SIB Swiss Institute of Bioinformatics

Respiratory viruses circulate yearly with strain-specific patterns. Although SARS-CoV-2 and Influenza A/B genomic surveillance is well-developed, most respiratory viruses are unevenly monitored, lacking geographical diversity to capture wider population dynamics. Consequently, insights into respiratory virus evolution are limited. We investigated the genetic diversity of these viruses within one country. During the 2023/24 season, we conducted whole-genome sequencing of 1'129 clinical samples using a hybrid-capture protocol. These samples were pre-tested by real-time PCR panels throughout Switzerland. Leveraging publicly-available full-length genomes, we constructed background datasets representative of geographical diversity and built 16 phylogenies covering the diversity of high-quality viral genomes produced from this study. We detected viral genomes in 632 samples, including 55.6% multi-positive infections (n = 352/632), and recovered high-quality genomes in 73% (n = 461/632) of cases, including 3.9% of co-infections (n = 18/461), from 454 PCR-positive and 7 PCR-negative samples. For 56% of samples (n = 634/1129), the hybrid-capture detection result was concordant with the PCR-result (at least one strain detected by PCR was also detected by sequencing, in addition to other viruses). The four most prevalent viruses were Influenza A/H1N1, SARS-CoV-2, RSV-A, and HPIV-3, and their seasonal spread was consistent with national wastewater monitoring and influenza-like illness reports. Swiss viral genomes were representative of the global genomic diversity, with evidence for multiple introductions into Switzerland, and we identified putative Swiss clusters. In this proof-of-concept study, we focus on 3 viruses (Influenza A/H1N1, RSV-A/B, and HPIV-3), and we demonstrate the streamlined implementation of a broad respiratory virus genomic surveillance workflow with an off-the-shelf protocol and publicly-available software. In addition, we highlight additional evolutionary insights that can only be derived from genomic surveillance. Going forward, this dataset will be a useful resource for future investigations into respiratory viral genomic diversity.

Neurological recovery following spinal cord injury (SCI) is commonly studied through changes in high-level descriptors of injury severity, such as the American Spinal Injury Association Impairment Scale (AIS) grade, or total upper and/or lower extremity motor scores. More recently, the analysis of segmental motor scores has attracted interest as it provides a more detailed understanding of the exact location and extent of changes occurring during recovery. We propose to augment the analysis of segmental motor recovery with a qualitative descriptor of local motor score patterns, which is defined for all upper and lower extremity myotomes below the neurological level of injury (NLI) and based on a categorization of the changes along the rostrocaudal motor score sequence. Our hypothesis is that recovery of segmental motor scores depends on the residual function as described by the newly proposed descriptors of local motor score patterns. Using data of 1385 patients from the European Multicenter Study about SCI, we analyze differences in recovery at approximately 6 months after injury between local motor score patterns and find an increased probability of full motor recovery for myotomes associated with increased motor scores in the caudal direction. We further use an aggregated descriptor of motor score patterns focusing on increases of motor scores in the caudal direction as an alternative or complementary feature to the AIS grade in prediction models for segmental motor scores at recovery. We observe equivalent predictive performance as measured by the root mean square error between actual and predicted motor scores below the NLI for models using the same set of features and additionally either the AIS grade (median = 0.79) or local pattern (median = 0.80). This is noteworthy as the definition of local motor score patterns requires only the examination of the 10 key muscles of the International Standards for Neurological Classification of SCI on each side of the body, while the AIS grade can only be reliably determined if extensive sensory testing is performed in addition. These results indicate the potential benefits of considering information inherent to the rostrocaudal sequence of motor scores for a better understanding of motor recovery. Furthermore, it supports the development and use of abbreviated sensorimotor examinations specifically in the early phase after SCI.

Strain structure is a well-documented phenomenon in many pathogenic and commensal bacterial species, where distinct strains persist over time exhibiting stable associations between genetic or phenotypic traits. This structure is surprising, particularly in highly recombinogenic species like Streptococcus pneumoniae, because recombination typically breaks down linkage disequilibrium, the non-random association of alleles at different loci. Recent work suggests that multi-locus negative frequency-dependent selection (NFDS) acts to maintain allelic diversity across bacterial genomes, a pre-requisite for the existence of patterns of linkage disequilibrium. Here, using modeling and genomic analysis, we show that multi-locus NFDS can also shape bacterial strain structure through epistatic effects between these loci. We develop models of two NFDS mechanisms - metabolic niche differentiation and competition-colonisation trade-offs - and show how they can produce epistasis. Notably, both models generate frequency-dependent epistasis. Unlike classical constant sign epistasis, this acts to either reinforce or weaken existing linkage disequilibrium, making observed allele associations contingent on the evolutionary history of the population. We then use a dataset of over 3000 S. pneumoniae genomes to test our model predictions, and make observations consistent with frequency-dependent epistatic effects on gene associations. Our results extend and generalise previous theoretical work on the role of antigen-specific acquired immunity (a diversity-maintaining mechanism) on allele associations. Overall, this work contributes to a better understanding of the evolutionary processes shaping the structure of bacterial populations, which is central to predictive modeling of multi-strain pathogens.

Biodiversity genomics is converging from historically separate approaches — DNA barcoding and reference genome sequencing — into an integrated digital ecosystem driven by shared data stewardship principles: transparent provenance, persistent identifiers and interoperable repositories. We demonstrate how these workflows can operate within a unified informatics architecture spanning data generation, validation, publication and reuse. We describe coordinated infrastructure, including the European BOLD mirror, ERGA Genome Tracking Console and metadata platforms COPO and PlutoF. These systems employ harmonised validation pipelines, shared metadata standards that bridge the Darwin Core and Genomic Standards Consortium vocabularies and automated data exchange amongst ENA, UNITE and GBIF. Workflows in Galaxy, Nextflow and Snakemake are registered in WorkflowHub as Research Object Crates (RO-Crates), ensuring reproducibility and complete provenance. Key outcomes include comprehensive data flow documentation, automated quality control using BUSCO and ERGA Assembly Reports and robust specimen-to-data linkage. We identify challenges in metadata harmonisation, distributed tracking, collaborative attribution and infrastructure sustainability and provide recommendations for addressing them through existing platforms and emerging RO-Crate standards. This work establishes practical foundations for treating biodiversity molecular data as a continuum, demonstrating how FAIR principles can scale to continental initiatives.

Gene signature scoring is integral to single-cell RNA sequencing (scRNA-seq) data analysis, particularly for unsupervised cellular state annotation based on maximum signature score values. However, this application requires robust and comparable score distributions across diverse signatures and experimental conditions. Our systematic evaluation of established scoring methodologies-Seurat, SCANPY, UCell, and JASMINE-across nine healthy and cancer scRNA-seq data sets demonstrates their insufficiency in fulfilling this requirement. To address this limitation, we present Adjusted Neighborhood Scoring (ANS), a deterministic algorithm with enhanced control gene selection that significantly improves score stability and cross-signature comparability, achieving cell-state annotation accuracy comparable to supervised methods. We demonstrate the practical utility of ANS by developing and validating a gene signature to differentiate cancer-associated fibroblasts from malignant cells undergoing epithelial-to-mesenchymal transition. Overall, ANS provides a robust and reliable gene signature scoring framework, significantly improving the accuracy of score-based annotation of cell types and states in single-cell studies.

<ns7:p>The well-being of farmed and companion animals is increasingly recognised as integral to sustainable agroecosystems, companionship, and the One Health approach, which emphasises the interconnected health of people, animals, and the environment. The ELIXIR Domestic Animals Genome and Phenome (DAGP) Community supports genome-to-phenome analyses for farmed and companion animal species. Its aim is to coordinate, discuss, and explore the potential of data technology solutions to address key issues in animal welfare, behaviour, health, infectious diseases, metabolism, nutritional efficiency, and the preservation of genetic diversity and the environment. Through consolidating efforts to develop data standards, coordination, workflows, and visualisation, it will enhance the science underpinning rapidly growing fields in domestic animal genomics, including genome-enabled breeding, population genomics, pangenome analysis, functional genomics, genome editing, paleogenomics, phenotyping, and bio-banking. These standards will adhere to the FAIR data principles and leverage established ontologies to promote best practices in data coordination and archiving. This white paper, prepared by the ELIXIR DAGP Focus Group, summarises the current data infrastructure, resources, and tools available for domestic animal genomics and phenomics, and presents community-led plans and priorities to be implemented to meet the requirements of ELIXIR services and the animal science community. We describe how ELIXIR services can be applied in the domestic animal genomics and phenomics fields, and how we can connect projects and infrastructures that are active in the animal sciences domain. We also discuss three key priority areas: i) expanding the FAANG Data Portal for phenotype data with ELIXIR Data Platforms; ii) supporting submissions of new data types across ELIXIR Core Data Resources , including proprietary data from industry partners; and iii) strengthening connections to existing ELIXIR Communities and international consortia. This article provides a set of priorities for a Domestic Animals Genome and Phenome Community in ELIXIR and outlines the next steps to engage across stakeholders and to consolidate data for domestic animal science in Europe.</ns7:p>

The rapid expansion of precision oncology has led to a marked increase in the number of identified oncodriver genes and associated variants. This surge has elevated the frequency of mutations with unknown functional impact, highlighting the growing need for molecular modeling tools such as Swiss-PO to support variant interpretation for both clinical and research contexts. Swiss-PO was expanded and redesigned to integrate large-scale oncogenomic data with structure-based and sequence-based analytical tools. The platform combines data of oncodriver genes, multiple sequence alignment (MSA) for conservation analysis across orthologs and gene families, experimental and predicted three-dimensional (3D) structures prediction visualization to assess impacts of mutations. Additional modules include a BRAF kinase mutation classification model and a dedicated webpage for ligands targeting proteins, and unified integration with external databases to enable multidimensional analyses. The updated Swiss-PO platform integrates data for nearly 1,500 oncodriver genes, encompassing more than 3 million mutations and post-translational modification annotations, over 26,000 experimental and predicted 3D protein structures, more than 4,000 MSAs , and information on over 200,000 protein ligands. In addition, the platform includes a BRAF kinase mutation class predictor to support therapeutic decision-making, establishing Swiss-PO as one of the most comprehensive publicly available resources for precision oncology. With these enhancements, Swiss-PO strengthens its role as a powerful and versatile resource for oncologists, bioinformaticians, and molecular biologists engaged in the interpretation of cancer-associated mutations and the advancement of precision medicine. The website is available at Swiss-PO.