State University, Columbus, OH, United States, ⁴⁴Centre for Pathogen Genomics, Department of Microbiology and Immunology, University of Melbourne, at the Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia, ⁴⁵National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States, ⁴⁶Latvian Biomedical Research and Study Centre, Riga, Latvia, ⁴⁷The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel

Improved RNA virus understanding is critical to studying animal and plant health, and environmental processes. However, the continuous and rapid RNA virus evolution makes their identification and characterization challenging. While recent sequence-based advances have led to extensive RNA virus discovery, there is growing variation in how RNA viruses are identified, analyzed, characterized, and reported. To this end, an RdRp Summit was organized and a hybrid meeting took place in Valencia, Spain in May 2023 to convene leading experts with emphasis on early career researchers (ECRs) across diverse scientific communities. Here we synthesize key insights and recommendations and offer these as a first effort to establish a consensus framework for advancing RNA virus discovery. First, we need interoperability through standardized methodologies, data-sharing protocols, metadata provision and interdisciplinary collaborations and offer specific examples as starting points. Second, as an emergent field, we recognize the need to incorporate cutting-edge technologies and knowledge early and often to improve omic-based viral detection and annotation as novel capabilities reveal new biology. Third, we underscore the significance of ECRs in fostering international partnerships to promote inclusivity and equity in virus discovery efforts. The proposed consensus framework serves as a roadmap for the scientific community to collectively contribute to the tremendous challenge of unveiling the RNA virosphere.

KEYWORDS

RNA virus discovery, viral metagenomics, RNA-dependent RNA polymerase, viral genome annotation, metagenomic metadata standards, virus evolution and diversity
Technology (JAMSTEC), Yokosuka, Japan, ²⁵KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Division of Clinical and Epidemiological Virology, Laboratory of Viral Metagenomics, Leuven, Belgium, ²⁶Department of Microbiology, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland, ²⁷Department of Oceanography and Global Change, Centro Oceanográfico de Málaga (IEO-CSIC), Fuengirola, Spain, ²⁸Department of Microbiology and Immunology, University of Otago, Dunedin, New Zealand, ²⁹Department of Virology, Montpellier University Hospital, Montpellier, France, ³⁰Sydney Institute for Infectious Diseases, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia, ³¹Molecular Virology Laboratory, ICAR-National Research Centre for Banana, Tiruchirappalli, Tamil Nadu, India, ³²College of Marine Science, University of South Florida, St Petersburg, FL, United States, ³³Bioinformatics Centre, SP Pune University, Pune, India, ³⁴High Performance Computing (HPC)-Medical & Bioinformatics Applications Group, Centre for Development of Advanced Computing, Pune, India, ³⁵Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, United States, ³⁶Paul G. Allen School for Global Health, Washington State University, Pullman, WA, United States, ³⁷Bioinformatics/High-Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany, ³⁸KwaZulu-Natal Research and Innovation Sequencing Platform (KRISP), UKZN, Durban, South Africa, ³⁹School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa, ⁴⁰Research Department, Flanders Marine Institute (VLIZ), InnovOcean Site, Ostend, Belgium, ⁴¹Department of Microbiology and Biochemistry, Faculty of Sciences, Ghent University, Ghent, Belgium, ⁴²Laboratoire Ampère, École Centrale de Lyon, Écully, France, ⁴³Departments of Microbiology and Civil, Environmental, and Geodetic Engineering, and Center of Microbiome Science, Ohio State University, Columbus, OH, United States, ⁴⁴Centre for Pathogen Genomics, Department of Microbiology and Immunology, University of Melbourne, at the Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia, ⁴⁵National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States, ⁴⁶Latvian Biomedical Research and Study Centre, Riga, Latvia, ⁴⁷The Shmunis School of Biomedicine and Cancer Research, Tel Aviv University, Tel Aviv, Israel

Improved RNA virus understanding is critical to studying animal and plant health, and environmental processes. However, the continuous and rapid RNA virus evolution makes their identification and characterization challenging. While recent sequence-based advances have led to extensive RNA virus discovery, there is growing variation in how RNA viruses are identified, analyzed, characterized, and reported. To this end, an RdRp Summit was organized and a hybrid meeting took place in Valencia, Spain in May 2023 to convene leading experts with emphasis on early career researchers (ECRs) across diverse scientific communities. Here we synthesize key insights and recommendations and offer these as a first effort to establish a consensus framework for advancing RNA virus discovery. First, we need interoperability through standardized methodologies, data-sharing protocols, metadata provision and interdisciplinary collaborations and offer specific examples as starting points. Second, as an emergent field, we recognize the need to incorporate cutting-edge technologies and knowledge early and often to improve omic-based viral detection and annotation as novel capabilities reveal new biology. Third, we underscore the significance of ECRs in fostering international partnerships to promote inclusivity and equity in virus discovery efforts. The proposed consensus framework serves as a roadmap for the scientific community to collectively contribute to the tremendous challenge of unveiling the RNA virosphere.

KEYWORDS

RNA virus discovery, viral metagenomics, RNA-dependent RNA polymerase, viral genome annotation, metagenomic metadata standards, virus evolution and diversity
# 1 Introduction

RNA viruses (Orthornavirae) are genetic elements with RNA-based genomes that replicate using their encoded RNA-dependent RNA polymerase (RdRp) and by hijacking their host's cellular machinery. Progeny viruses are then transmitted to new hosts either vertically, or horizontally most often in protein-based viral particles that can sometimes be surrounded by a lipid envelope. Viruses are widely diverse, infect all life forms (1), and include many human pathogens of medical and epidemiological importance (2), as well as various species with strong deleterious impact in agriculture (3). Additionally, by infecting unicellular eukaryotic and prokaryotic life forms, RNA viruses play a role in shaping microbial ecosystems, from the oceans to the human gut (4–8).

Historically, RNA virus discovery and characterization relied on direct cultivation or isolation of the infective agents via experiments that are often laborious and inherently restricted to viruses infecting hosts amenable to laboratory cultivation or propagation. The procedures typically involve the concentration of infectious particles from symptomatic or diseased host cells or tissues, followed by various identification techniques like microscopy (imaging), neutralization (antibody), hemadsorption, hemagglutination and plaque assays, and animal, plant, tissue or cell culture inoculation. In most cases, isolated concentrated particles would then undergo (viral) RNA extraction and purification, followed by reverse transcription into cDNA and subsequent sequencing enabling further genomic investigations (phylogeny, genotyping, etc).

## 1.1 Recent developments in omic-based RNA virus discovery: more, bigger, faster is the new pace

The advent of the genomic era has gradually expanded RNA virus discovery beyond experimental cultivation and isolation methodologies. The substantial decrease in costs associated with high-throughput nucleic acid sequencing, coupled with advances in computational capacities for big data storage and processing greatly facilitates the development of RNA virus discovery projects through large-scale sequencing (omic-based).Importantly, this genomic-data-driven exploration of the RNA virosphere using computational tools offers a unique opportunity to bypass many biases and limitations of traditional approaches, and goes hand in hand with the growing recognition of global viral diversity in ecological systems as a whole, including public health and one health ["pandemic preparedness", surveillance - (9)].

With an expanded diversity of environments sampled as well as a growing re-assessment of publicly available sequencing data and the continuous development of tools and resources, this field has experienced massive growth in recent years, with no signs of deceleration in sight (Figure 1) (8, 10–29).

## 1.2 Interdisciplinarity nature of the field limits data uniformity and standardization

RNA virus discovery is at the interface of various disciplines (i.e. virology, molecular, and structural biology, evolution, genomics, ecology, and computational sciences) and spans various fields within virology itself, each with its specific virus groups of interest, priorities, approaches, concepts, resources, etc. leading to apparent discrepancies throughout the scientific process. This heterogeneity manifests in both the experimental design, in the interpretation of the data and in the eventual conclusions and data sharing (novelty estimation, host inference, risk assessment, choice of data deposition location).

This growing lack of standardization directly and severely hampers interoperability - i.e. the ability to review, compare, reproduce, share, and build on each other's efforts. For instance, what one study may consider as a new RNA virus group based on coat or movement protein sequence similarity, another study may consider part of an existing group using the RdRp comparison.

In recognition of these issues as detrimental to the advancement of the field, we recently held the first "RdRp summit" (https://rdrp.io/) - a discussion-centric event with the goals of fostering reproducibility, collaboration and interoperability in omics-derived RNA virus discovery. The event was attended by over 70 participants (60% in-person and 40% remotely), from 50 research institutions across the world. Most attendees were ECRs, with half of the participants listed as PhD students. To promote inclusion and exchanges between all participants, the meeting featured both open discussion sessions and traditional lectures, given by key bioinformaticians and experimentalists. Herein, we summarize the major insights and consensus that emerged from the workshop.

# 2 Current challenges in RNA virus discovery

## 2.1 Multiplicity of experimental and computational practices in RNA virus discovery workflows

The initial source and type of environment, the preservation, and preparation of RNA input have profound implications on the whole analysis and final RNA virus discovery. The input for RNA-virus metagenomic studies is often the total extractable RNA from an environmental, vector or host-associated sample (12, 17, 30). Alternatively, studies can focus on a size-selected fraction where host cells are excluded and virus-like-particles (VLPs) are enriched using filtration and/or centrifugation, otherwise known as viromics or virion-associated nucleic acids (VANA)-based sequencing (22, 31). Double-stranded RNA (dsRNA) purification can also be applied to total RNA samples to specifically target dsRNA virus genomes and replicative intermediate of single-stranded RNA (ssRNA) viruses instead of single-stranded transcripts and
# 1 Introduction

RNA viruses (Orthornavirae) are genetic elements with RNA-based genomes that replicate using their encoded RNA-dependent RNA polymerase (RdRp) and by hijacking their host's cellular machinery. Progeny viruses are then transmitted to new hosts either vertically, or horizontally most often in protein-based viral particles that can sometimes be surrounded by a lipid envelope. Viruses are widely diverse, infect all life forms (1), and include many human pathogens of medical and epidemiological importance (2), as well as various species with strong deleterious impact in agriculture (3). Additionally, by infecting unicellular eukaryotic and prokaryotic life forms, RNA viruses play a role in shaping microbial ecosystems, from the oceans to the human gut (4–8).

Historically, RNA virus discovery and characterization relied on direct cultivation or isolation of the infective agents via experiments that are often laborious and inherently restricted to viruses infecting hosts amenable to laboratory cultivation or propagation. The procedures typically involve the concentration of infectious particles from symptomatic or diseased host cells or tissues, followed by various identification techniques like microscopy (imaging), neutralization (antibody), hemadsorption, hemagglutination and plaque assays, and animal, plant, tissue or cell culture inoculation. In most cases, isolated concentrated particles would then undergo (viral) RNA extraction and purification, followed by reverse transcription into cDNA and subsequent sequencing enabling further genomic investigations (phylogeny, genotyping, etc).

## 1.1 Recent developments in omic-based RNA virus discovery: more, bigger, faster is the new pace

The advent of the genomic era has gradually expanded RNA virus discovery beyond experimental cultivation and isolation methodologies. The substantial decrease in costs associated with high-throughput nucleic acid sequencing, coupled with advances in computational capacities for big data storage and processing greatly facilitates the development of RNA virus discovery projects through large-scale sequencing (omic-based).Importantly, this genomic-data-driven exploration of the RNA virosphere using computational tools offers a unique opportunity to bypass many biases and limitations of traditional approaches, and goes hand in hand with the growing recognition of global viral diversity in ecological systems as a whole, including public health and one health ["pandemic preparedness", surveillance - (9)].

With an expanded diversity of environments sampled as well as a growing re-assessment of publicly available sequencing data and the continuous development of tools and resources, this field has experienced massive growth in recent years, with no signs of deceleration in sight (Figure 1) (8, 10–29).

## 1.2 Interdisciplinarity nature of the field limits data uniformity and standardization

RNA virus discovery is at the interface of various disciplines (i.e. virology, molecular, and structural biology, evolution, genomics, ecology, and computational sciences) and spans various fields within virology itself, each with its specific virus groups of interest, priorities, approaches, concepts, resources, etc. leading to apparent discrepancies throughout the scientific process. This heterogeneity manifests in both the experimental design, in the interpretation of the data and in the eventual conclusions and data sharing (novelty estimation, host inference, risk assessment, choice of data deposition location).

This growing lack of standardization directly and severely hampers interoperability - i.e. the ability to review, compare, reproduce, share, and build on each other's efforts. For instance, what one study may consider as a new RNA virus group based on coat or movement protein sequence similarity, another study may consider part of an existing group using the RdRp comparison.

In recognition of these issues as detrimental to the advancement of the field, we recently held the first "RdRp summit" (https://rdrp.io/) - a discussion-centric event with the goals of fostering reproducibility, collaboration and interoperability in omics-derived RNA virus discovery. The event was attended by over 70 participants (60% in-person and 40% remotely), from 50 research institutions across the world. Most attendees were ECRs, with half of the participants listed as PhD students. To promote inclusion and exchanges between all participants, the meeting featured both open discussion sessions and traditional lectures, given by key bioinformaticians and experimentalists. Herein, we summarize the major insights and consensus that emerged from the workshop.

# 2 Current challenges in RNA virus discovery

## 2.1 Multiplicity of experimental and computational practices in RNA virus discovery workflows

The initial source and type of environment, the preservation, and preparation of RNA input have profound implications on the whole analysis and final RNA virus discovery. The input for RNA-virus metagenomic studies is often the total extractable RNA from an environmental, vector or host-associated sample (12, 17, 30). Alternatively, studies can focus on a size-selected fraction where host cells are excluded and virus-like-particles (VLPs) are enriched using filtration and/or centrifugation, otherwise known as viromics or virion-associated nucleic acids (VANA)-based sequencing (22, 31). Double-stranded RNA (dsRNA) purification can also be applied to total RNA samples to specifically target dsRNA virus genomes and replicative intermediate of single-stranded RNA (ssRNA) viruses instead of single-stranded transcripts and
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>FIGURE 1</figcaption></figure>

The recent expansion of the omic-based RNA virus discovery field. (A) Examples of RdRp-based viral metagenomic studies and tools (PMID are indicated in grey); (B) Multiplicity of publicly available sequences in the Sequence Read Archive based on human (pink bars) and other host (grey bars) composition (bar chart, left axis) and total cumulative number of bases (blue line, right axis). Data taken from SRA metadata available via BigQuery (nih-sra-datastore.sra.metadata).

ribosomal RNAs (32). Beyond dsRNA enrichment, targeted approaches such as the Fragmented and primer-Ligated dsRNA Sequencing (FLDS) method also feature the ability to sequence both ends of the genome (33, 34), from which pairs of segmented or multipartite viral genomes can be searched (33). Deep sequencing of small RNAs (sRNAs) can also be advantageous to plant and mycovirus discovery, by using various sRNA size profiles depending on the organism for RNA genome assembly (35–40). On the other

hand, untargeted total RNA extraction followed by RNA-seq better reflects the global sample complexity, including host and viral diversity, and can assist with host association, further answering ecological questions (41). However, the choice of the kit used for RNA extraction can substantially influence the downstream analysis (42). Prior to sequencing, classical treatments include genomic DNA digestion and either targeted ribosomal RNA (rRNA) depletion, or poly(A) enrichment steps prior to reverse
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>FIGURE 1</figcaption></figure>

The recent expansion of the omic-based RNA virus discovery field. (A) Examples of RdRp-based viral metagenomic studies and tools (PMID are indicated in grey); (B) Multiplicity of publicly available sequences in the Sequence Read Archive based on human (pink bars) and other host (grey bars) composition (bar chart, left axis) and total cumulative number of bases (blue line, right axis). Data taken from SRA metadata available via BigQuery (nih-sra-datastore.sra.metadata).

ribosomal RNAs (32). Beyond dsRNA enrichment, targeted approaches such as the Fragmented and primer-Ligated dsRNA Sequencing (FLDS) method also feature the ability to sequence both ends of the genome (33, 34), from which pairs of segmented or multipartite viral genomes can be searched (33). Deep sequencing of small RNAs (sRNAs) can also be advantageous to plant and mycovirus discovery, by using various sRNA size profiles depending on the organism for RNA genome assembly (35–40). On the other

hand, untargeted total RNA extraction followed by RNA-seq better reflects the global sample complexity, including host and viral diversity, and can assist with host association, further answering ecological questions (41). However, the choice of the kit used for RNA extraction can substantially influence the downstream analysis (42). Prior to sequencing, classical treatments include genomic DNA digestion and either targeted ribosomal RNA (rRNA) depletion, or poly(A) enrichment steps prior to reverse
transcription of RNA into cDNA. Along with the choice of
sequencing platforms/technologies, those methods will directly
impact the subsequent RNA virus findability and identification.

Current computational identification of RNA viruses from
"-omic" data (mainly transcriptomic and metatranscriptomic
sequencing, i.e. bulk RNA-seq of either a single organism or a
community of organisms, respectively) is typically conducted via
direct comparative approaches following quality control and
filtering of raw reads and de novo assembly. The discovery of
viruses through omics data primarily depends on identifying
sequence similarities with existing RNA virus genomes, protein
sequences, or protein sequence profiles. This is often accomplished
using methods such as Hidden Markov Models (HMMs) or
Position-Specific Scoring Matrices (PSSMs). Similarity is defined
as a set of minimal statistical thresholds; typically established
arbitrarily in each study and thus further stresses the need for
standardized protocols.

Predominantly, the RdRp, which is the only protein shared by
all known RNA viruses, is used as the marker gene for RNA virus
identification (14). Virus RdRps share a right-hand-shape structure,
typical of DNA/RNA polymerases, with a palm-based active site
comprising several catalytic and structural motifs, which may
require additional host factors to constitute a mature, complete
replicase domain (43). Assignment of a query sequence as a
potential viral RdRp usually requires the identification of at least
the three "core" motifs, commonly referred to as A, B, and C, with
the presence of any additional motifs increasing the reliability of the
assignment and the presumed completeness of the analyzed
sequence (44). The presence of these motifs (or roughly the
region they occupy) is most often identified via sequence search
engines (BLAST, DIAMOND, MMseqs, etc) (45-47) or profile-
based approaches (HMM via HH-Suite or HMMER, or PSSM/
PWM using PSI-BLAST or MEME) (48-50), often used in
conjunction with public databases and repositories of RNA virus-
derived RdRp e.g. subsetting NCBI nr, or custom databases like
TSA-database derived RdRps (29), NeoRdRp (23), Palmscan (51),
or RdRp-scan (18).

Furthermore, the enhancement of RNA extraction methods,
sequencing technologies, and the rapid advancement in the
development of new AI-based techniques, among other factors, is
playing a crucial role in advancing RNA virus discovery. These
advances facilitate the improved identification of potentially
divergent and low-concentration viruses within overlooked
environmental or host taxa. Nevertheless, these developments also
reinforce the methodology gaps and heterogeneity between studies
and severely limit interoperability.

## 2.2 Consequences of procedural inconsistency for comparative analyses across studies

Choices in both the experimental procedures and subsequent *in silico* analyses play a crucial role in how different studies handle, share, report, and reach conclusions regarding the suspected viral sequences in the corresponding data. Discussions held during this

first RdRp summit pinpointed the global lack of procedure and
good practice standards at every level (sampling, extraction,
sequencing, read and contig processing, as well as data analysis,
storage, submission, mining, survey, etc) (Figure 2). Ultimately,
those differences in the computational and experimental aspects can
constitute strong obstacles for the ability to adequately compare the
results of different studies and it seemed important to first
identify them.

Regarding RNA virus detection itself, there is a crucial lack of
standard minimal alignment statistics (e-value, %ID, %coverage,
etc). Also, the inconsistency of what is considered a genuine viral
genome/viral hit/viral sequence versus a potential "false-positive" or
contaminant poses a major risk of misinterpretation of results. The
ability to identify and discriminate true replicative RNA virus
signals from active or integrated viruses replicating via reverse-
transcriptases (RT) (divergent other palm-like polymerases)
(kingdom *Pararnavirae*), endogenous viral elements (EVEs), and
non-viral hits or contaminants, is absolutely crucial in our field.
However, there is currently no widely agreed-upon consensus about
defining quality standards for viral sequences and how to ensure
their identification as such. Compounding this issue is the lack of
definition for real RNA-virus derived sequences that are either
chimeric or misassembled, and thus are not likely to represent a
functional infectious entity. Plus, expanding our knowledge of the
RNA virosphere revealed an ever-increasing plurality of genome
architectures and RdRp properties, which make it even harder to
define one single rule for all of them. The recently-described divided
RdRps confirmed and validated *in silico* (52-54), which are encoded
by two distinct ORFs from separate genomic segments, constitute
the best example of such unexpected plurality. Such challenges
require continuous adaptation of standard practices and motivate to
establish community-driven, up-to-date guidelines for RNA
virus discovery.

Standardizing RNA virus detection would strongly require a
community-built consensus about performance evaluation
pipelines (sensitivity, recall, F1, precision, algorithm resilience,
etc.), similarly to the ongoing efforts in microbial and DNA virus
metagenomics (55-58). Directly linked to this, unequivocal
agreements on the plurality of operational taxonomic unit (OTU)
definitions, clustering thresholds, and minimal procedure for
genome completeness estimation of novel and divergent viruses
will help set gold standards for the scientific community (Figure 2).

The aforementioned considerations would also dramatically
decrease common inconsistencies regarding the multiplicity of
repositories that host the data as well as metadata associated with
viral metagenomics projects deposited in standard databases.
Indeed, one could note the major confusion between host and
sample source, arbitrary taxonomy assignment and gene, protein,
and genome annotations, the lack of information relating to sample
preparation, sequencing, and computational analysis, the
inadequacy of current tools for uploading viruses with divided
RdRps or segmented genomes, or sequences with alternative
START codons, and the inability for external users to revise/re-
assess/edit/ annotate the deposited metadata. All of which leads to
an absurd rate of unclassified/unannotated sequences when dealing
with remote homology searches.
transcription of RNA into cDNA. Along with the choice of
sequencing platforms/technologies, those methods will directly
impact the subsequent RNA virus findability and identification.

Current computational identification of RNA viruses from
"-omic" data (mainly transcriptomic and metatranscriptomic
sequencing, i.e. bulk RNA-seq of either a single organism or a
community of organisms, respectively) is typically conducted via
direct comparative approaches following quality control and
filtering of raw reads and de novo assembly. The discovery of
viruses through omics data primarily depends on identifying
sequence similarities with existing RNA virus genomes, protein
sequences, or protein sequence profiles. This is often accomplished
using methods such as Hidden Markov Models (HMMs) or
Position-Specific Scoring Matrices (PSSMs). Similarity is defined
as a set of minimal statistical thresholds; typically established
arbitrarily in each study and thus further stresses the need for
standardized protocols.

Predominantly, the RdRp, which is the only protein shared by
all known RNA viruses, is used as the marker gene for RNA virus
identification (14). Virus RdRps share a right-hand-shape structure,
typical of DNA/RNA polymerases, with a palm-based active site
comprising several catalytic and structural motifs, which may
require additional host factors to constitute a mature, complete
replicase domain (43). Assignment of a query sequence as a
potential viral RdRp usually requires the identification of at least
the three "core" motifs, commonly referred to as A, B, and C, with
the presence of any additional motifs increasing the reliability of the
assignment and the presumed completeness of the analyzed
sequence (44). The presence of these motifs (or roughly the
region they occupy) is most often identified via sequence search
engines (BLAST, DIAMOND, MMseqs, etc) (45-47) or profile-
based approaches (HMM via HH-Suite or HMMER, or PSSM/
PWM using PSI-BLAST or MEME) (48-50), often used in
conjunction with public databases and repositories of RNA virus-
derived RdRp e.g. subsetting NCBI nr, or custom databases like
TSA-database derived RdRps (29), NeoRdRp (23), Palmscan (51),
or RdRp-scan (18).

Furthermore, the enhancement of RNA extraction methods,
sequencing technologies, and the rapid advancement in the
development of new AI-based techniques, among other factors, is
playing a crucial role in advancing RNA virus discovery. These
advances facilitate the improved identification of potentially
divergent and low-concentration viruses within overlooked
environmental or host taxa. Nevertheless, these developments also
reinforce the methodology gaps and heterogeneity between studies
and severely limit interoperability.

## 2.2 Consequences of procedural inconsistency for comparative analyses across studies

Choices in both the experimental procedures and subsequent *in silico* analyses play a crucial role in how different studies handle, share, report, and reach conclusions regarding the suspected viral sequences in the corresponding data. Discussions held during this

first RdRp summit pinpointed the global lack of procedure and
good practice standards at every level (sampling, extraction,
sequencing, read and contig processing, as well as data analysis,
storage, submission, mining, survey, etc) (Figure 2). Ultimately,
those differences in the computational and experimental aspects can
constitute strong obstacles for the ability to adequately compare the
results of different studies and it seemed important to first
identify them.

Regarding RNA virus detection itself, there is a crucial lack of
standard minimal alignment statistics (e-value, %ID, %coverage,
etc). Also, the inconsistency of what is considered a genuine viral
genome/viral hit/viral sequence versus a potential "false-positive" or
contaminant poses a major risk of misinterpretation of results. The
ability to identify and discriminate true replicative RNA virus
signals from active or integrated viruses replicating via reverse-
transcriptases (RT) (divergent other palm-like polymerases)
(kingdom *Pararnavirae*), endogenous viral elements (EVEs), and
non-viral hits or contaminants, is absolutely crucial in our field.
However, there is currently no widely agreed-upon consensus about
defining quality standards for viral sequences and how to ensure
their identification as such. Compounding this issue is the lack of
definition for real RNA-virus derived sequences that are either
chimeric or misassembled, and thus are not likely to represent a
functional infectious entity. Plus, expanding our knowledge of the
RNA virosphere revealed an ever-increasing plurality of genome
architectures and RdRp properties, which make it even harder to
define one single rule for all of them. The recently-described divided
RdRps confirmed and validated *in silico* (52-54), which are encoded
by two distinct ORFs from separate genomic segments, constitute
the best example of such unexpected plurality. Such challenges
require continuous adaptation of standard practices and motivate to
establish community-driven, up-to-date guidelines for RNA
virus discovery.

Standardizing RNA virus detection would strongly require a
community-built consensus about performance evaluation
pipelines (sensitivity, recall, F1, precision, algorithm resilience,
etc.), similarly to the ongoing efforts in microbial and DNA virus
metagenomics (55-58). Directly linked to this, unequivocal
agreements on the plurality of operational taxonomic unit (OTU)
definitions, clustering thresholds, and minimal procedure for
genome completeness estimation of novel and divergent viruses
will help set gold standards for the scientific community (Figure 2).

The aforementioned considerations would also dramatically
decrease common inconsistencies regarding the multiplicity of
repositories that host the data as well as metadata associated with
viral metagenomics projects deposited in standard databases.
Indeed, one could note the major confusion between host and
sample source, arbitrary taxonomy assignment and gene, protein,
and genome annotations, the lack of information relating to sample
preparation, sequencing, and computational analysis, the
inadequacy of current tools for uploading viruses with divided
RdRps or segmented genomes, or sequences with alternative
START codons, and the inability for external users to revise/re-
assess/edit/ annotate the deposited metadata. All of which leads to
an absurd rate of unclassified/unannotated sequences when dealing
with remote homology searches.
<figure><img src="image_7.png" /><figcaption>FIGURE 2
Main challenges identified in the omic-based RNA discovery field and proposed solutions.</figcaption></figure>

Considering the rising scale of viral metagenomic studies and the pace of virus taxonomy expansion, such misannotations or misassignments in reference databases can have dramatic consequences when propagated to new studies and data submission, and drastically limit the scope and efficiency of data mining projects, yet are increasingly essential in our field.

## 3 Solutions and future perspectives

While incredibly valuable, the interdisciplinarity in the RNA virus discovery field also requires concerted efforts from researchers to build connections between those communities, share, and adapt our respective practices, tools, vocabulary, terminologies, and standards to fit in with everyone's domain language.

To tackle the first challenge consisting of establishing minimum standards for RNA virus genome (or viral RdRp) annotations, cut-off for parameters (alignment scores, e-values, query and reference lengths, etc.) need to be agreed upon when comparing candidate sequences to known valid RdRp sequence database vs. decoy - (RT)-like databases and sets of unclassified/unannotated sequences. Annotation could then be automatically assigned based on these comparison scores (true complete RdRp vs RT-like hits vs

unclassified/unknown). Discussions and lectures at the summit also highlighted the importance of integrating additional procedures into classical workflows such as placement within phylogenies, genomic context scan (untranslated regions, RNA structure, nucleotide and kmer composition among others), and structural homology assessment using cutting-edge AI-based prediction tools (59, 60), essential for distant homology detection or validation. While some quality criteria and cut-offs can easily be built in, some others may be very challenging. Defining boundaries for the RdRp gene (minimal length to describe a RdRp, structural attributes, minimal presence of the catalytic motifs, presence/absence of additional domains, such as Nidovirus RdRp associated nucleotidyl transferase domain - NiRAN) remains a complex task and requires expertise and extended knowledge of the viral strain (61–63).

In addition, the standardization effort should also promote the integration into the standard discovery pipelines of the most recent and state-of-the-art concepts in RNA virology such as the search for potential additional segments (64), the screen for divided RdRp (52–54), etc.

Another challenge consists of homogenization of manual and automatic clusterization procedures and taxonomic assignment of viral-like sequences. Formal virus classification by the International
<figure><img src="image_7.png" /><figcaption>FIGURE 2
Main challenges identified in the omic-based RNA discovery field and proposed solutions.</figcaption></figure>

Considering the rising scale of viral metagenomic studies and the pace of virus taxonomy expansion, such misannotations or misassignments in reference databases can have dramatic consequences when propagated to new studies and data submission, and drastically limit the scope and efficiency of data mining projects, yet are increasingly essential in our field.

## 3 Solutions and future perspectives

While incredibly valuable, the interdisciplinarity in the RNA virus discovery field also requires concerted efforts from researchers to build connections between those communities, share, and adapt our respective practices, tools, vocabulary, terminologies, and standards to fit in with everyone's domain language.

To tackle the first challenge consisting of establishing minimum standards for RNA virus genome (or viral RdRp) annotations, cut-off for parameters (alignment scores, e-values, query and reference lengths, etc.) need to be agreed upon when comparing candidate sequences to known valid RdRp sequence database vs. decoy - (RT)-like databases and sets of unclassified/unannotated sequences. Annotation could then be automatically assigned based on these comparison scores (true complete RdRp vs RT-like hits vs

unclassified/unknown). Discussions and lectures at the summit also highlighted the importance of integrating additional procedures into classical workflows such as placement within phylogenies, genomic context scan (untranslated regions, RNA structure, nucleotide and kmer composition among others), and structural homology assessment using cutting-edge AI-based prediction tools (59, 60), essential for distant homology detection or validation. While some quality criteria and cut-offs can easily be built in, some others may be very challenging. Defining boundaries for the RdRp gene (minimal length to describe a RdRp, structural attributes, minimal presence of the catalytic motifs, presence/absence of additional domains, such as Nidovirus RdRp associated nucleotidyl transferase domain - NiRAN) remains a complex task and requires expertise and extended knowledge of the viral strain (61–63).

In addition, the standardization effort should also promote the integration into the standard discovery pipelines of the most recent and state-of-the-art concepts in RNA virology such as the search for potential additional segments (64), the screen for divided RdRp (52–54), etc.

Another challenge consists of homogenization of manual and automatic clusterization procedures and taxonomic assignment of viral-like sequences. Formal virus classification by the International
Committee on Taxonomy of Viruses (ICTV) plays a vital role in providing a reference language for scientists to communicate, collaborate, and share knowledge about viruses. By coupling the robust, updated, and standardized ICTV classification framework with the power of omic-based RNA virus discovery, we can collectively improve our description and global understanding of RNA virus diversity (65).

Concretely, all these proposals will be pursued by the RdRp summit community with different initiatives that are intended to be maintained and updated over time by the community members. In particular, the community will focus on:

I) Building a central infrastructure for the RNA virus discovery community, which would work as a central repository for data and knowledge.

II) Creating a curated database with automated quality scores for data deposition based on the information provided. The database should be community-driven and open to feedback from end users to aid further curation.

III) Consolidating state-of-the-art experimental and computational resources and knowledge to ensure the best up-to-date practices among the community (pipelines, scripts, protocols, glossary, guides, international journal club). Among others, this could consist of recommended workflows for the challenging identification of novel/remote RdRp, the Do's and Don'ts of annotating new, uncultivated RNA virus genomes, and metadata recommendations for RNA viruses.

IV) In the same manner as the European Virus Bioinformatics Center (https://evbc.uni-jena.de/), particular effort will be put into enhancing the communication and uniting our expanding community into one single spot through a potential membership system, forums, round tables, workshops, online chat channels, etc.

To conclude, the major consensus that emerged from the rich discussions led during this first RdRp summit lies in the current lack of interoperability and reproducibility in our field but also the possible concrete solutions to tackle these obstacles (summarized in Figure 2).

The omic-based RNA virus discovery community, as an open science-to-society-oriented community, should be aware of its roles and responsibilities to make its scope as transparent and accessible as possible. Through the collective development of a user-friendly open platform, we aim to build a solid foundation for communicating, sharing, and performing comparable analyses using optimal and state-of-the-art tools across a wide array of biological contexts by reaching the broadest audience possible. With similar issues faced in microbial metagenomics and omic-based DNA virus discovery, we also intend to inspire from the emerging solutions and infrastructures being developed in these related fields and learn from their experiences in tackling these challenges (e.g. 66–68).

We believe these efforts will lay the groundwork to promote ECRs insertion into the community, best practices and repeatability, and ultimately ensure the best future for our exploration of the RNA virosphere.

# Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

# Author contributions

JC: Writing – original draft, Writing – review & editing, Conceptualization, Project administration. IO: Writing – original draft, Writing – review & editing, Visualization. MF: Writing – original draft, Writing – review & editing. LC: Writing – original draft, Writing – review & editing, Visualization. LH: Writing – original draft, Writing – review & editing. SR: Writing – original draft, Writing – review & editing. AK: Writing – original draft, Writing – review & editing. HD: Writing – original draft, Writing – review & editing. SS: Writing – original draft, Writing – review & editing. RT: Writing – original draft, Writing – review & editing. SN: Writing – original draft, Writing – review & editing. ABa: Writing – original draft, Writing – review & editing. AA: Writing – review & editing. NB: Writing – review & editing. KB: Writing – review & editing. KBM: Writing – review & editing. ABu: Writing – review & editing. AC: Writing – review & editing. FC: Writing – review & editing. GC: Writing – review & editing. YC: Writing – review & editing. LDC: Writing – review & editing. TD: Writing – review & editing. GD: Writing – review & editing. JD: Writing – review & editing. SG: Writing – review & editing. EH: Writing – review & editing. FJ: Writing – review & editing. DK: Writing – review & editing. SL: Writing – review & editing. SMK: Writing – review & editing. JM: Writing – review & editing. YN: Writing – review & editing. AO: Writing – review & editing. MR: Writing – review & editing. RR: Writing – review & editing. SS: Writing – review & editing. SS: Writing – review & editing. JS: Writing – review & editing. HS: Writing – review & editing. ES: Writing – review & editing. MS: Writing – review & editing. YS: Writing – review & editing. MW: Writing – review & editing. YIW: Writing – review & editing. NZ: Writing – review & editing. UN: Writing – original draft, Writing – review & editing, Conceptualization, Supervision.

# Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. JC was supported by the French National Research Agency (ANR-21-CE35-0009). UN was supported by the European Research Council (ERC-AdG 787514) and by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel Aviv University, and by fellowship from the Shmunis School of Biomedicine and Cancer Research. The work conducted by the U.S. Department of Energy Joint Genome Institute (S.R.) (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. AB was supported by the postdoctoral fellowship from Fondation Recherche Médicale (FRM) and
Committee on Taxonomy of Viruses (ICTV) plays a vital role in providing a reference language for scientists to communicate, collaborate, and share knowledge about viruses. By coupling the robust, updated, and standardized ICTV classification framework with the power of omic-based RNA virus discovery, we can collectively improve our description and global understanding of RNA virus diversity (65).

Concretely, all these proposals will be pursued by the RdRp summit community with different initiatives that are intended to be maintained and updated over time by the community members. In particular, the community will focus on:

I) Building a central infrastructure for the RNA virus discovery community, which would work as a central repository for data and knowledge.

II) Creating a curated database with automated quality scores for data deposition based on the information provided. The database should be community-driven and open to feedback from end users to aid further curation.

III) Consolidating state-of-the-art experimental and computational resources and knowledge to ensure the best up-to-date practices among the community (pipelines, scripts, protocols, glossary, guides, international journal club). Among others, this could consist of recommended workflows for the challenging identification of novel/remote RdRp, the Do's and Don'ts of annotating new, uncultivated RNA virus genomes, and metadata recommendations for RNA viruses.

IV) In the same manner as the European Virus Bioinformatics Center (https://evbc.uni-jena.de/), particular effort will be put into enhancing the communication and uniting our expanding community into one single spot through a potential membership system, forums, round tables, workshops, online chat channels, etc.

To conclude, the major consensus that emerged from the rich discussions led during this first RdRp summit lies in the current lack of interoperability and reproducibility in our field but also the possible concrete solutions to tackle these obstacles (summarized in Figure 2).

The omic-based RNA virus discovery community, as an open science-to-society-oriented community, should be aware of its roles and responsibilities to make its scope as transparent and accessible as possible. Through the collective development of a user-friendly open platform, we aim to build a solid foundation for communicating, sharing, and performing comparable analyses using optimal and state-of-the-art tools across a wide array of biological contexts by reaching the broadest audience possible. With similar issues faced in microbial metagenomics and omic-based DNA virus discovery, we also intend to inspire from the emerging solutions and infrastructures being developed in these related fields and learn from their experiences in tackling these challenges (e.g. 66–68).

We believe these efforts will lay the groundwork to promote ECRs insertion into the community, best practices and repeatability, and ultimately ensure the best future for our exploration of the RNA virosphere.

# Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

# Author contributions

JC: Writing – original draft, Writing – review & editing, Conceptualization, Project administration. IO: Writing – original draft, Writing – review & editing, Visualization. MF: Writing – original draft, Writing – review & editing. LC: Writing – original draft, Writing – review & editing, Visualization. LH: Writing – original draft, Writing – review & editing. SR: Writing – original draft, Writing – review & editing. AK: Writing – original draft, Writing – review & editing. HD: Writing – original draft, Writing – review & editing. SS: Writing – original draft, Writing – review & editing. RT: Writing – original draft, Writing – review & editing. SN: Writing – original draft, Writing – review & editing. ABa: Writing – original draft, Writing – review & editing. AA: Writing – review & editing. NB: Writing – review & editing. KB: Writing – review & editing. KBM: Writing – review & editing. ABu: Writing – review & editing. AC: Writing – review & editing. FC: Writing – review & editing. GC: Writing – review & editing. YC: Writing – review & editing. LDC: Writing – review & editing. TD: Writing – review & editing. GD: Writing – review & editing. JD: Writing – review & editing. SG: Writing – review & editing. EH: Writing – review & editing. FJ: Writing – review & editing. DK: Writing – review & editing. SL: Writing – review & editing. SMK: Writing – review & editing. JM: Writing – review & editing. YN: Writing – review & editing. AO: Writing – review & editing. MR: Writing – review & editing. RR: Writing – review & editing. SS: Writing – review & editing. SS: Writing – review & editing. JS: Writing – review & editing. HS: Writing – review & editing. ES: Writing – review & editing. MS: Writing – review & editing. YS: Writing – review & editing. MW: Writing – review & editing. YIW: Writing – review & editing. NZ: Writing – review & editing. UN: Writing – original draft, Writing – review & editing, Conceptualization, Supervision.

# Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. JC was supported by the French National Research Agency (ANR-21-CE35-0009). UN was supported by the European Research Council (ERC-AdG 787514) and by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel Aviv University, and by fellowship from the Shmunis School of Biomedicine and Cancer Research. The work conducted by the U.S. Department of Energy Joint Genome Institute (S.R.) (https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231. AB was supported by the postdoctoral fellowship from Fondation Recherche Médicale (FRM) and
Canadian Institutes for Health Research (PJT-496709). ABu was supported by a postdoctoral fellowship from Fondation Recherche Médicale (FRM). KBM was supported by the Ministry of Agriculture of the Czech Republic, institutional support (MZE-RO0423). RT was funded by the Belgian Federal Public Service Health, Food Chain Safety and Environment through the contract (GenoPREDICT - RF 22/635). ES was supported by Marie Skłodowska-Curie fellowship "DIVOBIS" awarded by Horizon Europe 2022. SN was supported by KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas 16H06429, 16K21723, 19H04843 and by JST CREST JPMJCR20H6. YIW was supported by intramural funds of the US Department of Health and Human Services (National Institutes of Health, National Library of Medicine). EH was supported by AIR@InnoHK administered by the Innovation and Technology Commission, Hong Kong Special Administrative Region, China. AO-B was supported by a National Health and Medical Research Council (NHMRC) Investigator Grant (GNT2017197) awarded to Prof. Edward C. Holmes. YS was supported by Hong Kong Research Grants Council (RGC) General Research Fund (GRF) (11206819, 11217521) and Hong Kong Innovation and Technology Fund (ITF) MRP/071/20X. RR was supported by funding to Verena (viralemergence.org) from the U.S. National Science Foundation, including NSF BII 2021909 and NSF BII 2213854 and by a Chan Zuckerberg Initiative (CZI) Global Voice Travel Award from CZI, Redwood City, California, USA. YC and YN were supported by JSPS KAKENHI Grant Number JP22H05714. YC was supported by JSPS KAKENHI Grant Number JP23KJ1995. TD was supported by the Research Council of Finland grant 330977 and the Kone Foundation grant. FM was supported by a Chan Zuckerberg Initiative (CZI) Global Voice Travel Award from CZI, Redwood City, California, USA. DK and AK were supported by the Graduate School Experimental Plant Sciences (project V-GENE), LDC was supported by the Research Foundation Flanders (11L1323N). IO and KB were supported by Wellcome Trust 220814/Z/20/Z awarded to Prof Andrew Firth. HS was supported by Flanders Marine Institute (VLIZ). AC was supported by Ministerio de Ciencia e Innovación of Spain and FEDER grant PID2020-116008GB-I00 awarded to Marcos de la Peña. JS was supported by the South African Medical Research Council (SAMRC) with funds received from the National Department of Health. MS was supported by the US National Science Foundation awards #OCE1829831 and ABI#2149505. FC and SG were supported by the French Direction Générale de l'Alimentation (grant C-2023-016). LH was supported by the U.S.

Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program, award number DE-SC0023127 grant to Joanne B. Emerson, grant PI Sydney Glassman. SS was supported by KAKENHI Grant-in-Aid for Scientific Research (22K14999).

## Acknowledgments

We would like to thank Amy Heather Fitzpatrick, Anastasia Gulyaeva, Chien-Fu Wu, Heli Mönttinen, Liubov Chuprikova, Fabiana Neves, Nikolay Simankov, Noriko Cassman, Nuria Fontdevila Pareta, Rebecca Grimwood, Stephanie Waller, Zivile Buivydaite, Alexander Allman, Artem Baidaliuk, Benjamin Lee, Cadhla Firth, Chuan Cao, Clarence Le, Luca Nishimura, Dehan Cai, Manja Marz, Elizabeth Fahsbender, Gabriel Lencioni Lovate, Hassan Z. A. Ishag, Isa de Vries, Janelle Wierenga, Javier Rodriguez-Grille, Jordan Taylor, Katarina Bačnik, Katrina Kalantar, Lauren Lim, Mark Paul S. Rivarez, Nina A. H. Madsen, Nolwenn Dheilly, Olve Peersen, Robert Edgar, Sandra Triebel, Satyabrata Satapathy, Sung won Lim, Xubo Tang and Yuanyuan Zhang for their precious contribution to the RdRp summit and the fruitful exchanges. We are also thankful to the sponsors of the first RdRp summit, the Chan-Zuckerberg Initiative (CZI) and Frontiers in Virology, for their support.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## References

1. Dominguez-Huerta G, Wainaina JM, Zayed AA, Culley AI, Kuhn JH, Sullivan MB. The RNA virosphere: How big and diverse is it? *Environ Microbiol*. (2023) 25:209–15. doi: 10.1111/1462-2920.16312

2. Jones KE, Patel NG, Levy MA, Storeygard A, Balk D, Gittleman JL, et al. Global trends in emerging infectious diseases. *Nature*. (2008) 451:990–3. doi: 10.1038/nature06536

3. Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. The global burden of pathogens and pests on major food crops. *Nat Ecol Evol*. (2019) 3:430–9. doi: 10.1038/s41559-018-0793-y

4. Lang AS, Rise ML, Culley AI, Steward GF. RNA viruses in the sea. *FEMS Microbiol Rev*. (2009) 33:295–323. doi: 10.1111/j.1574-6976.2008.00132.x

5. Steward GF, Culley AI, Mueller JA, Wood-Charlson EM, Belcaid M, Poisson G. Are we missing half of the viruses in the ocean? *ISME J*. (2013) 7:672–9. doi: 10.1038/ismej.2012.121

6. Liang G, Bushman FD. The human virome: assembly, composition and host interactions. *Nat Rev Microbiol*. (2021) 19:514–27. doi: 10.1038/s41579-021-00536-5
Canadian Institutes for Health Research (PJT-496709). ABu was supported by a postdoctoral fellowship from Fondation Recherche Médicale (FRM). KBM was supported by the Ministry of Agriculture of the Czech Republic, institutional support (MZE-RO0423). RT was funded by the Belgian Federal Public Service Health, Food Chain Safety and Environment through the contract (GenoPREDICT - RF 22/635). ES was supported by Marie Skłodowska-Curie fellowship "DIVOBIS" awarded by Horizon Europe 2022. SN was supported by KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas 16H06429, 16K21723, 19H04843 and by JST CREST JPMJCR20H6. YIW was supported by intramural funds of the US Department of Health and Human Services (National Institutes of Health, National Library of Medicine). EH was supported by AIR@InnoHK administered by the Innovation and Technology Commission, Hong Kong Special Administrative Region, China. AO-B was supported by a National Health and Medical Research Council (NHMRC) Investigator Grant (GNT2017197) awarded to Prof. Edward C. Holmes. YS was supported by Hong Kong Research Grants Council (RGC) General Research Fund (GRF) (11206819, 11217521) and Hong Kong Innovation and Technology Fund (ITF) MRP/071/20X. RR was supported by funding to Verena (viralemergence.org) from the U.S. National Science Foundation, including NSF BII 2021909 and NSF BII 2213854 and by a Chan Zuckerberg Initiative (CZI) Global Voice Travel Award from CZI, Redwood City, California, USA. YC and YN were supported by JSPS KAKENHI Grant Number JP22H05714. YC was supported by JSPS KAKENHI Grant Number JP23KJ1995. TD was supported by the Research Council of Finland grant 330977 and the Kone Foundation grant. FM was supported by a Chan Zuckerberg Initiative (CZI) Global Voice Travel Award from CZI, Redwood City, California, USA. DK and AK were supported by the Graduate School Experimental Plant Sciences (project V-GENE), LDC was supported by the Research Foundation Flanders (11L1323N). IO and KB were supported by Wellcome Trust 220814/Z/20/Z awarded to Prof Andrew Firth. HS was supported by Flanders Marine Institute (VLIZ). AC was supported by Ministerio de Ciencia e Innovación of Spain and FEDER grant PID2020-116008GB-I00 awarded to Marcos de la Peña. JS was supported by the South African Medical Research Council (SAMRC) with funds received from the National Department of Health. MS was supported by the US National Science Foundation awards #OCE1829831 and ABI#2149505. FC and SG were supported by the French Direction Générale de l'Alimentation (grant C-2023-016). LH was supported by the U.S.

Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER), Genomic Science Program, award number DE-SC0023127 grant to Joanne B. Emerson, grant PI Sydney Glassman. SS was supported by KAKENHI Grant-in-Aid for Scientific Research (22K14999).

## Acknowledgments

We would like to thank Amy Heather Fitzpatrick, Anastasia Gulyaeva, Chien-Fu Wu, Heli Mönttinen, Liubov Chuprikova, Fabiana Neves, Nikolay Simankov, Noriko Cassman, Nuria Fontdevila Pareta, Rebecca Grimwood, Stephanie Waller, Zivile Buivydaite, Alexander Allman, Artem Baidaliuk, Benjamin Lee, Cadhla Firth, Chuan Cao, Clarence Le, Luca Nishimura, Dehan Cai, Manja Marz, Elizabeth Fahsbender, Gabriel Lencioni Lovate, Hassan Z. A. Ishag, Isa de Vries, Janelle Wierenga, Javier Rodriguez-Grille, Jordan Taylor, Katarina Bačnik, Katrina Kalantar, Lauren Lim, Mark Paul S. Rivarez, Nina A. H. Madsen, Nolwenn Dheilly, Olve Peersen, Robert Edgar, Sandra Triebel, Satyabrata Satapathy, Sung won Lim, Xubo Tang and Yuanyuan Zhang for their precious contribution to the RdRp summit and the fruitful exchanges. We are also thankful to the sponsors of the first RdRp summit, the Chan-Zuckerberg Initiative (CZI) and Frontiers in Virology, for their support.

## Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

## Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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