Alexander G. Lucaci

RNA viruses represent an integral component of human-associated environments and human health. However, the ecology of environmental RNA viruses remains largely unexplored. Here, we analyzed 2922 metatranscriptomic samples collected from urban and surrounding environments—including human-dense settings (e.g., transit hubs, hospitals, banks), alongside peri-urban settings — across 102 cities in 31 countries and constructed the Urban & Peri-urban RNA Virus Atlas (UPVAtlas), comprising 54,945 RNA viruses, 77% of which had not been previously observed. Phylogenetic reconstruction based on RNA-dependent RNA polymerases from UPVAtlas greatly expanded the evolutionary diversity of RNA viruses, leading to the identification of two potential candidate phyla, one candidate class, and several unclassified clades. Host association analyses further revealed the ecological complexity of environmental RNA viruses, with the diversity of vertebrate-related and ESKAPE pathogen-related viruses underscoring the importance of continued monitoring of urban environments for tracking RNA viral prevalence and dynamics, with direct relevance to future public health.

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. Recent advances include the discovery of novel viral phyla such as Ambiviricota and the application of AI-driven models like LucaProt, highlighting both the rapid expansion of viral diversity and the growing role of machine learning in RNA virus research. 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 petabyte-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. Importantly, this statement reflects a clear community consensus and provides concrete recommendations to prioritize standardized benchmarking, structure-informed evolutionary analysis, and reproducible virus–host inference as foundational pillars for advancing RNA virus research. 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.

Disparities in formal bioinformatics training exacerbate the global skills gap, impeding the democratized application of advanced genomic technologies. To bridge this divide, we introduce a scalable, hybrid training framework designed to rapidly accelerate regional bioinformatics capacity. We exemplify this approach through the Eastern European Bioinformatics and Genomics (EEBG) workshop series — a cross-disciplinary initiative that pairs international faculty with local institutions to deliver modular, hands-on curricula. Functioning as a structured knowledge-transfer pipeline, the series has catalyzed a sustainable educational ecosystem, evidenced by the establishment of multiple independent summer schools across the region. The assessment of the 2025 EEBG workshop in Kraków, Poland, validates the model’s viability; participant metrics confirm high efficacy in skill acquisition (mean satisfaction: 4.4/5.0) and community building. Crucially, the hybrid delivery mode dismantled geographic barriers, serving as a vital mechanism for maintaining scientific continuity for researchers facing displacement and crisis. Synthesizing these outcomes, we define the core features of a replicable blueprint for scientific readiness in resource-constrained environments. We conclude by presenting a strategic roadmap — organized around infrastructure standardization, governance sustainability, and geographical expansion — for adapting this regional proof-of-concept into a global export-ready model, offering a critical path toward ensuring universal access to genomic innovation.

Wastewater-based genomic surveillance (WWGS) has proven effective for monitoring SARS-CoV-2 and other viruses within communities. It enables rapid detectio

Positive selection is an evolutionary process which increases the frequency of advantageous mutations because they confer a fitness benefit. Inferring the past action of positive selection on protein-coding sequences is fundamental for deciphering phenotypic diversity and the emergence of novel traits. With the advent of genome-wide comparative genomic datasets, researchers can analyze selection not only at the level of individual genes but also globally, delivering systems-level insights into evolutionary dynamics. However, genome-scale datasets are generated with automated pipelines and imperfect curation that does not eliminate all sequencing, annotation, and alignment errors. Positive selection inference methods are highly sensitive to such errors. We present BUSTED-E: a method designed to detect positive selection for amino acid diversification while concurrently identifying some alignment errors. This method builds on the flexible branch-site random effects model (BUSTED) for fitting distributions of dN/dS, with a critical modification: it incorporates an “error-sink” component to represent an abiological evolutionary regime. Using several genome-scale biological datasets that were extensively filtered using state-of-the art automated alignment tools, we show that BUSTED-E identifies pervasive residual alignment errors, produces more realistic estimates of positive selection, reduces bias, and improves biological interpretation. The BUSTED-E model promises to be a more stringent filter to identify positive selection in genome-wide contexts, thus enabling further characterization and validation of the most biologically relevant cases.

RNA viruses are major zoonotic threats driving infectious disease outbreaks across hosts, including humans. However, the ecosystem of RNA viruses in urban areas remains largely unexplored. This study analyzed the 3,326 metatranscriptome samples from 102 cities in 31 countries, including urban built and non-built environments, uncovering 54,945 RNA viral units, 77% of which are previously unseen. Two new phyla were discovered (urv.p.001 and urv.p.002), enhancing our understanding of the RNA virome’s phylogenetic diversity. Our research also supports Duplornaviricota’s polyphyletic nature and identifies 104 amino acid sites in RNA polymerase affecting viral function. A distinct biogeographical pattern of RNA viruses was observed, showing significant clustering of different environmental types, with soil environment being central and indicating its potential role in mediating environmental viral exchange. Interactions between RNA viruses and ESKAPE pathogens, highlighting urban areas as significant reservoirs for RNA viruses. These findings underscore the value of continuous surveillance and mapping of urban environments to track RNA virus prevalence and dynamics for the future of public health.

Positive selection is an evolutionary process which increases the frequency of advantageous mutations because they confer a fitness benefit. Inferring the past action of positive selection on protein-coding sequences is fundamental for deciphering phenotypic diversity and the emergence of novel traits. With the advent of genome-wide comparative genomic datasets, researchers can analyze selection not only at the level of individual genes but also globally, delivering systems-level insights into evolutionary dynamics. However, genome-scale datasets are generated with automated pipelines and imperfect curation that does not eliminate all sequencing, annotation, and alignment errors. Positive selection inference methods are highly sensitive to such errors. We present BUSTED-E: a method designed to detect positive selection for amino acid diversification while concurrently identifying some alignment errors. This method builds on the flexible branch-site random effects model (BUSTED) for fitting distributions of dN/dS, with a critical modification: it incorporates an “error-sink” component to represent an abiological evolutionary regime. Using several genome-scale biological datasets that were extensively filtered using state-of-the art automated alignment tools, we show that BUSTED-E identifies pervasive residual alignment errors, produces more realistic estimates of positive selection, reduces bias, and improves biological interpretation. The BUSTED-E model promises to be a more stringent filter to identify positive selection in genome-wide contexts, thus enabling further characterization and validation of the most biologically relevant cases.