The growth of metagenomics-derived amino acid sequence data has transformed our understanding of protein function, microbial diversity, and evolutionary relationships. However, the vast majority of these proteins remain functionally uncharacterized. Grouping the millions of such uncharacterized sequences with the few experimentally characterized ones allows the transfer of annotations, while the inspection of conserved residues with multiple sequence alignments can provide clues to function, even in the absence of existing functional information. To address the challenges associated with this data surge and the need to group sequences, we present a scalable, open-source, parametrizable Nextflow pipeline (nf-core/proteinfamilies) that generates nascent protein families or assigns new proteins to existing families. The computational benchmarks demonstrated that resource usage scales approximately linearly with input size, and the biological benchmarks showed that the generated protein families closely resemble manually curated families in widely used databases.

Abstract Advances in whole-genome sequencing (WGS) technologies have enabled large-scale recovery of metagenome-assembled genomes (MAGs), providing unprecedented insights into microbial diversity across diverse environments. However, the reconstruction of MAGs remains computationally demanding and methodologically complex, requiring the integration of multiple tools for quality control, assembly, binning, refinement, and annotation. Existing workflows often rely on scripting-based implementations, constrain user-driven modification and stepwise execution, and require advanced expertise in high-performance computing (HPC) system administration, thereby limiting accessibility, reproducibility, and adaptability. Here, we present FAIRyMAGs, a Findable, Accessible, Interoperable, and Reusable (FAIR)-compliant, modular pipeline implemented within the Galaxy platform for the generation and analysis of MAGs. FAIRyMAGs consists of six interconnected workflows covering all major steps of MAG reconstruction, including read preprocessing, host and contaminant removal, assembly, binning, dereplication, and downstream taxonomic and functional annotation. The workflows are accompanied by extensive training material, including tutorials, a learning pathway, FAQs, test datasets and video walk-throughs by domain experts, supporting community adaptation. By leveraging Galaxy’s graphical interface and federated infrastructure, FAIRyMAGs enables users to execute complex analyses on public or private compute resources without requiring local installation or workflow programming expertise. The modular design further supports flexible adaptation, iterative optimization, and seamless integration of new tools contributed by the community. To demonstrate applicability, FAIRyMAGs was applied to four real-world microbiome datasets spanning various host-associated and environmental systems. These analyses revealed substantial variability in MAG recovery, community complexity, and clustering structure, underscoring the importance of flexible workflows adaptable to dataset-specific characteristics. Overall, FAIRyMAGs provides an accessible, extensible, and reproducible framework for genome-resolved metagenomics, reducing technical barriers and enabling methodological innovation through community-driven development within the adaptable Galaxy ecosystem.

The Biodiversity, Food Security, and Pathogens (BFSP) Priority Area has been developing since its launch in 2024, and is this year approaching a consolidation point of the 2024–2026 ELIXIR Scientific Programme. Following the publication of the BFSP Strategy, the four BFSP projects launched in 2025, and the five awarded for a January 2026 start, this workshop is a timely opportunity to reflect collectively on the important milestones that have been achieved so far, the progress towards the BFSP goals outlined in the Strategy, to identify gaps and new opportunities, and to actively shape the next phase of the BFSP Programme within ELIXIR. This 90-minute interactive workshop will bring together participants from multiple ELIXIR Platforms, Communities, Focus Groups and Nodes – ELIXIR members who have been actively contributing to the Priority Area development, and members engaged in open call project work overseen by the BFSP co-leads and ELIXIR Hub. Together, we will use the workshop setting to share progress on the ongoing BFSP projects, reflect on successes and challenges of members’ activities aligned to the BFSP domain, and share updates and the latest developments of the bottom-up driven plans for the finalisation of the 2027–2028 BFSP work programme. Building on past BFSP All Hands Meeting Mini-Symposia, this workshop shall also include presentations from Nodes to share their local BFSP-related activities and promote new collaborations amongst different nodes. There shall also be an interactive panel discussion to collect opinions and suggestions (participants will be asked to add their input to a framework document provided to the audience) from the wider ELIXIR community regarding the current BFSP Strategy, as well as possible ways to review and update the Strategy including wider consultation, potentially beyond ELIXIR. The workshop aligns primarily with the Science and Technology strategic tiers, while also supporting Nodes and People by fostering shared ownership of BFSP priorities and strengthening connections across the ELIXIR ecosystem. Expected outcomes include a consolidated set of community-informed priorities, identified collaboration opportunities, potential key updates to the BFSP Strategy, and actionable input to support the finalisation of the upcoming 2027-2028 BFSP Work Programme. These slides were used for a workshop delivered during the 2026 ELIXIR All Hands Meeting.

The HMMER web server, available at https://www.ebi.ac.uk/Tools/hmmer, provides online access to tools from the HMMER software suite (http://hmmer.org/) for protein analysis using profile hidden Markov models. Users can perform sequence similarity searches against a range of regularly updated protein sequence databases or annotate protein sequences with domains and families using profile HMM libraries from protein family databases. Since the 2018 update, the continued exponential growth of sequence databases has necessitated substantial infrastructural improvements to maintain search performance speed and service reliability. To achieve this, the web interface has been completely reengineered using modern web technologies (JavaScript and React), providing users with an enhanced experience, including session-based search history and streamlined results visualization. The web application programming interface has been rewritten to better support programmatic access with updated endpoints and JSON-based responses. The infrastructure has been redesigned to efficiently handle searches against much larger databases through horizontal scaling and asynchronous job processing. Target database offerings have been updated to reflect current usage patterns and data availability. The HMMER web server is free and open to all users, and there is no login requirement.

The identification of amplicon sequence variants from DNA metabarcoding data is a common method for revealing the taxonomic makeup of environmental samples, and for allowing comparative studies between similar datasets. A significant hurdle to the large-scale calling of amplicon sequence variants from publicly available nucleotide datasets is the heterogeneous presence of primer sequences in reads, the removal of which is a necessary pre-processing step for this form of analysis. Furthermore, as the details of the experimental primers are rarely captured in the metadata associated with the sequence records, there is a need for a method that can automatically infer the presence and identity of primers in sequencing data. In this work, we introduce the PrIMER infereNce TOolkit (PIMENTO), a Python package that uses a dual-strategy approach for identifying primers that are present in sequencing reads to enable their removal, and therefore facilitate amplicon sequence variant calling at scale.

Abstract Metagenomic assembly can be a computationally intensive step in microbiome analysis, with memory requirements that vary widely depending on input data characteristics. In workflow systems like Galaxy and large-scale platforms like MGnify, which run thousands of heterogeneous jobs, inaccurate memory allocation drives job failures and costly retries when underestimated, and reduces throughput when overestimated. Current approaches rely primarily on heuristic rules based on input file size or sample metadata, which often fail to generalize across diverse datasets. In this study, we present a machine learning-based framework for predicting memory requirements of metagenomic assembly using metaSPAdes. We analyzed 300 assembly jobs from diverse biomes and evaluated 18 predictive models using combinations of input file size, biome classification, and sequence-derived k-mer features. K-mer profiles were computed from raw sequencing data and summarized into statistical descriptors capturing sequence complexity and diversity. Model performance was assessed using both conventional regression metrics and a production-oriented cost function that accounts for retry policies and resource waste in high-performance computing environments. Our results show that machine learning models can outperform commonly used heuristics. In particular, models incorporating biome information achieved the best performance and can be tuned to favor conservative predictions that reduce job failure rates. Simpler models based solely on input file size also performed competitively, offering a practical alternative for systems with limited feature availability. When evaluated under realistic workload distributions, predictive approaches reduced total memory waste by several million gigabyte-hours per 1,000 jobs compared to static allocation strategies. These findings demonstrate that data-driven resource prediction can substantially improve efficiency in metagenomic workflows. The proposed framework is adaptable to different computational environments and provides a foundation for integrating predictive resource allocation into large-scale bioinformatics platforms beyond Galaxy.

Multi-omics datasets are an increasingly prevalent and necessary resource for achieving scientificadvances in microbial ecosystem research. However, they present twin challenges to researchinfrastructures: firstly the utility of multi-omics datasets relies entirely on interoperability ofomics layers, i.e. on formalised data linking. Secondly, microbiome derived data typically leadto computationally expensive analyses, and so rely on the availability of high performancecomputing (HPC) or cloud infrastructures. These challenges can be better met by combining the resources of multiple groups, services and infrastructures. In this BioHackathon Europe 2024 project, we envisioned a "federated microbiome analysis service" and worked on three tracks of development towards it: mapping metagenomics metadata standards to Schema.org and Bioschemas terms, rendering Nextflow workflow executions as RO-Crates, and tooling for creating, viewing and interlinking human-readable RO-Crate previews.

The calling of amplicon sequence variants from DNA metabarcoding data is a common method of revealing the taxonomic makeup of environmental samples. A significant hurdle to the large-scale calling of amplicon sequence variants from publicly available nucleotide datasets is the presence of primer sequences in reads, the removal of which is a necessary pre-processing step for this form of analysis. Further, as the details of which primers were used is rarely associated with the sequence records, there is a need for a method that can automatically infer the presence and identity of primers in sequencing data. In this work, we introduce PIMENTO, a Python package which uses a dual-strategy approach for identifying primers that are present in sequencing reads to enable their removal, and therefore facilitate amplicon sequence variant calling at scale. ### Competing Interest Statement The authors have declared no competing interest. European Union, https://ror.org/019w4f821, 101094227, 101112823 European Bioinformatics Institute, https://ror.org/02catss52

SUMMARY:In recent years, there has been a surge in prokaryotic genome assemblies, coming from both isolated organisms and environmental samples. These assemblies often include novel species that are poorly represented in reference databases creating a need for a tool that can annotate both well-described and novel taxa, and can run at scale. Here, we present mettannotator-a comprehensive, scalable Nextflow pipeline for prokaryotic genome annotation that identifies coding and noncoding regions, predicts protein functions, including antimicrobial resistance, and delineates gene clusters. The pipeline summarizes these results in a GFF (General Feature Format) file that can be easily utilized in downstream analysis or visualized using common genome browsers. Here, we show how it works on 200 genomes from 29 prokaryotic phyla, including isolate genomes and known and novel metagenome-assembled genomes, and present metrics on its performance in comparison to other tools. AVAILABILITY AND IMPLEMENTATION:The pipeline is written in Nextflow and Python and published under an open source Apache 2.0 licence. Instructions and source code can be accessed at https://github.com/EBI-Metagenomics/mettannotator. The pipeline is also available on WorkflowHub: https://workflowhub.eu/workflows/1069.

The use of 16S rRNA metabarcoding for functional prediction is limited by several biases. Shallow shotgun sequencing is a cost-effective and taxonomically high-resolution alternative to 16S rRNA metabarcoding, but the low sequencing depth limits functional inference. Our BioSIFTR tool maps shallow shotgun sequencing reads against single-biome databases and extrapolates into their precalculated functional profiles. We used three datasets from red junglefowl, mice, and human gut, containing matched deep shotgun metagenomic and 16S rRNA metabarcoding data for taxonomic and functional benchmarking. An additional human gut deep shotgun sequencing data set was subsampled to 1 M reads and analysed with BioSIFTR to replicate previously obtained results. BioSIFTR taxonomic and functional profiles closely agree with the results of the full deep sequencing data in all biomes. We also replicated differences in the human gut microbiome between high and low trimethylamine N-oxide producing participants, using only < 2 % of the original deep sequencing data. The BioSIFTR tool is a powerful approach which approximates the functional information of a deep-sequenced metagenome while using only a fraction of the data. Shallow shotgun sequencing combined with BioSIFTR could be a stand-in replacement for 16S rRNA metabarcoding with an increased taxonomic and functional resolution, and lower bias. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. European Union’s Horizon 2020 research and innovation programme, 952914 The Research Council of Finland, 338818 Finnish Cultural Foundation, https://ror.org/027xav248, 210944 Medical Research Council, https://ror.org/03x94j517, MC\_PC\_21045 [1]: pending:yes

Ensembl (www.ensembl.org) is an open platform integrating publicly available genomics data across the tree of life with a focus on eukaryotic species related to human health, agriculture and biodiversity. This year has seen a continued expansion in the number of species represented, with >4800 eukaryotic and >31 300 prokaryotic genomes available. The new Ensembl site, currently in beta, has continued to develop, currently holding >2700 eukaryotic genome assemblies. The new site provides genome, gene, transcript, homology and variation views, and will replace the current Rapid Release site; this represents a key step towards provision of a single integrated Ensembl site. Additional activities have included developing improved regulatory annotation for human, mouse and agricultural species, and expanding the Ensembl Variant Effect Predictor tool. To learn more about Ensembl, help and documentation are available along with an extensive training program that can be accessed via our training pages.

The FAIRyMAGs project aims to optimize metagenomics assembled genomes (MAGs) building by developing an end-to-end workflow through Galaxy, incorporating FAIR principles. The project addresses challenges in metagenomics research, including technically demanding workflows and high computational requirements, and will provide a reusable and accessible workflow for analyzing microbial communities. The project's objectives include finalizing the workflow, creating training materials, evaluating its performance on real data, and developing a tool for estimating resource allocation, with the goal of advancing scientific knowledge and supporting decision-making in biodiversity conservation, food security, and pathogen management.

Resolving the microbiome of the Atlantic salmon Salmo salar gut is challenged by a low microbial diversity often dominated by one or two species of bacteria, and high levels of host contamination in sequencing data. Nevertheless, existing metabarcoding and metagenomic studies consistently resolve a putative beneficial Mycoplasma species as the most abundant organism in gut samples. The remaining microbiome is heavily influenced by factors such as developmental stage and water salinity. We profiled the salmon gut microbiome across 540 salmon samples in differing conditions with a view to capture the genomic diversity that can be resolved from the salmon gut. The salmon were exposed to 3 different nutritional additives: seaweed, blue mussel protein and silaged blue mussel protein, including both pre-smolts (30-60 g salmon reared in freshwater) as well as post-smolts (300–600 g salmon reared in saltwater). Using genome-resolved metagenomics, we generated a catalogue of 11 species-level bacterial MAGs from 188 input metagenome assembled genomes, with 5 species not found in other catalogues. This highlights that our understanding of salmon gut microbial diversity is still incomplete. A prevalent bacterial genome annotated as Mycoplasmoidaceae is present in adult fish, and a comparison of functions revealed significant sub-species variation. Juvenile fish have a different microbial diversity, dominated by a species of Pseudomonas aeruginosa. We also present the first viral catalogue for salmon including prophage sequences which can be linked to the bacterial MAGs.

Biological language model performance depends heavily on pretraining data quality, diversity, and size. While metagenomic datasets feature enormous biological diversity, their utilization as pretraining data has been limited due to challenges in data accessibility, quality filtering and deduplication. Here, we present the Open MetaGenomic (OMG) corpus, a genomic pretraining dataset totalling 3.1T base pairs and 3.3B protein coding sequences, obtained by combining two largest metagenomic dataset repositories (JGI's IMG and EMBL's MGnify). We first document the composition of the dataset and describe the quality filtering steps taken to remove poor quality data. We make the OMG corpus available as a mixed-modality genomic sequence dataset that represents multi-gene encoding genomic sequences with translated amino acids for protein coding sequences, and nucleic acids for intergenic sequences. We train the first mixed-modality genomic language model (gLM2) that leverages genomic context information to learn robust functional representations, as well as coevolutionary signals in protein-protein interfaces and genomic regulatory syntax. Furthermore, we show that deduplication in embedding space can be used to balance the corpus, demonstrating improved performance on downstream tasks. The OMG dataset is publicly hosted on the Hugging Face Hub at https://huggingface.co/datasets/tattabio/OMG and gLM2 is available at https://huggingface.co/tattabio/gLM2_650M.

Backgroun : Plant-associated microbiotas play a key role in plant health, growth, and stresses resilience. These microbiotas are considered the second plant genome by expanding its genetic potential. One of the main components of the plant microbiota is the endophytic bacterial communities, which live in the plant's internal tissues. The main sources of this microbiota are the seed and the soil where the plant grows. Particularly, soil bacteria are attracted by different signals present in plant root exudates, to later colonize the rhizoplane and infect internal tissues. Although the colonization and infection processes of endophytic bacteria are well documented, the molecular bases of the mechanisms involved in the plant-endophyte interaction are still poorly understood. Previously it was shown that strain Kosakonia radicincitans UYSO10 promotes the growth of sugarcane plants and was defined as a true endophyte. Moreover, it was demonstrated that the biological nitrogen fixation process is involved in the plant growth promotion observed. The aim of this work is to expand the knowledge about the possible mechanisms involved in the early stages of the interaction between the diazotrophic endophytic strain UYSO10 and sugarcane plants. Methodology : a proteomic approach was conducted in the strain UYSO10 exposed or not to sugarcane exudates. Results showed that in the presence of root exudates, strain UYSO10 senses the environment and adapts its proteome to transport and metabolize different nutrients, and to interact with the host plant. These results deepen the knowledge of the potential mechanisms involved in the early stage of plant-bacteria endophyte interaction.

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.

BACKGROUND:Genomic Observatories (GOs) are sites of long-term scientific study that undertake regular assessments of the genomic biodiversity. The European Marine Omics Biodiversity Observation Network (EMO BON) is a network of GOs that conduct regular biological community samplings to generate environmental and metagenomic data of microbial communities from designated marine stations around Europe. The development of an effective workflow is essential for the analysis of the EMO BON metagenomic data in a timely and reproducible manner. FINDINGS:Based on the established MGnify resource, we developed metaGOflow. metaGOflow supports the fast inference of taxonomic profiles from GO-derived data based on ribosomal RNA genes and their functional annotation using the raw reads. Thanks to the Research Object Crate packaging, relevant metadata about the sample under study, and the details of the bioinformatics analysis it has been subjected to, are inherited to the data product while its modular implementation allows running the workflow partially. The analysis of 2 EMO BON samples and 1 Tara Oceans sample was performed as a use case. CONCLUSIONS:metaGOflow is an efficient and robust workflow that scales to the needs of projects producing big metagenomic data such as EMO BON. It highlights how containerization technologies along with modern workflow languages and metadata package approaches can support the needs of researchers when dealing with ever-increasing volumes of biological data. Despite being initially oriented to address the needs of EMO BON, metaGOflow is a flexible and easy-to-use workflow that can be broadly used for one-sample-at-a-time analysis of shotgun metagenomics data.

The MGnify platform (https://www.ebi.ac.uk/metagenomics) facilitates the assembly, analysis and archiving of microbiome-derived nucleic acid sequences. The platform provides access to taxonomic assignments and functional annotations for nearly half a million analyses covering metabarcoding, metatranscriptomic, and metagenomic datasets, which are derived from a wide range of different environments. Over the past 3 years, MGnify has not only grown in terms of the number of datasets contained but also increased the breadth of analyses provided, such as the analysis of long-read sequences. The MGnify protein database now exceeds 2.4 billion non-redundant sequences predicted from metagenomic assemblies. This collection is now organised into a relational database making it possible to understand the genomic context of the protein through navigation back to the source assembly and sample metadata, marking a major improvement. To extend beyond the functional annotations already provided in MGnify, we have applied deep learning-based annotation methods. The technology underlying MGnify's Application Programming Interface (API) and website has been upgraded, and we have enabled the ability to perform downstream analysis of the MGnify data through the introduction of a coupled Jupyter Lab environment.

MGnify is EMBL-EBI's metagenomics resource. MGnify's recently launched Notebook Server provides an online Jupyter Lab environment for users to explore programmatic access to MGnify's datasets using Python or R. Here, we report several developments to the Notebook Server completed during the BioHackathon Europe 2022. The developments range from establishing an instance of the notebooks on the Galaxy platform, to adding new notebooks and Jupyter UI extensions enabling more users to perform downstream analysis tasks on MGnify's extensive metagenomics datasets.

An increasingly common output arising from the analysis of shotgun metagenomic datasets is the generation of metagenome-assembled genomes (MAGs), with tens of thousands of MAGs now described in the literature. However, the discovery and comparison of these MAG collections is hampered by the lack of uniformity in their generation, annotation and storage. To address this, we have developed MGnify Genomes, a growing collection of biome-specific non-redundant microbial genome catalogues generated using MAGs and publicly available isolate genomes. Genomes within a biome-specific catalogue are organised into species clusters. For species that contain multiple conspecific genomes, the highest quality genome is selected as the representative, always prioritising an isolate genome over a MAG. The species representative sequences and annotations can be visualised on the MGnify website and the full catalogue and associated analysis outputs can be downloaded from MGnify servers. A suite of online search tools is provided allowing users to compare their own sequences, ranging from a gene to sets of genomes, against the catalogues. Seven biomes are available currently, comprising over 300,000 genomes that represent 11,048 non-redundant species, and include 36 taxonomic classes not currently represented by cultured genomes. MGnify Genomes is available at https://www.ebi.ac.uk/metagenomics/browse/genomes/.