• Fiona Brinkman, Fiona Brinkman, Simon Fraser University

Microbiome analysis is increasingly becoming a critical component of a wide range of health, agri-foods, and environmental studies. I will present case studies showing the benefit of integrating very diverse metadata into such analyses - and also pitfalls to watch out for. The results of one such cohort study will be further presented, illustrating the need for analyses that allow one to flexibly view metadata in the context of microbiome data. The results support the multigenerational importance of “healthy

• Marija Dmitrijeva, Marija Dmitrijeva, Institute of Microbiology and Swiss Institute of Bioinformatics
• Hans-Joachim Ruscheweyh, Hans-Joachim Ruscheweyh, Institute of Microbiology and Swiss Institute of Bioinformatics
• Lilith Feer, Lilith Feer, Institute of Microbiology and Swiss Institute of Bioinformatics
• Kang Li, Kang Li, Institute of Microbiology and Swiss Institute of Bioinformatics
• Samuel Miravet-Verde, Samuel Miravet-Verde, Institute of Microbiology and Swiss Institute of Bioinformatics
• Anna Sintsova, Anna Sintsova, Institute of Microbiology and Swiss Institute of Bioinformatics
• Andrew Abi Younes, Andrew Abi Younes, Institute of Microbiology
• Wolf-Dietrich Hardt, Wolf-Dietrich Hardt, Institute of Microbiology
• Daniel Mende, Daniel Mende, Human Biology Microbiome Quantum Research Center (Bio2Q)
• Georg Zeller, Georg Zeller

Microbial communities are crucial to the health and functioning of diverse ecosystems on Earth. A key step in their analysis is taxonomic profiling, i.e., the identification and quantification of microbial community composition, typically done by comparing environmental samples to reference genome collections. However, species from underexplored ecosystems are poorly represented in public databases, limiting the accuracy of taxonomic profiling tools. Here, we present mOTUs4 and its accompanying online database, accessible at https://motus-db.org/. This resource comprises 2.83 million metagenome-assembled genomes (MAGs) recovered from over 50 environments using a unified genome reconstruction workflow. The MAGs are accompanied by 919,090 genomes from reference databases, totalling 3.75 million prokaryotic genomes. mOTUs4 can profile 124,295 species, expanding taxonomic coverage of underrepresented ecosystems. The associated genomic data can be interactively browsed online and filtered based on taxonomy, mOTUs identifiers, and genome quality metrics; the user-friendly interface minimises the need for programming skills to link profiling results with genomic context. In addition, the output produced by mOTUs4 can serve as a proxy for the number of cells within a sample, allowing its use as a scaling factor for normalising gene counts. This opens the utility of using the profiler output to calculate per cell copy numbers of diverse gene functional groups, such as antimicrobial resistance genes. By improving accuracy and interpretability in taxonomic profiling across diverse ecosystems and standardising quantification of gene functional groups, mOTUs4 offers a scalable approach to microbial community analysis.

• Amine Ghozlane, Amine Ghozlane, Institut Pasteur
• Florence Thirion, Florence Thirion, INRAE
• Florian Plaza Oñate, Florian Plaza Oñate, INRAE
• Franck Gauthier, Franck Gauthier, INRAE
• Emmanuelle Le Chatelier, Emmanuelle Le Chatelier, INRAE
• Anita Annamalé, Anita Annamalé, Institut Pasteur
• Mathieu Almeida, Mathieu Almeida, INRAE
• Stanislav Ehrlich, Stanislav Ehrlich, University College London
• Nicolas Pons, Nicolas Pons, INRAE

The characterization of complex microbial communities is a critical challenge in microbiome research. Metagenomic profiling has advanced to include taxonomic, functional, and strain-level profiling (TFSP) of microbial communities. We present Meteor2, a tool that leverages compact, environment-specific microbial gene catalogues to deliver comprehensive TFSP insights from metagenomic samples. Meteor2 currently supports ten ecosystems, with 63,494,365 microbial genes clustered into 11,653 metagenomic species pangenomes (MSPs).
In benchmark tests, Meteor2 demonstrated strong performance in TFSP, excelling in detecting low-coverage species. It improved species detection sensitivity by at least 45% compared to other tools, such as MetaPhlAn4 and sylph, in human and mouse gut microbiota simulations. For functional profiling, Meteor2 improved abundance estimation accuracy by at least 35% compared to HUMAnN3. Additionally, Meteor2 tracked more strain pairs than StrainPhlAn, capturing an additional 9.8% on the human dataset and 19.4% on the mouse dataset.
In its fast configuration, Meteor2 emerges as one of the fastest available tools for profiling, requiring only 2.3 minutes for taxonomic analysis and 10 minutes for strain-level analysis against the human microbial gene catalogue when processing 10M paired reads — operating within a modest 5GB RAM footprint. We futher validated Meteor2 using a published faecal microbiota transplantation (FMT) dataset, demonstrating its ability to deliver extensive and actionable metagenomic analysis. As an open-source, easy-to-install, and accurate analysis platform, Meteor2 is highly accessible to researchers, facilitating the exploration of complex microbial ecosystems. Meteor2 is available on github (https://github.com/metagenopolis/meteor) and bioconda (bioconda/meteor). A preprint is currently available here (DOI:21203/rs.3.rs-6122276/v1).

• Haitao Han, Haitao Han, Fudan University
• Ziye Wang, Ziye Wang, Nankai University
• Shanfeng Zhu, Shanfeng Zhu, Fudan University

Metagenomic binning is a culture-free approach that facilitates the recovery of metagenome-assembled genomes by grouping genomic fragments. However, there remains a lack of a comprehensive benchmark to evaluate the performance of metagenomic binning tools across various combinations of data types and binning modes. In this study, we benchmark 13 metagenomic binning tools using short-read, long-read, and hybrid data under co-assembly, single-sample, and multi-sample binning, respectively. The benchmark results demonstrate that multi-sample binning exhibits optimal performance across short-read, long-read, and hybrid data. Moreover, multi-sample binning outperforms other binning modes in identifying potential antibiotic resistance gene hosts and near-complete strains containing potential biosynthetic gene clusters across diverse data types. This study also recommends three efficient binners across all data-binning combinations, as well as high-performance binners for each combination.

• Nils Kleinbölting, Nils Kleinbölting, Forschungszentrum Jülich GmbH
• Peter Belmann, Peter Belmann, Forschungszentrum Jülich GmbH
• Benedikt Osterholz, Benedikt Osterholz, Forschungszentrum Jülich GmbH

The metagenome analysis of complex environments with thousands of datasets, such as those available in the Sequence Read Archive, requires immense computational resources to complete the computational work within an acceptable time frame. Such large-scale analyses require that the underlying infrastructure is used efficiently. In addition, any analysis should be fully reproducible and the workflow must be publicly available to allow other researchers to understand the reasoning behind computed results. To address this challenge, we have developed and like to present the Metagenomics-Toolkit, a scalable, data agnostic workflow that automates the analysis of short and long metagenomic reads obtained from Illumina or Oxford Nanopore Technology devices, respectively. The Metagenomics-Toolkit offers not only standard features expected in a metagenome workflow, such as quality control, assembly, binning, and annotation, but also distinctive features, such as plasmid identification based on various tools, the recovery of unassembled microbial community members and the discovery of microbial interdependencies through a combination of dereplication, co-occurrence, and genome-scale metabolic modeling. Furthermore, the Metagenomics-Toolkit includes a machine learning-optimized assembly step that tailors the peak RAM value requested by a metagenome assembler to match actual requirements, thereby minimizing the dependency on dedicated high-memory hardware. While the Metagenomics Toolkit can be executed on user workstations, it also offers several optimizations for an efficient cloud-based cluster execution.

• Rob Knight

• Zhihan Zhou, Zhihan Zhou, Northwestern University
• Weimin Wu, Weimin Wu, Northwestern University
• Harrison Ho, Harrison Ho, University of California
• Jiayi Wang, Jiayi Wang, Northwestern University
• Lizhen Shi, Lizhen Shi, Northwestern University
• Ramana Davuluri, Ramana Davuluri, Stony Brook University
• Zhong Wang, Zhong Wang, Lawrence Berkeley National Laboratory
• Han Liu, Han Liu, Northwestern University

We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e., DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C$^2$LR) strategy. Empirical results on 23 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training.

• Tatiana Gurbich, Tatiana Gurbich, EMBL-EBI
• Germana Baldi, Germana Baldi, EMBL-EBI
• Martin Beracochea, Martin Beracochea, EMBL-EBI
• Alejandra Escobar-Zepeda, Alejandra Escobar-Zepeda, EMBL-EBI
• Varsha Kale, Varsha Kale, EMBL-EBI
• Jennifer Lu, Jennifer Lu, EMBL-EBI
• Lorna Richardson, Lorna Richardson, EMBL-EBI
• Alexander Rogers, Alexander Rogers, EMBL-EBI
• Ekaterina Sakharova, Ekaterina Sakharova, EMBL-EBI
• Mahfouz Shehu, Mahfouz Shehu, EMBL-EBI
• Robert Finn, Robert Finn, EMBL-EBI

The generation of metagenome-assembled genomes (MAGs) has become a routine method for studying microbiomes. With the growing availability of MAGs in public repositories, MGnify, a free platform for metagenomic data assembly, analysis, and archiving, has introduced MGnify Genomes. This resource serves as a hub for systematically organising and annotating publicly available MAGs and isolate genomes into non-redundant, biome-specific catalogues.
The resource includes over half a million genomes and has recently expanded to incorporate eukaryotic genomes in addition to prokaryotic ones. These genomes are sourced from a wide range of biomes, including both host-associated and environmental contexts. Within each biome, genomes are organised into species-level clusters, with the highest-quality genome selected as the representative, prioritising isolate genomes over MAGs. Each representative genome is richly annotated with comprehensive functional information, including antimicrobial resistance. Additional annotations cover biosynthetic gene clusters, carbohydrate metabolism—including polysaccharide utilisation loci, non-coding RNAs, CRISPR, phage sequences, plasmids, and integrative mobile elements.
An open-source Nextflow pipeline is maintained for generating new catalogues and updating existing ones. The platform offers multiple ways to utilise these references: each biome-specific catalogue is accompanied by Kraken2, protein, and gene databases. A fast, k-mer-based search tool is available on the MGnify Genomes website, allowing users to quickly compare their genomes against the reference catalogues. The resource supports a wide range of applications, including the identification of novel genomes, analysis of species-level adaptation across environments, and research in agricultural, environmental, and health and disease fields.

• Johanna von Wachsmann, Johanna von Wachsmann, European Bioinformatics Institute
• John A. Lees, John A. Lees, European Bioinformatics Institute
• Robert D. Finn, Robert D. Finn, European Bioinformatics Institute

The exponential growth of bacterial genomic databases presents unprecedented challenges for researchers, with isolate genomes increasing from 661,405 samples in 2021 to 2,440,377 samples by August 2024, alongside expanding MAG repositories like those provided by MGnify. While removing genome redundancy at species or strain levels is essential for navigating this vast landscape, current gold-standard tools like dRep have become computationally infeasible for datasets exceeding 50,000 genomes - illustrated by the human gut MAG catalogue in MGnify requiring artificial splitting into multiple chunks for processing, risking taxonomic inconsistencies and demanding extensive manual intervention. We present a novel sketching-based clustering approach that dramatically improves scalability while maintaining high biological accuracy. Our method is built on sketchlib.rust (approximately 100× faster than MASH) for sketching genomes and constructing genome similarity networks that effectively partition millions of genomes into species clusters. When benchmarked against dRep on a 1,125-genome dataset, our approach clusters the genomes in just 0.2 CPU hours compared to dRep's 92 CPU hours. More importantly, our method successfully processes 219,000 genomes in only 17.1 CPU hours - a task impossible for dRep. Quality assessment across multiple datasets demonstrates excellent taxonomic coherence, with monophyletic scores >99%. This breakthrough enables researchers to effectively navigate and utilise the unprecedented scale of available bacterial genomic data, facilitating analyses previously considered impracticable or even impossible.