Consensus statement from the second RdRp Summit: towards a unified framework for RNA virus biology

Consensus statement from the second RdRp Summit: towards a unified framework for RNA virus biology Authors Alexander G. Lucaci¹,²,*, Hisham M. Shaikh³,*, Li Chuin Chong⁴,*, Rachid Tahzima⁵, Marco Forgia⁶, Karima Ben Mansour7,8,46, Shoichi Sakaguchi⁹, So Nakagawa¹⁰, Xin Hou¹¹, Tatiana Demina¹², Fhilmar Raj Jayaraj Mallika¹³, Anne Kupczok¹⁴, Spyros Lytras¹⁵,¹⁶, Humberto Debat¹⁷, Justine Charon¹⁸, Michael Louie Urzo¹⁹, Milica Raco²⁰, Rachel Seongeun Kim²¹, Ricardo Rivero²², Dimitris Karapliafis¹⁴, Leyla Sirkinti²³, Laura Luebbert²⁴,²⁵, Luca Nishimura¹⁵, Rayan Chikhi²⁶, Lander De Coninck²⁷, Florian Charriat²⁸, Emma Soufir²⁸,²⁹, Vladimir Gajdov³⁰, Thomas Krannich³¹, Gytis Dudas³², Cédric Lood³³, Josue Rodríguez-Ramos³⁴, Anja Pecman³⁵, Uri Neri³⁶, Almut Werner³⁷, Mia Le³⁸,³⁹,⁴⁰, Bolaji Osundahunsi⁴¹, Nils Peter Petersen³⁸,³⁹, François Maclot¹⁸, Serafin Gutierrez²⁸,⁴², Sofia Paraskevopoulou³¹, Luke Hillary⁴³, Ingrida Olendraite⁴⁴,⁴⁵ Affiliations ¹ Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA ² The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA ³ Research Department, Flanders Marine Institute (VLIZ), InnovOcean Site, Ostend, Belgium ⁴ Institute for Experimental Virology, TWINCORE Centre for Experimental and Clinical Infection Research (MHH–HZI), Hannover, Germany ⁵ University of Brussels (VUB) – Bio2Byte / Structural Biology and Artificial Intelligence Lab, Brussels, Belgium ⁶ IPSP-CNR, Bari, Italy ⁷ Ecology, Diagnostics and Genetic Resources of Agriculturally Important Viruses, Fungi and Phytoplasmas, Czech Agrifood Research Center, Prague, Czech Republic ⁸ Department of Plant Protection, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic ⁹ Department of Microbiology and Infection Control, Faculty of Medicine, Osaka Medical and Pharmaceutical University, Osaka, Japan ¹⁰ Department of Molecular Life Science, Tokai University School of Medicine, Kanagawa, Japan ¹¹ Institut Pasteur, Université Paris Cité CNRS UMR2000, Evolutionary Genomics of RNA Viruses Unit, Paris, France ¹² University of Helsinki, Faculty of Agriculture and Forestry, Department of Microbiology, Helsinki, Finland ¹³ Molecular Virology Laboratory, ICAR–National Research Centre for Banana, Tiruchirappalli, Tamil Nadu, India ¹⁴ Bioinformatics Group, Wageningen University & Research, Wageningen, Netherlands ¹⁵ Division of Systems Virology, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan ¹⁶ MRC–University of Glasgow Centre for Virus Research, Glasgow, UK ¹⁷ National Institute of Agricultural Technology, Cordoba, Argentina ¹⁸ Fruit Biology and Pathology Unit, INRAE / University of Bordeaux, Villenave d'Ornon, France ¹⁹ Virology Laboratory, Microbiology Division, Institute of Biological Sciences, University of the Philippines Los Baños, Philippines ²⁰ Department of Botany and Plant Pathology, Oregon State University, Corvallis, USA ²¹ Interdisciplinary Program in Bioinformatics, Seoul National University / School of Biological Sciences, Seoul National University, Seoul, Republic of Korea ²² Paul G. Allen School for Global Health, Washington State University, Pullman, Washington, USA ²³ Department of Molecular and Medical Virology, Ruhr University Bochum, Bochum, Germany ²⁴ Eric and Wendy Schmidt Center & Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, MA, USA ²⁵ Department of Organismic and Evolutionary Biology, Harvard University, MA, USA ²⁶ Institut Pasteur, Université Paris Cité, CNRS UMR3525, Paris, France ²⁷ KU Leuven, Division of Clinical and Epidemiological Virology, Leuven, Belgium ²⁸ ASTRE, CIRAD, INRAE, Université de Montpellier, Montpellier, France ²⁹ PPCEI, INSERM, Université de Montpellier, Montpellier, France ³⁰ Scientific Veterinary Institute Novi Sad, Novi Sad, Serbia ³¹ Genome Competence Center, Robert Koch Institute, Berlin, Germany ³² Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania ³³ Department of Biology, University of Oxford, Oxford, United Kingdom ³⁴ Pacific Northwest National Laboratory, Biological Sciences Division, USA ³⁵ National Institute of Biology, Department of Biotechnology and Systems Biology, Ljubljana, Slovenia ³⁶ DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA ³⁷ Institute for Microbiology, Christian-Albrechts-University, Kiel, Germany ³⁸ Bernhard Nocht Institute for Tropical Medicine (BNITM), Hamburg, Germany ³⁹ German Center for Infection Research (DZIF), partner site Hamburg–Lübeck–Borstel–Riems, Germany ⁴⁰ Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany ⁴¹ Department of Entomology and Plant Pathology, University of Arkansas, USA ⁴² Laboratory of Virology, Montpellier University Hospital, Montpellier, France ⁴³ Department of Plant Pathology, University of California Davis, Davis, CA, USA ⁴⁴ Division of Virology, Department of Pathology, University of Cambridge, United Kingdom ⁴⁵ VUGENE, Lithuania 46 Department of Plant Biology, Swedish University of Agricultural Sciences, Uppsala, Sweden * denotes equal contribution Correspondence [email protected], [email protected] 2 Abstract RNA-dependent RNA polymerase, or RdRp, remains the central molecular hallmark of RNA viruses. It serves as both a universal anchor for virus detection and a critical target for understanding the functional and evolutionary properties of RNA viruses. Since the inaugural RdRp summit in 2023, there have been significant advances in sequencing, structural prediction and artificial intelligence, all of which have accelerated the pace of RNA virus discovery and taxonomic annotation, revealing unprecedented levels of viral diversity, including novel phyla and unique genome architectures. The second RdRp summit, which was held in Lisbon in May 2025, gathered a group of research scientists from diverse subfields of virology to address emerging challenges in RNA virus biology. These challenges ranged from standardising annotation and data sharing to harnessing structure-guided phylogenetics and planetary-scale computational tools. Here, our consensus statement outlines key progress, current and future challenges and community-driven initiatives, including benchmarking, virus-host inference, and ongoing knowledge exchange efforts - all of which are designed to unify the field. By fostering an environment of sustained collaboration, our efforts aim to build a coherent framework for modern RNA virus biology and to accelerate the exploration of the hidden RNA virosphere. 3 1 Introduction RNA-dependent RNA polymerase (RdRp) is a key enzyme in the life cycle of RNA viruses, and serves as the central molecular hallmark of Orthornavirae, providing both a conserved anchor for their identification, and a critical target for functional assessment of genome replication, and evolutionary analysis (Koonin et al. 2015). Due to its inherent role in viral replication and its ubiquitous presence across major RNA virus lineages, RdRp therefore remains one of the most powerful markers for uncovering viral diversity, especially in the era of metagenomic and metatranscriptomic datasets (Wolf et al. 2018; Charon et al. 2022). The relatively conserved catalytic motifs enable detection even across highly divergent sets of viral clades, making it indispensable for the characterization of the immense “viral dark matter” that remains unculturable and unclassified. By recognizing the central role of RdRp in RNA virus biology, a global community of scientific researchers from diverse disciplines including virology, bioinformatics, structural biology, epidemiology, and evolutionary genomics convened for the inaugural RdRp summit in Valencia, Spain, on 22-23 May 2023 (Charon et al. 2024). A second RdRp summit took place in May 2025 in Lisbon, Portugal, with the purpose of discussing the latest advancements, and remaining challenges in the identification, annotation, and phylogenetic interpretation of RdRp sequences. Participants from across the globe gathered in order to share benchmarks, discuss emerging tools and define best practices for large-scale RNA virus discovery. This second summit succeeded in fostering new collaborative initiatives to identify and address the emerging challenges of the field. 1.1 Advancements in the field since the last RdRp Summit Since the first RdRp summit, several studies have been published in the field of RNA virus discovery and characterization (Figure 1). These studies focus on benchmarking RNA processing methods for High-Throughput Sequencing (HTS) (Schönegger et al., 2023), and on the conceptualization and development of new tools for RdRp identification (Liu et al. 2025) (Figure 2, Top). Technological advances in long-read sequencing have encouraged the publication of studies applying these methods to virome investigations through total RNA sequencing (Pichler et al. 2023). This approach could potentially open new avenues for describing viral diversity at the viral isolate level in the future. Most importantly, progress in the accessibility and scalability of protein structural prediction and modeling has provided the community with powerful tools to investigate the phylogenetic relationships between divergent viruses. These tools also show promise in detecting new clades of RNA viruses with ORFs lacking any detectable homology to known open reading frames (Yin and Fischer 2006) and completing viral genomes by identifying additional genomic fragments coding for distantly related proteins. These applications are made possible by continuous efforts in the analysis (Chikhi et al. 2025) and annotation (Hou et al. 2024) of global RNAseq databases, which are now available to the RNA virology community. All these efforts have led to a remarkable expansion of viral taxonomy, both in terms of the number of known viral species and higher-level classifications. Notably, two new phyla have been added to the Riboviria realm: Ambiviricota (Kuhn et al. 2024a) and Artimaviricota 4 (Urayama et al. 2024). The discovery of viruses belonging to the phylum Ambiviricota also revealed, for the first time, that RNA viruses can utilize a genomic organization based on circular RNA sequences that replicate via a rolling circle mechanism (RCA). Accelerated discovery and RdRp-based phylogeny of viruses known as bunyavirals led to the establishment of the class Bunyaviricetes under the Negarnaviricota phylum, accommodating negative-sense RNA viruses (Kuhn et al. 2024b). 2 Modern challenges in RdRp Biology 2.1 RNA Virus Categorization Environmental sampling and sequencing has revolutionized RNA virus taxonomy by expanding the known diversity of RNA viruses (Simmonds et al. 2017), effectively doubling the recognized repertoire in recent years through discoveries across a wide range of ecosystems and hosts. Many of these newly identified viruses lack known pathogenicity or clear evolutionary ties to existing taxa (Neri et al. 2022; Wolf et al. 2020). In response to this surge in genomic data, the International Committee on Taxonomy of Viruses (ICTV) has adopted phylogenetic analysis as the main criterion for classifying newly identified viruses, even in the absence of biological information (International Committee on Taxonomy of Viruses Executive Committee 2020). This shift acknowledges the practical challenges of characterising the rapidly growing number of viral sequences but has also sparked debate among virologists about the implications for species definitions and biological relevance (Simmonds et al. 2017; Neri et al. 2022; Wolf et al. 2020; Gibbs 2020) (Figure 2, Bottom). This taxonomic framework relies on computational methods that can classify metagenomic sequences despite limited biological information. Reference-based methods are often used for taxonomic classification which compare sequences to curated viral databases, along with marker-based methods, which rely on conserved genes in viral clades. For RNA viruses, the RdRp gene is typically used because it is the most conserved genomic region and is widely used to infer evolutionary relationships (Tang et al. 2022). However, the extensive sequence divergence and structural variability of RdRp often complicate accurate reconstruction and limit taxonomic resolution (Holmes and Duchêne 2019). To address these challenges, Tang et al. developed RdRpBin, a computational tool combining an alignment-based strategy and machine learning models to improve RdRp sequence detection and classification (Tang et al. 2022). 5 Figure 1. Cumulative growth of studies focused on RdRp over the years. The rate of new studies and associated SRA submissions appears to be plateauing, whereas the average size of individual sequencing runs is steadily increasing (Chikhi et al. 2025). Since the last RdRp summit in 2023, 475 extra studies on RdRp have been conducted. This figure was generated by doing a keyword search of the SRA for "RdRp", therefore it may miss some metatranscriptome studies. 2.2 A unified RNA virus data landscape The analysis of samples using metagenomics methods often reveals diverse and largely unexplored RNA viral genomes in the environment. By advancing the discovery, annotation, and standardized sharing of these viral genomes, the RNA virus data landscape can be unified. Although efforts have been made in the past to standardize the reporting of viral genome data (MIUViG), these were mostly tailored to dsDNA bacteriophages (Roux et al. 2019). Therefore, a growing need exists for an update to these standards to include the specific needs for RNA virus reporting, e.g. methods used for recovery of segmented genomes, (i.e. MIUViG v2), along with user-friendly tools (e.g. suvtk, https://github.com/LanderDC/suvtk) that ensure viral genome data are not only accurately represented but also FAIR-compliant. Viral metagenomics is revealing the hidden diversity of viruses in both urban (e.g., global RNA viromes in cities) (Gao et al. 2024) and natural settings (e.g., rodent-associated viruses in Serbia, water-associated viromes in high-altitude lakes or bee-and pollen-associated viromes in Canadian tree fruit orchards) pointing towards their broader ecological and public health relevance (Wu et al. 2025; Vansia et al. 2024). An integrated vision for building a comprehensive and accessible global viral landscape exists, and is guided through the improvement of methods, tools, and collaborative standards. 2.3 Expanding the RNA virus discovery toolkit Advances in computational biology, sequencing technologies, and machine learning are transforming how RNA viruses are detected, classified, and studied. Traditional alignment-based methods have been supplemented by profile Hidden Markov Models (pHMMs) (https://github.com/dimitris-karapliafis/RdRpCATCH), structure-aware homology detection, and, more recently, deep learning models that can be applied to identify viruses from genomic and metagenomic data, such as CHEER, VirHunter, Virtifier, and RNN-VirSeeker (Shang and Sun 2021; Sukhorukov et al. 2022; Miao et al. 2022; Liu et al. 2022). These tools employ convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Despite their 6 versatility, both face limitations in processing biological sequences: CNNs may encounter challenges with inputs of varying lengths and capturing global correlations, while RNNs struggle with longer sequences due to vanishing or exploding gradients and difficulties in capturing long-term dependencies. To address this, Hou et al. employed a new AI model based on transformer architecture (i.e., LucaProt) for RNA virus discovery that utilizes both protein sequences and the structural characteristics of viral RdRp sequences, and shows its power for uncovering remote viral RdRp homologies and functional signatures, even in sequences with no detectable similarity to known references (Hou et al. 2024; Nakagawa and Sakaguchi 2024). These tools enable researchers to uncover RNA viruses that would have previously escaped detection due to their low sequence similarity to known taxa. At the same time, long-read and single-cell sequencing technologies are enhancing genome assembly and host linkage, especially in complex environmental samples. This growing arsenal of methods facilitates the discovery of the hidden RNA virosphere, annotation of functional viral elements, and inference of host–virus interactions, thereby enriching both taxonomic resolution and biological interpretation. 2.4 Mining the planetary virosphere at scale and depth Several global initiatives, such as VIRION, PREDICT, and the NIH Human Virome Program (https://commonfund.nih.gov/humanvirome) have been instrumental in elucidating viromes across different environments, organisms, or ecosystems, each implementing distinct sampling and analytical strategies (Wallace et al. 2025; Wu and Peng 2024; Wang et al. 2025; Carlson et al. 2022). Alongside these studies are the increasing number of research practices which introduce challenges in verifying the identity and taxonomy of uncultivated viruses. This underscores the need for standardized methodologies that would ensure interoperability, reproducibility, and robust results that are both verifiable and reliable. Leveraging large-scale computational and evolutionary approaches allows for the discovery of hidden dimensions of the planet's virosphere and enables the assessment of emerging viral threats at unprecedented scales (Chikhi et al. 2025; Edgar et al. 2022). For example, the Logan project provides a framework for the assembly and analysis of vast amounts of complex and fragmented genomic data. The identification of endogenous viral elements embedded in public databases, using uncharacterized proteins as a window into ancient viral integrations, provides us with a deeper understanding of their evolutionary legacies in host genomes and their biological implications (Brown and Firth 2024). Predicting the risk of viral spillover with PREDICTORix (https://github.com/lauberlab/PREDICTORix, not publicly available at the time of submission), a scalable, phylogeny-aware framework that integrates viral sequence data, host associations, and evolutionary relationships, allows for systematic evaluation of how likely a given virus is to cross species barriers (Deng et al. 2025). The integration of bioinformatics, evolutionary biology, and big data illuminates the origins, trajectories, and zoonotic potential of RNA viruses on a global scale. Taken together, these tools provide insights into viral emergence, biological history, and future threats to human and animal health. 7 Figure 2. Advances in RNA virus biology and ICTV growth since 2023. (Top) Progress in RNA virus research following the inaugural RdRp Summit in 2023 includes long-read and single-cell sequencing, structure-based phylogenetics, large-scale protein modeling (AlphaFold, ESMfold, Foldseek, BFVD), and AI-driven discovery tools (LucaProt, CHEER, VirHunter), which together have revealed deeply divergent RNA virus lineages and illuminated the hidden virosphere. (Bottom) Each of the rings illustrates the rapid expansion of the ICTV taxonomy, including new phyla (Ambiviricota and Artimaviricota). 2.5 Illuminating the hidden RNA virosphere Approaches to uncovering the vast hidden diversity of RNA viruses, often referred to as “viral dark matter”, offer insights into one of the largest biological diversity reservoirs on the planet. By harnessing artificial intelligence to systematically document the hidden RNA virosphere, we have begun to understand and demonstrate how machine learning can be applied to detect and classify novel RNA viruses from increasingly complex datasets (Hou et al. 2024). Structural bioinformatics methods (BFVD-Foldseek) use protein-folding and alignment to identify deeply divergent viral sequences that elude current conventional sequence homology-based detection methods and further illuminate the structural and functional diversity (and their relationship) of viral dark matter (Kim et al. 2025). The extreme diversity presented by the marker gene RdRp can be examined using deep evolutionary analysis to demonstrate how this core viral enzyme varies significantly across lineages and diverse environments. The potentially transformative synergy between advances in AI methods, structural biology, and evolutionary sequence-based modeling is poised to fundamentally reshape our understanding of the breadth and depth of RNA viral diversity. The deliberate harmonization of these approaches constitutes a major strength of the field and has the potential to exert a lasting impact on future discoveries. 8 3 Towards community-driven solutions and future initiatives The RdRp Summit 2025 brought together researchers working on current challenges of RNA virus research, but also in actively addressing them through community-driven collaboration. As the field of RNA biology continues to expand at an unprecedented pace and is spurred on by advances in shortand long-read sequencing technologies, structural prediction, artificial intelligence, and metagenomics - it has become increasingly important to align efforts around shared methodologies, standards, and infrastructures. A key goal of the summit is to foster a community that remains active between the biannual meetings. To support this objective, the summit serves as a hub to connect researchers facing similar challenges, enabling them to work collectively toward shared solutions. To this end, participants were invited to propose ideas for community-driven initiatives. A dedicated session was held to form collaborative groups around selected topics of mutual interest. In this report, we present the community projects that will be supported and developed by the RdRp Summit over the next two years (Figure 3). 3.1 Community projects As the field of RNA biology expands at an unprecedented pace, spurred on by significant advances in short-read and long-read sequencing technologies, structural prediction, AI, and metagenomics, it becomes increasingly important to align efforts around shared methods, standards, and infrastructures. To meet this need, the summit aims to establish collaborative initiatives that have been proposed and launched by the community. For example, as part of the 1st RdRp Summit, the community project of RdRpCATCH -RdRp Collaborative Analysis Tools with Collections of pHMMswas introduced (https://github.com/dimitris-karapliafis/RdRpCATCH). This project consolidated multiple RdRp pHMM databases developed over the past five years into a single resource, addressing fragmentation in the field and reducing the technical barriers to the discovery of RNA viruses. The 2nd RdRp Summit led to the launch of multiple community projects, each designed to tackle key gaps in RNA virus discovery. 9 Heinzinger, Michael, Konstantin Weissenow, Joaquin Gomez Sanchez, et al. 2024. “Bilingual Language Model for Protein Sequence and Structure.” NAR Genomics and Bioinformatics 6 (4): lqae150. https://doi.org/10.1093/nargab/lqae150. 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