iMeta

Accurate taxonomic classification in metagenomic studies remains challenging because reference databases are often static and incomplete, limiting our understanding of microbial diversity, especially in habitats that are not well represented. We introduce MetaMAG Explorer, a complete and modular pipeline designed to fill this gap with its unique database augmentation framework. Together with end-to-end features like read preprocessing, assembly, binning, and annotation, MetaMAG also presents an automated method for finding new metagenome-assembled genomes (MAGs), confirming their uniqueness by dereplication against curated repositories, and dynamically adding them to classification databases that are compatible with Kraken2. Additionally, MetaMAG makes it easier to understand data by automatically creating high-quality figures that are ready for publication, allowing results to be quickly included in scientific papers. Evaluated across human, plant, and rumen datasets, MetaMAG recovered 233 MAGs, including 121 high-quality genomes, of which 48 (20%) were novel. Database augmentation increased Kraken2 classification rates and reassigned millions of previously misclassified reads. Beyond the gain in read classification, the database augmentation revealed ecologically important taxa that are consistently present in all samples but previously undetected. By enabling iterative database growth driven by the novel MAGs, MetaMAG offers a scalable, highly reproducible, and extensible solution for truly genome-resolved metagenomics, advancing both microbial discovery and taxonomic classification accuracy.

Microbiome research has expanded globally, yet the Middle East and North Africa (MENA) region remains severely under-represented in international sequencing repositories. Here we present the MENA Microbiome Database, the first systematically harmonized catalog of publicly available metagenomic sequencing data from 24 MENA countries, consolidating 60,126 runs across 51,365 biological samples and 2,373 BioProjects deposited between 2008 and 2026. Records were retrieved from ENA, NCBI SRA, and PubMed, enriched with BioSample and study-level metadata, and classified into microbiome subtypes using a 73-rule keyword-based harmonization framework. Amplicon sequencing accounted for 80.6% of runs, with Illumina platforms dominating at 92.7%. Geographic coverage is highly skewed: Saudi Arabia and Turkey together contribute over half of all records, while five countries (Libya, Syria, Palestine, Yemen, and South Sudan) remain critically under-sampled. Metadata completeness averaged 73.97% under a MIxS-MIMS proxy framework, with geographic coordinates available for fewer than 15% of runs. Ecological analyses revealed that country-level factors significantly structure environmental, animal-associated, and plant-associated microbiomes, but not human-associated microbiomes. Spatial autocorrelation confirmed non-random clustering of sampling effort around Red Sea coastal and eastern Mediterranean hotspots. This open, reproducible resource, comprising harmonized data files, analysis code, and an interactive browsing platform, establishes a foundational infrastructure for regional microbiome science and equitable global comparative studies. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/722303v1_ufig1.gif" ALT="Figure 1000"> View larger version (69K): org.highwire.dtl.DTLVardef@16ebcd3org.highwire.dtl.DTLVardef@12ed2d1org.highwire.dtl.DTLVardef@112b5b1org.highwire.dtl.DTLVardef@156b8a4_HPS_FORMAT_FIGEXP M_FIG C_FIG

Polysaccharide utilization loci (PULs) have been a goldmine for the characterization of novel carbohydrate active enzymes (CAZymes) and the understanding of their synergistic degradation of complex polysaccharides. We collected PUL predictions containing CAZymes from glycoside hydrolase families GH29, GH50 and GH117, expected to participate in marine polysaccharide breakdown. We explored the evolutionary diversity in these families in terms of sequences and PUL composition, based on sulfatases and CAZymes. From 41 selected PULs, more than 400 putative enzymes were produced, purified and screened on a large collection of carbohydrates. We attributed a function to more than 130 enzymes from five sulfatase subfamilies, 29 known CAZymes families and discovered an activity for 4 families previously of unknown function, including an -L-galactosidase structurally and functionally characterized with mutants. Finally, our detailed analysis of the enzymatic synergies in five PULs, two targeting marine polysaccharides and three targeting eukaryotic polysaccharides, by marine and human gut organisms, highlight the efficiency of our exploratory strategy.

Real-time taxonomic classification of nanopore amplicon sequencing data enables rapid insights into microbial communities, with applications in clinical diagnostics, environmental monitoring, and outbreak surveillance. However, bridging the gap between long-read data and interpretable results often requires specialised bioinformatics expertise. There remains a need for integrated, user-friendly software that combines live data acquisition with downstream microbiome analysis. Here we present NANOTAXI, a fully automated Shiny-based GUI for the classification of barcoded 16S rRNA gene sequences generated by Oxford Nanopore sequencing. The platform supports four taxonomic classifiers, integrated with five reference databases, enabling flexible selection of classification strategies based on user requirements and available computational resources. In addition to real-time monitoring, NANOTAXI performs cohort-level analyses, including alpha and beta diversity, ordination, differential abundance testing, and functional inference using PICRUSt2. Validation using barcoded synthetic communities comprising pooled genomic DNA from clinically relevant bacterial species and the ZymoBIOMICS mock community demonstrated that NANOTAXI generated biologically coherent taxonomic and functional profiles. Benchmarking revealed clear trade-offs between computational performance and taxonomic specificity. Emu provided the lowest observed species-level false-positive rate, whereas Kraken2 offered the fastest classification and enabled continuous near-real-time monitoring across all tested databases. NANOTAXI is open source and freely available at https://github.com/Nirmal2310/NANOTAXI under the GPL version 3 license.

Wastewater surveillance has emerged as a critical tool for global epidemiology, yet the functional diversity of wastewater microbiomes remains poorly characterized at the protein level. Here, we present WasteFams, the first comprehensive database dedicated to the systematic exploration of protein families in wastewater metagenomic and metatranscriptomic studies worldwide. Integrating data from 580 metagenomes, 132 metatranscriptomes, and 1,709 reference genomes, WasteFams catalogs 3,887 non-redundant protein families (containing {succeq}100 members) derived from over 105 million predicted proteins. Each protein family is enriched with multi-layered annotations, including AlphaFold3 structural predictions, taxonomic classifications, and biome-specific metadata. To further expand their functional annotation, we integrated deep genomic context analysis to link protein families to Mobile Genetic Elements (MGEs), Biosynthetic Gene Clusters (BGCs), Antibiotic Resistance Genes (ARGs), and CRISPR elements. Accessible through the EnvoFams portal, WasteFams provides a user-friendly interface featuring advanced search capabilities, sequence and structural similarity tools, and interactive visualization modules. As global initiatives increasingly leverage wastewater for public health and environmental insights, WasteFams can serve as a critical resource for discovering novel microbial functions, monitoring resistance mechanisms, and exploring the biotechnological potential of secondary metabolites within wastewater-engineered ecosystems.

Microbiome analysis has become a pivotal tool in understanding the role of microbiota in human health and disease. However, the lack of standardized workflows, together with the limitations of proprietary software solutions, hampers reproducibility and flexibility. Here, we present Mbiome, an open-source, user-friendly and automated workflow designed to streamline amplicon-based microbiome analysis. Built upon QIIME2, Mbiome supports both bacterial (16S rRNA) and fungal (ITS) profiling, and is compatible with raw fastq files generated by Ion Torrent (IT) and Illumina (IL) sequencing platforms. The workflow guides users through an interactive setup process via a simple configuration file, enabling researchers with minimal bioinformatics experience to perform comprehensive analyses without writing code. Once configured, Mbiome automates major steps including quality control, taxonomic assignment, and {beta}-diversity analyses, functional predictions (via q2-metnet), and customizable visualizations and statistical analyses. Mbiome has been validated using real-world datasets from multiple sclerosis research projects, performing a comparison between different microbiome analysis approaches, including 16S hypervariable region reconstruction, amplicon-based strategies, and cross-platform sequencing (IT and IL), as well as against results obtained with Ion Reporter (IR) commercial software. This evaluation demonstrated its versatility and effectiveness across different sequencing platforms. Moreover, Mbiome provided improved flexibility, transparency, and taxonomic resolution compared to IR. By combining accessibility, reproducibility, and cross-platform compatibility, Mbiome lowers the barrier to microbiome data analysis and facilitates high-quality, standardized workflows in both research and applied settings. Mbiome is publicly available at https://github.com/MGorostidi/mbiome.

BackgroundSynthetic microbial communities (SynComs) are essential tools for dissecting the causal mechanisms in host-microbiota interactions. To date, however, SynCom design suffers from a lack of standardization, typically oscillating between arbitrary strain selection and computational pipelines that misalign with experimental design. As microbiome research transitions toward functionally defined community systems with reproducible experimental outcomes, there is a strong need for a user-friendly platform that integrates multi-dimensional genomic and/or biological data into a standardized and tailored SynComs design. ResultsHere, we present SynCom101, a web-based platform that democratizes the design of reproducible, hypothesis-driven SynComs. SynCom101 accommodates diverse input formats including genomic annotations and laboratory-obtained phenotypic traits, allowing users to customize their design criteria with high flexibility. The platform utilizes a parsimony algorithm to ensure computational scalability for large datasets, complemented by an optional correlation-aware mode to account for microbial compatibility and co-occurrence patterns when ecological interactions among strains are available. A core innovation of SynCom101 is its suite of trait-weighting modules, which empowers researchers to strategically guide the selection algorithm toward maximal functional trait coverage, the emulation of natural community architectures, or the enrichment of positively correlated microbial assemblages to enhance community stability. We showcase the functionalities of the platform by in silico design of communities from different datasets, demonstrating its capacity to generate concise, functionally prioritized SynComs aligned with targeted design objectives. ConclusionBy providing a transparent, parameter-documented workflow, SynCom101 ensures that community design is no longer a "black box" but a reproducible scientific record. This platform establishes a necessary standard for in silico community assembly, facilitating the transition from descriptive microbiome studies toward high-throughput, predictive functional screening and cross-study comparability. AvailabilitySynCom101 can be accessed via the web interface (https://syncom101.bioinformatics.nl/). The datasets used for case studies are available on Zenodo (https://doi.org/10.5281/zenodo.18310451). The source code is available at Git (https://git.wur.nl/jiayi.jing/syncom101).

16S rRNA amplicon sequencing is widely used for microbiome profiling, but most methods rely on reference databases of characterized organisms, limiting its accuracy in function prediction for underrepresented environments. We discovered that 16S rRNA k-mer composition carries substantial functional signal: (i) whole-genome k-mer profiles predict genome-encoded functions, and (ii) 16S rRNA k-mer profiles reflect their source genomes composition. Building on these relationships, we developed embeRNA, a neural network framework that predicts functions directly from 16S rRNA k-mer embeddings without requiring taxonomy assignment or phylogenetic placement. embeRNA outputs per-function probability scores, enabling users to tune decision thresholds to balance precision and recall or account for community novelty. In a stringent "novel microbes" benchmark - where all test sequences shared <97% identity with training data - embeRNA outperformed reference-based methods, particularly for hard-to-label functions. Applied to soil metagenomes with paired 16S and whole metagenome shotgun sequencing (WMS) data, embeRNA recovered most WMS-inferred functions and produced abundance profiles strongly correlated with WMS results, attaining better performance than a reference-based approach. Our findings demonstrate that 16S rRNA directly captures functional potential, and 16S amplicon sequencing data can complement WMS-based inference to broaden functional characterization of microbiomes, especially in understudied environments.

Spatial transcriptomic studies of infectious diseases still rely on fragmented data analysis processes. Here, we developed STID, a standardized framework for spatial transcriptomic analysis of infectious diseases that leverages the Seurat ecosystem and incorporates Python-based modules. STID provides an extensible infection-specific data structure and supports a full suite of analyses, such as pathogen background correction, infection-associated spot and niche identification, single-sample niche characterization, and multi-sample comparative and temporal analyses. Moreover, STID is broadly applicable to spatial transcriptomic data from infectious diseases caused by bacteria, viruses, and parasites, and enables systematic characterization of the structural features, cellular composition, molecular functions, and host-pathogen interactions within pathogen-infected and/or host-responsive niches. Overall, STID provides an accessible, reproducible, and extensible framework for analyzing infection-associated spatial transcriptomic data and for dissecting host-pathogen interactions in their native spatial microenvironments. MotivationSpatial transcriptomics technologies have emerged as powerful approaches for dissecting the structural and functional features of spatial microenvironments. However, the current general-purpose tools remain fundamentally inadequate for resolving the spatial heterogeneity of infectious disease samples, where the intricacies of host-pathogen interactions render spatial microenvironments both challenging to dissect and largely inaccessible. Tools tailored to infectious diseases are critically lacking, including those for reducing pathogen-derived background noise, identifying and isolating infection{square}associated spots or niches, dissecting host-pathogen interactions, and supporting systematic multi-sample analyses. We therefore developed STID, a unified framework that integrates standardized workflows and addresses the analytical bottlenecks in spatial transcriptomic analysis of infectious diseases. HighlightsO_LISTID standardizes spatial transcriptomic analysis in infectious diseases C_LIO_LISTID improves pathogen-infected spot detection by correcting pathogen background C_LIO_LISTID distinguishes pathogen-infected and host-responsive niches C_LIO_LISTID supports multi-sample comparative and temporal analyses of niches C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/727492v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@167d351org.highwire.dtl.DTLVardef@1628848org.highwire.dtl.DTLVardef@1e157aforg.highwire.dtl.DTLVardef@143ca1b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Human-associated microbial genomes encode extensive strain-level diversity and niche-specific gene repertoires that are critical to host health. However, these complex sequence features remain difficult to capture using general-purpose DNA foundation models, highlighting the need for dedicated representation learning tailored to the human microbiome. Here, we introduce Genos-m, an open-source foundation model for human-associated microbial genome representation. Genos-m was pretrained on approximately 1.2 trillion nucleotide tokens from a curated microbial genome corpus, including human-associated prokaryotic isolates, high-quality metagenome-assembled genomes (MAGs) and bacteriophages, supplemented with GTDB species-level representative genomes to broaden prokaryotic taxonomic breadth. The model uses a sparsely activated Mixture-of-Experts (MoE) Transformer architecture, with 4.7 billion total parameters, approximately 330 million activated parameters per forward pass and a maximum context length of one million base pairs. We evaluated frozen Genos-m representations across short-sequence and gene-level tasks, biosynthetic gene cluster (BGC)-based regional sequence tasks, whole-genome strain phenotype prediction, and zero-shot transfer on prokaryote-related RNAfitness assays. Across these benchmarks, Genos-m consistently ranked among the leading comparison models, with the best performance in five of eight gene-fitness regression tasks and in BGC type classification. Using sparse autoencoders, we identified sparse features in Genos-m hidden activations that aligned with annotated ORFs, intergenic regions, and tRNA and rRNA loci. In downstream applications, Genos-m-derived genome-informed species representations in-corporated into a human microbiome self-supervised learning model improved colorectal cancer (CRC)-control classification over conventional species-abundance random forest models. Genos-m also generated stable sample-level embeddings from as few as 10,000 metagenomic reads, retaining gut microbial community structure that distinguished geographic origin and aligned with enterotypes defined from full-depth taxonomic profiles. Together, these results support Genos-m as a reusable representation model for microbial genomes and metagenomes, with conclusions bounded by the reported datasets, task definitions and evaluation protocols. Genos-m model weights, inference code, and usage documentation are publicly available on GitHub (https://github.com/BGI-HangzhouAI/Genos-m) and Hugging-Face (https://huggingface.co/BGI-HangzhouAI/Genos-m).

The absence of a scalable system for organizing the vast majority of unidentified species becomes the central obstacle in biodiversity science. Existing molecular and computer-vision methods rely on DNA material or closed-set labels, which hamper biodiversity quantification under the open, incomplete conditions that characterize real ecosystems. Here, we introduce morphOTUs, a general image-based framework that constructs operational units of biodiversity directly from phenotype. Using morphOTU, we derive image-based OTUs across five plant and beetle datasets spanning heterogeneous imaging conditions. These units recover species-level boundaries, retain coherent structure when most species are "unseen" during training, and accurately approximate richness and Shannon diversity indices even under sparse labeling or limited sampling. Visual explanations reveal that morphOTU consistently focuses on biologically meaningful traits and captures continuous phenotypic variation. By providing a scalable and open-set framework for quantifying phenotypic diversity, morphOTUs enable biodiversity assessment that includes unnamed species and unlock the ecological value of rapidly expanding digital image repositories.

Extreme environments, though hostile to most life forms, host specialized extremophile communities that have redefined biological cognition and emerged as vital biotechnological resources, with their unique adaptive traits and bioactive molecules driving advances in multiple scientific and industrial fields. However, research on extremophiles is hindered by limitations in culture-based methods, fragmented multi-omics data with non-uniform annotation standards across repositories, the lack of cross-extreme comparative research in existing resources, and the singularity of data dimensionality that neglects key structural information, all of which restrict the functional interpretation of extremophile microbes and the exploitation of their bioprospecting potential. To tackle these challenges, we developed ExMODE (https://db.genomics.cn/exmode/), a comprehensive multi-omics database platform dedicated to extremophiles. It centrally integrates multi-omics data from diverse extreme habitats with a standardized annotation framework, resolving data fragmentation and enabling systematic cross-environment comparative analyses to elucidate extremophile adaptive mechanisms. Moreover, ExMODE aggregates multi-dimensional datasets including genes, genomes, secondary metabolite sequences and protein structures, overcoming the constraints of single-dimensional data and significantly improving the efficiency of biotechnological resource discovery from extreme microorganisms.

Motivation: Horizontal gene transfer (HGT) shapes bacterial evolution and microbial ecosystems, yet detecting HGT within microbiomes remains a challenge due to fragmented metagenomic assemblies, reference bias, reliance on gene boundaries, and limited ability to model structural mosaicism and patterns across genomes. Methods: We present Kente, a novel pangenome graph-based framework designed for HGT detection that aligns metagenomic assembly contigs to a curated database of >600 genus-level bacterial pangenome graphs constructed using minigraph. Kente infers local taxonomic composition along contigs using alignment evidence and classifies candidate transfers using structured clade-transition topologies (e.g., A-B-A sandwich, open tips, and mosaic patterns). A complementary intra-genus module detects inter-species transfers within a single genus graph using segment-level clade annotations. Results: Across simulated intra- and inter-genus transfer scenarios, Kente achieves higher precision and comparable recall relative to existing gene-centric microbiome HGT detection approaches while reducing false positives from fragmented assemblies. Application to real human gut metagenomes (HMP2, n = 26) demonstrates Kente's ability to detect candidate cross-lineage transfer regions in complex microbial communities. Runtime profiling shows near-linear scaling with input size, enabling efficient analysis of large metagenomic assemblies. Availability and Implementation: https://github.com/treangenlab/Kente

Phylogenomic surveillance is limited not by sequencing throughput, but by the difficulty of converting heterogeneous raw data into reliable evolutionary inference, particularly for low-titer and contaminated viral field samples. Here we present Omni2Tree, an assembly-free framework that reconstructs viral phylogenies directly from raw sequencing reads and generates easily shareable interactive reports and genome-wide entropy profiles to identify diversification. In H5N1 benchmark analyses, Omni2Tree maintained accurate placement and topological stability even under low coverage, unlike assembly or reference based methods. Omni2Tree generated an annotated phylogeny for 64-sample H5N1 field surveillance dataset from the eastern USA in under 3 hours. Omni2Tree recovered known phylogenetic structure and key variability insights across 1,328 hepatitis C virus and 707 human cytomegalovirus datasets, and resolved co-infecting respiratory viruses in clinical metagenomic samples. By enabling direct analysis from raw reads, Omni2Tree supports faster, more portable, and more decentralized phylogenomic surveillance across outbreak, clinical, and resource-limited settings.

Viruses play indispensable roles in ecosystems and human health, yet deciphering their molecular functions remains challenging. Many viral protein annotations are incomplete or poorly characterized. Existing tools typically predict functional categories without linking to verifiable evidence, hindering the credibility of functional interpretation. Here, we present VirProtRAG, a viral protein function annotation framework that integrates information retrieval with evidence-grounded knowledge generation. It introduces three task-adapted components: a hybrid retrieval module combining keyword-based and semantic dense retrieval to maximize literature coverage, synonym-expanded and rank-aware retrieval with reciprocal rank fusion for improved search effectiveness, and literature quality and evidence-oriented re-ranking to enhance reliability and interpretability. Results show that hybrid retrieval strategy performed best, with quality and evidence features further enhancing re-ranking. Compared with direct LLM prompting without retrieved literature, it consistently improves generation performance, underscoring the critical role of external knowledge. Finally, we built a searchable database comprising all 17,484 reviewed Swiss-Prot viral proteins, supporting both sequence- and text-based queries. VirProtRAG introduced 32.53% non-overlapping function annotations beyond existing expert curation, and independently supported 56.34% of sequence-inferred function points with retrieved literature. Case studies further demonstrate its capability to augment and refine the characterization of previously unannotated or poorly understood viral proteins.

Shotgun metagenomic sequencing enables high-resolution profiling of host-associated microbial communities. However, contaminant DNA can substantially distort biological interpretations, especially in low-biomass samples. Here, we introduce Metacontam, a control-free method for species-level decontamination of shotgun metagenomic data. Metacontam integrates blacklist-guided community detection within a species correlation network with average nucleotide identity (ANI) to identify contaminants arising from shared sources. Across diverse low-biomass and mixed-biomass datasets, Metacontam outperformed existing approaches, improving the detection of low-abundance and low-prevalence contaminants while retaining biologically plausible taxa. It also reduces kit-specific biases in skin metagenomes and improves downstream analyses of tissue microbiome data. Together, these results demonstrate that Metacontam enables accurate identification of contaminant taxa across diverse metagenomic datasets, even in the absence of negative controls.

The prevailing taxonomic profiling methods for an environmental sample rely heavily on PCR amplification of SSU ribosomal RNA (rRNA) genes and genome-based reference databases. Identification and extraction of Illumina metagenomics sequencing data are PCR independent but technically challenging in recognition of the SSU rRNA fragments. Here we present Mitag4taxa, a computational pipeline designed for taxonomic profiling of microbial communities from metagenomic Illumina sequencing reads containing rRNA tags (mitag). A Hidden Markov Model (HMM) of SSU rRNA genes and those for the V4 region of 16S rRNA and the V9 region of 18S rRNA genes were created, respectively, using the representative sequences of different families and corresponding hypervariable regions in the SILVA database. The pipeline identifies and extracts 16S and 18S rRNA gene fragments along with the quality score from metagenomic or metatranscriptomic datasets using HMM search integrated with the models. The hypervariable regions, including the V4 region of 16S rRNA and the V9 region of 18S rRNA genes, can be further scanned and recruited for taxonomic classification and biodiversity estimate. To demonstrate its high reliability, the performance of Mitag4taxa was evaluated using both real and simulated datasets. In human gut metagenomic assessments, taxonomic profiles derived from Mitag4taxa showed high consistency with those based on conventional 16S rRNA gene amplicons, identifying dominant families such as Bacteroidaceae and Prevotellaceae with similar relative abundances. Statistical analyses confirmed highly significant positive correlations between Mitag4taxa and amplicon-based community structures. The 18S V9 module was further validated using shotgun metagenomic data from deep-sea sediment cores, successfully recovering key eukaryotic taxa such as Collodaria and Leotiomycetes. Furthermore, benchmarking against the RiboTagger software using CAMI marine simulated datasets revealed that Mitag4taxa achieved a higher average F1 score and lower error metrics. Overall, Mitag4taxa provides a complementary rRNA gene amplicon- and genome-independent strategy for microbial community profiling, enabling improved detection of both prokaryotic and eukaryotic taxa from metagenomic and metatranscriptomic sequencing data.

A significant fraction of the microbial diversity remains unclassified, hindering our understanding of microbial roles in health and ecosystems. State-of-the-art methods like Kraken 2 perform well for taxa that are present in the database. However, their accuracy drops significantly when classifying taxa that are not included. While deep learning has advanced many fields, its applications in metagenomics remain limited, and its full potential has yet to be realized. Here, we present Metaxa, a transformer-based deep learning model designed for the taxonomic classification of long-read Nanopore sequences. Metaxa leverages the sequential context of Nanopore reads, enabling robust classification beyond fixed k-mer profiles. Our results show that Metaxa matches Kraken 2 on in-sample data at both the species and genus levels, and significantly outperforms both Kraken 2 and MetageNN at the genus level on out-of-sample datasets where the species genome is absent from the reference database but a different species from the same genus is present. Furthermore, Metaxa demonstrates strong generalization across different Nanopore chemistries (R9.4.1 and R10.4.1). This work highlights the potential of deep learning models to improve metagenomic classification accuracy, especially in complex or underexplored environments where traditional tools fall short.

Shotgun metagenomics has become a cornerstone of microbiome research, yet the complexity of existing workflows remains a major barrier for life scientists without dedicated bioinformatics support. Manual database setup, detailed sample sheet preparation, and management of software dependencies can make routine analysis difficult and time-consuming. Cross-study comparisons are further hampered by inconsistent processing pipelines, database versions, and profiling strategies, limiting reproducibility and the potential for large-scale meta-analyses. We present OpusTaxa, an open-source Snakemake workflow that provides end-to-end processing of short paired-end shotgun metagenomic data with minimal configuration. Users provide either FASTQ files or Sequence Read Archive accessions; OpusTaxa automatically downloads required databases, performs quality control, removes host reads, and executes taxonomic profiling, metagenome assembly, and functional analysis. All analysis modules can be independently toggled, and per-sample outputs are automatically merged into harmonised, cross-sample tables ready for downstream exploration. Across two public datasets, we demonstrate how OpusTaxa can be used to compare consistency across complementary taxonomic profilers and to estimate microbial load in addition to standard metagenomic workflows. AvailabilityOpusTaxa is freely available at https://github.com/yenkaiC/OpusTaxa. Documentation, test data, and example configurations are included in the repository.

Genome-scale metabolic models (GSMMs) are important aids towards system-level understanding of the metabolic physiology of the gut microbes and for rational microbiome engineering. While large-scale repositories of GSMMs for gut-associated bacteria are available, strain-level variability and the continuous discovery of novel taxa through metagenomics and culturomics underscore the need for scalable, ab initio reconstruction tools. Here, we present CarveMe-GutMicrobes, a client-side framework for rapid reconstruction of metabolic models directly from (meta)genomic input. Building upon the original CarveMe framework, CarveMe-GutMicrobes incorporates an expanded, gut-microbe-centric biochemical database that includes reactions, metabolites, and gene-protein-reaction (GPR) associations curated specifically for Bacteria and Archaea inhabiting the human gut. The tool supports taxonomic restriction of the reference database to improve context-specific accuracy. To test the CarveMe-GutMicrobes and to address the paucity of experimental data for non-model gut taxa, we generated new experimental datasets on metabolite secretion profiles and gene essentiality. CarveMe-GutMicrobes models demonstrated high predictive performance performance against these as well as previously available datasets. By integrating curated resources, extending reaction coverage, and offering new empirical datasets, CarveMe-GutMicrobes provides a scalable platform for high-resolution metabolic reconstruction towards broader adoption of GSMMs in gut microbiome research.