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.
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 metaIVP is introduced, a modular, integrative, and flexible framework designed to systematically manage genome content purification, re-binning, quality assessment, and downstream analyses of viral and non-viral metagenomic contexts that addresses a key gap in metavirome analysis.
Genome annotation is an important step in deriving functional meaning from prokaryotic sequencing data, yet systematic evaluations guiding tool selection are lacking. We present the first large-scale investigation of four prominent open-source annotation tools (Prokka, Bakta, EggNOG-mapper, and PGAP) across 156,033 diverse genomes. This includes Escherichia coli strains for baseline performance, thousands of archaea and bacteria genomes, as well as frameshifted and metagenome-assembled genomes. Bakta excels in annotating high-quality bacterial genomes, while PGAP was better for archaeal genomes and challenging bacterial assemblies, including metagenome-assembled, fragmented, or contaminated samples. For Gene Ontology annotation, PGAP consistently provides broader term coverage, whereas EggNOG-mapper offers more terms per feature. Our findings highlight tool-specific strengths crucial for selecting optimal solutions based on genome quality, taxonomy, and origin (e.g. MAGs). This study provides an evidence-based guide for users and informs future tool development.
Mateusz Jundzill, Martin Hölzer, S. Mangul et al.· Genome Biology· 0 citations
Reproducibility challenges scientific reporting, including metagenomics, where increasingly complex bioinformatics pipelines hinder transparency, comparability, and customization for life science students globally. To address this demanding task, we built an open, interactive and web-based tutorial that guides scholars with basic command-line skills through the detailed development of a validated and reproducible Nextflow metagenomics classification pipeline. As important features, the tutorial emphasizes simplicity, modularity, and containerization, which empowers users with both conceptual understanding and practical implementation skills. Noteworthy, this tutorial provides all the required files, databases, dependencies, software and environment for users to run it without the need of local installation or computational adaptations elsewhere. Finally, by offering a fully reproducible pipeline with a step-by-step developing tutorial, this work aims to lower technical barriers in microbiome bioinformatics and promote best practices in metagenomics data analysis. TaxoFlow is freely available at https://taxoflow.work/.
Background Environmental microbiome research has advanced through three complementary sequencing modalities, targeted 16S rRNA amplicon sequencing, whole-genome shotgun (WGS) metagenomics, and long-read full-length 16S rRNA profiling, each supported by distinct toolsets with heterogeneous outputs, variable configurations, and different levels of reproducibility documentation. Existing pipelines are typically modality-specific, require substantial configuration expertise, or produce outputs that need further custom scripting before standard ecological analyses can begin. This analytical fragmentation introduces avoidable technical variability and complicates cross-study reproducibility and comparability. PUDU addresses this by integrating all three modalities into a single reproducible workflow with simplified configuration, harmonized outputs across classifiers, and direct compatibility with downstream ecological analysis frameworks. Results We present PUDU (Pipeline for Universal Diversity Unveiling), a modular Snakemake workflow that supports amplicon (short-read 16S), shotgun metagenomics (WGS), and long-read 16S analyses from raw reads to standardized outputs for downstream microbial ecology. PUDU performs technology-aware preprocessing and centralized quality control, and integrates established taxonomic approaches, including DADA2 for amplicons, Emu for full-length 16S long reads, and Kraken2/Bracken and Centrifuger for WGS. Across methods, PUDU produces harmonized count and relative-abundance tables at user-defined taxonomic ranks, Krona files, and a standardized Phyloseq-compatible R object to streamline diversity analyses and statistical workflows. PUDU also provides an integrated Shiny interface for metadata-aware alpha/beta diversity, ordination, community composition, and shared-taxa exploration with exportable figures and taxa tables. We demonstrate PUDU on two publicly available environmental datasets spanning rhizosphere WGS and long-read marine sediment 16S, yielding broadly consistent community-level patterns across classifiers (Spearman ρ = 0.936 at phylum level; PERMANOVA R2 = 0.87–0.95) with peak memory below 45 GB on a standard Linux workstation. Conclusion PUDU is an end-to-end, reproducible, and extensible framework that enables standardized taxonomic profiling and ecology-oriented analysis across sequencing modalities. By combining harmonized outputs, Phyloseq interoperability, and an integrated visualization layer, PUDU facilitates reproducible, standardized, and comparable environmental microbiome analysis from raw reads to interpretable ecological insights.
Alejandro Medaglia-Mata, Pablo Rojas-Rodríguez, V. Bystrý et al.· Frontiers in Bioinformatics· 0 citations
Background
Prokaryotic genome annotation is central to comparative genomics, functional interpretation, and hypothesis generation. Established tools such as Prokka and Bakta provide streamlined annotation workflows, but predefined database choices and hierarchical annotation strategies can limit flexibility, especially for non-model organisms or specialized tasks. In contrast, custom pipelines are more adaptable but usually require substantial bioinformatic expertise to build and maintain.
Methods
We developed Prokanota (PROKAryotic ANnOTAtion), a modular workflow combining genomic feature prediction, including coding sequences, RNA genes, and CRISPR loci, with configurable functional annotation and standardized output generation. Prokanota is implemented as a Snakemake-based command-line tool and uses configuration files to define inputs, outputs, and annotation databases. It integrates pyrodigal for coding sequence prediction, pybarrnap for ribosomal RNA prediction, tRNAscan-SE for transfer RNA prediction, and diced for CRISPR loci prediction. Functional annotation is based on user-defined database modules and currently supports pyhmmer, RPS-BLAST, DIAMOND, and MMseqs2 search backends.
Results
Prokanota generates input-derived deterministic feature identifiers and synchronized GFF, TSV, GenBank, nucleotide FASTA, and protein FASTA outputs. Its modular database architecture enables users to combine general-purpose and specialist resources while retaining database-specific top-hit information. In an evaluation of eight bacterial and eight archaeal reference genomes, combining databases increased the proportion of CDS classified as annotated, particularly in Archaea. Combining CDD, KOfam, and arCOG databases yielded the highest annotation coverage for all tested archaeal genomes, with 81–90% of predicted CDS classified as annotated, rendering Prokanota especially useful for the annotation of archaeal genomes.
Conclusions
Prokanota fills a practical gap between fixed, standardized annotation tools and fully custom expert-built workflows. It provides an accessible and reproducible annotation backbone while allowing project-specific databases and evidence layers to be incorporated without modifying workflow code. The software is freely available under the Boost Software License 1 at https://github.com/richardstoeckl/prokanota.
Richard Stöckl, Felix Grünberger, Dina Grohmann· F1000Research· 0 citations
Gene annotation of metagenome-assembled genomes is a critical step in determining the functional potential of microbial communities from environmental samples. However, annotation workflows using tools such as Prokka or Bakta produce per-bin output with 10 to 14 files per bin, making manual review infeasible at scale. Existing tools incompletely aggregate and visualize gene annotation content across an entire metagenomic dataset. Here we present annoreport, a single-script Python tool requiring no external dependencies beyond Python 3.9+ that accepts output from either Prokka or Bakta, automatically detecting the annotation tool used. annoreport produces an interactive web-based report summarizing gene product frequencies, hypothetical protein rates, feature type distributions, and functional gene clustering via UniProt annotation across all bins. Applied to 206 metagenome-assembled genomes from Antarctic soil metagenomes, annoreport identified 603,799 coding sequences with a 47.1% annotation rate and revealed functional categorization in Transport & Membrane, Nucleotide Binding, and DNA Metabolism categories.
Freely available at https://github.com/keplerridge/annoreport under MIT license, via Bioconda (annoreport) and PyPI (annoreport).
Kepler Ridge, Byron J. Adams· Bioinformatics Advances· 0 citations
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