iMeta

Precise identification depends on highly resolved classification. Therefore, whole genome-based strain typing schemes have been implemented for identification of the most important microbial human pathogens. However, there are no dedicated typing schemes for the majority of prokaryotic human, animal, and plant pathogens, or for non-pathogenic microbes, often limiting their identification to taxonomic classification tools that have the species as the smallest unit. To address this deficiency, we created the genomeRxiv web server which contains over 350,000 genomes and leverages the average nucleotide identity (ANI)-based Life Identification Number (LIN) approach, allowing users to identify prokaryotic genomes in real time from approximately genus-rank to near strain-level resolution. LINs are overlaid with circumscriptions of species and infraspecies classes, integrating taxonomy with strain typing. Metadata and distance trees are visualized to complement inferred phylogenetic relationships with epidemiological and ecological information. A command line tool is provided for users to upload k-mer sketches of unpublished genomes and associated metadata, facilitating epidemiological investigations without the need to share complete DNA sequences. In conclusion, genomeRxiv constitutes a much-needed resource for precise, rapid identification of the many prokaryotes not included in any pathogen-specific typing scheme.

Meta2Data is a modular, largely automated pipeline implemented in Python and Bash for the retrieval, standardization, and processing of public microbiome sequencing data. It retrieves metadata from major public repositories, including INSDC and CNCB, using user-provided keywords or BioProject/BioSample identifiers. Retrieved metadata are standardized using a synonym dictionary and then subjected to manual curation. Based on the curated metadata, Meta2Data downloads the corresponding raw sequencing data and processes them using standardized workflows based on DADA2 or VSEARCH. The pipeline supports both single- and multiple-target-region datasets and produces integrated feature tables, taxonomic assignments, and phylogenetic trees for downstream microbial community analyses. Availability and ImplementationMeta2Data is freely available at https://github.com/LinyangSun/Meta2Data and archived on Zenodo at https://doi.org/10.5281/zenodo.20670433

High-throughput sequencing (HTS) has revolutionized virosphere exploration. However, characterizing highly divergent viral sequences remains a bottleneck known as Viral Dark Matter (VDM). Numerous tools were developed to unravel such diversity, but they usually require complex prior HTS data analysis processes. Consequently, a common bottleneck to VDM exploration is the manual, chained execution of complex command-line applications. To address the need for automated and scalable viral discovery, we developed DeepVir, a reproducible Snakemake pipeline that integrates classical homology-based alignments with profile Hidden Markov Model (HMM) mining of the RNA-dependent RNA polymerase (RdRp). To validate the pipelines efficacy, we analyzed 385.23 GB of publicly available transcriptomic data (203 Sequence Read Archive libraries) from 49 American bat species. DeepVir successfully identified 179 distinct viral groups. This included 903 contigs spanning nine known viral families, enabling the characterization of novel genomes within Orthomyxoviridae (Influenza A H7N9), Picornaviridae, Alphaflexiviridae, Retroviridae (Spumaretrovirinae), Papillomaviridae, Herpesviridae, and Adenoviridae. Furthermore, the pipeline uncovered 170 putative novel VDM lineages. By employing deep homology searches and Sequence Similarity Network (SSN) visualization, we contextualized these highly divergent VDM sequences, revealing significant evolutionary relationships with the orders Mononegavirales and Bunyavirales. Notably, human-driven curation of the pipelines outputs allowed for the cross-library assembly of the first putative exogenous Spumavirus in the Americas. Ultimately, by automating complex bioinformatic processing steps, scalable pipelines like DeepVir empower researchers to prioritize the biological and epidemiological interpretation of their findings, accelerating the characterization of wildlife virospheres and enhancing pathogen genomic surveillance.

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 Galaxys 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.

Species-resolved profiling of microbial functional genes is important for linking microbial community composition to biological function, but existing functional profiling approaches are not designed to systematically define user-specified genes and resolve their contributing species. We developed Vritra (Versatile gene-guided Reads-identification with Impartial Taxonomic Refinement and Assignment), a framework for constructing gene-specific reference databases for metagenomic and metatranscriptomic data. Vritra expands and refines the UniRef-based sequence space for user-specified target genes using sequence-similarity network connectivity and functional annotations, retains related homologs as decoys to reduce assignment ambiguity, and links the resulting sequences to standardized microbial taxonomy. Across genes involved in oxalate, urate, and bile acid metabolism, Vritra recovered >97% of sequences represented by established annotation resources for most evaluated genes while substantially expanding the represented sequence space. In real microbiome datasets, the expanded references increased recovery of target-gene reads by up to threefold for poorly annotated genes while maintaining high sequence identity. Application to population-based and publicly available microbiome datasets enabled species-resolved profiling of functional genes and revealed gene-specific associations with microbial taxa, dietary factors, and disease-related phenotypes. Vritra provides a scalable framework for translating continuously expanding sequence resources into gene-specific, species-resolved references for microbiome studies.

Public sequencing repositories contain large amounts of gut microbiome data that could support cross-study comparison, reproducibility analysis, and microbiome foundation model development. However, the extent to which these data are structured, harmonized, and reusable at archive scale remains unclear. Here, we characterized publicly available gut microbiome sequencing metadata from the NCBI Sequence Read Archive using Google BigQuery, focusing on human gut metagenome, mouse gut metagenome, and broadly annotated gut metagenome records. We evaluated temporal growth, sequencing depth, BioSample and BioProject structure, platform and instrument use, metadata completeness, host attribution, publication linkage, and research themes from linked literature. Public gut microbiome data increased substantially over time and were dominated by human-associated datasets and Illumina sequencing platforms. Core technical metadata fields were highly complete, but biological context needed for reuse, including host identity, phenotype, study design, and disease status, was often inconsistently encoded or required recovery from BioSample attributes and linked publications. In the generic "gut metagenome" cohort, host identity could be assigned for only 13.00% of BioSamples, highlighting the limitations of broad organism annotations for automated cohort construction. Publication linkage was also incomplete at the archive level, although usable text was recovered for most linked publications. Topic modeling of SRA-linked literature showed persistent emphasis on core gut microbiota composition and increasing representation of human cohort and infant microbiome studies. Overall, these findings show that public gut microbiome data are extensive and technically rich but not uniformly analysis ready. Improved metadata harmonization, publication linkage, and biological context recovery will be necessary to support reliable large-scale reuse and AI-ready microbiome data resources.

PanGBank (https://pangbank.genoscope.cns.fr) is a comprehensive open-access database providing precomputed prokaryotic pangenomes at a broad taxonomic scale. Built upon PPanGGOLiN partitioned pangenome graphs, PanGBank addresses the growing need for large-scale comparative genomics through a standardized, regularly updated, and fully accessible resource. The initial release comprises two complementary collections covering more than 4,600 prokaryotic species from the Genome Taxonomy Database (GTDB), encompassing over 393,000 genomes: GTDB all, maximizing taxonomic and environmental diversity through the inclusion of MAGs and SAGs, and GTDB refseq, focusing on high-quality, annotation-rich genomes. Each species-level pangenome integrates graph-based statistical partitions into persistent, shell, and cloud gene families, together with regions of genomic plasticity (panRGP) and co-localized functional modules (panModule). PanGBank offers multiple access modes, including a REST API, a command-line interface (PanGBank-cli), and an interactive web interface. By combining large-scale pangenome resources with advanced graph-based analyses, PanGBank provides a scalable framework for exploring microbial diversity, genome evolution, functional variation, and the dissemination of adaptive traits across prokaryotic populations, as illustrated by a use case on Acinetobacter baumannii pangenome investigating the distribution and evolution of antimicrobial resistance determinants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/742796v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@186b88dorg.highwire.dtl.DTLVardef@1be33d0org.highwire.dtl.DTLVardef@3bc596org.highwire.dtl.DTLVardef@292401_HPS_FORMAT_FIGEXP M_FIG C_FIG

The human gut microbiome plays a central role in host physiology and disease, yet metagenomic analysis pipelines remain fragmented across sample preparation, taxonomic classification, and clinical interpretation stages, complicating reproducibility and translational use. Here we present GTX-GUT, a fully automated, containerized Snakemake pipeline for 16S rRNA gut microbiome profiling that integrates quality control, taxonomic classification (QIIME2/DADA2 against Greengenes 13.8), diversity and compositional metrics benchmarked against a curated healthy reference population, enterotype classification, a clinical association module spanning 11 disease categories, and automated natural-language report generation. We validated the pipeline using the ZymoBIOMICS mock community, showing that BBDuk preprocessing substantially reduced genus-level quantification error (Mean Absolute Error reduced from 7.34 to 1.58 percentage points; Pearsons r improved from 0.576 to 0.833). Application to a human sample from a patient with type 2 Diabetes Mellitus recovered a dysbiotic signature consistent with the literature, including reduced Firmicutes abundance, elevated Bacteroidetes and Proteobacteria, and a predominance of clinical associations within metabolic and gastrointestinal categories. These results demonstrate that GTXGUT provides a reproducible, end-to-end framework linking raw sequencing data to clinically interpretable output, with direct applicability to research and translational microbiome studies.

Long-read sequencing advances metagenomics by producing highly contiguous assemblies and more complete metagenome-assembled genomes (MAGs). However, current long-read metagenomic binners fail to incorporate the rich information of long-read assemblies into representation learning and exhibit limited performance on complex datasets. Here, we show that a higher proportion of long-read assembled contigs contain single-copy genes (SCGs) and more SCGs per contig. Therefore, we developed SCGBinner, which leverages SCG-guided contrastive learning to exploit the advantage of long-read data for learning high-quality contig embeddings. SCGBinner consistently outperforms other binning methods across five simulated and seven real-world long-read datasets, especially on real-world high-diversity samples. For a deep agricultural soil metagenome, SCGBinner recovered 71% more high-quality MAGs and 38% more near-complete MAGs than the second-best method. Notably, SCGBinner uniquely recovered 449 novel high-quality species, which shed light on the predicted ecological roles of 65 uncharacterised families and 93 novel genera. Overall, SCGBinner could harness the potential of long-read sequencing to provide unprecedented insights into the microbial dark matter of complex microbial communities.

Tuberculosis remains a major global public health threat. While whole-genome sequencing has transformed our understanding of the causative agent, Mycobacterium tuberculosis (MTB), existing genomic databases are highly fragmented and often underrepresent structural variations (SVs). Furthermore, critical population-genetic statistics are rarely integrated with phylogenetic and geographic context, forcing researchers to reconcile separate datasets manually. To address this gap, we developed TBpop (https://tbpop.chinacdc.cn), an open-access, integrated population genomics portal. TBpop is built from 420 clinical MTB isolates selected from the first national drug resistance baseline survey in China. The portal integrates isolate metadata, pangenome categories, SNPs, SVs, IS6110 insertion sites, strain phylogeny, and gene-level statistics, and provides three interactive explorer modules: the Population Explorer, the Statistics Explorer, and the Variation Explorer. Additionally, a User Analysis module allows researchers to run population genetic workflows on their own alignments. TBpop provides an integrated platform for exploring genome plasticity, signatures of positive selection, and conservation patterns of functionally important genes in MTB.

Marine sponges are known sources of bioactive natural products (NPs), many of which are produced by associated bacterial symbionts via encoded biosynthetic gene clusters (BGCs). A particularly interesting subclass of sponge-derived NPs is comprised of small, brominated alkaloids, which are recovered from diverse habitats and host sponge taxonomies. Despite having been described decades ago, most of these NPs do not have an elucidated biosynthetic origin. We queried metagenomes of several sponge species by making use of a minimal set of core enzymes that we postulate to be necessary to produce these small peptidic NPs: an FADH2-dependent halogenase and an AMP-binding adenylation enzyme. This revealed a variety of novel BGC architectures, many of which showed conservation among sponge host phylogenies and were encoded in the genomes of diverse sponge-associated bacteria. Furthermore, we identified a BGC in the sponge G. barretti that is potentially linked to the production of the iconic barettins, given its enzymatic machinery and specific acidobacterial origin. The present work contributes to the challenging quest to link orphan brominated NPs to their parent BGCs in the sponge holobiont and beyond.

Metagenomic analyses can be performed at multiple analytical levels, including read-based, assembly-based, and genome-resolved approaches, each capturing complementary biological information while introducing distinct analytical biases and trade-offs. However, existing workflows are commonly optimized for a single analytical strategy, making it difficult to integrate these complementary representations within a unified, reproducible framework. Here we present Geomosaic, a modular framework that integrates complementary analytical representations of metagenomic data, from reads to genomes, within a single scalable, customizable, and reproducible workflow. Built on a graph-based architecture implemented in Snakemake, Geomosaic enables users to construct complete end-to-end workflows or execute individual analytical modules while selecting among interchangeable software packages. The framework supports read preprocessing, quality control, taxonomic and functional profiling, assembly, genome reconstruction, genome-resolved annotation, custom HMM-based analyses, and automated downstream result aggregation. Automatic generation of execution scripts, modular workflows, and multiple analysis entry points make Geomosaic accessible to researchers approaching metagenomic analyses for the first time, while providing the flexibility and control required by expert users. Native support for HPC environments enables efficient analysis of datasets ranging from individual projects to large-scale metagenomic surveys. Rather than treating read-, assembly-, and genome-resolved metagenomics as alternative analytical strategies, Geomosaic integrates them as complementary representations of the same biological system, allowing users to move seamlessly between community-wide patterns and organism-resolved functional interpretation. By combining workflow flexibility, computational reproducibility, standardized analysis-ready outputs, and extensive documentation, Geomosaic provides a unified platform for environmental metagenomic analyses and facilitates reproducible downstream ecological and evolutionary investigations.

Recent efforts have demonstrated that systematic analysis of the millions of metagenomes in the Sequence Read Archive (SRA) enables groundbreaking biological discoveries. However, SRA-scale comparison of whole metagenomes has remained elusive. Here, we introduce the most comprehensive resource to date for experiment-to-experiment analysis of all SRA metagenomes, comprising FracMinHash sketches (470GB), hypergen sketches (19GB), and an all-pairs Jaccard similarity matrix (90GB), which we release online at https://github.com/RolandFaure/SRA-metagenome-sketches-databases. These resources enable a wide range of analyses, which we demonstrate with several applications. First, we use the FracMinHash sketches to uncover temporal trends, finding that the amount of newly discovered microbial sequence continues to grow, rather than saturate or shrink. Second, we use the hypergen sketches, which enable nearest-neighbor search, to identify co-assembly candidates and thereby improve assembly quality. Third, we use the similarity matrix to analyze all SRA metagenomes, identifying duplicated submissions and over 50K candidates for metadata correction. Together, our resources unlock systematic analysis of metagenomic data at unprecedented scale.

Large-scale metagenomic studies increasingly require taxonomic profiles that are sensitive, precise, strain-resolved and computationally tractable. Existing profilers typically trade taxonomic breadth, sensitivity, precision and speed against one another, limiting their utility for high-resolution microbiome analyses. Here we present protal -- profiling through alignment -- an ultra-fast alignment-based method for species- and strain-resolved profiling of metagenomes. Protal combines a newly developed alignment algorithm, machine-learning-based classification and conserved bacterial marker genes to profile species represented in the standardized and regularly updated GTDB taxonomy. Protal reliably profiles all 143,614 bacterial and archaeal species in GTDB r226 and achieved higher precision on CAMI2 benchmarks than all tested contemporary profilers, including MetaPhlAn 4, mOTUs4, sylph and Kraken2+Bracken (mean species-level precision 98.3% versus 97.5% for the next-best profiler, sylph). In custom benchmarks, protal showed particularly strong gains for rare species represented by a single reference genome and for highly complex communities containing 10,000 species (F1-score 9% and 14% higher than the second best profiler). Because protal retains read alignments, it can reconstruct intraspecific phylogenetic relationships among detected bacteria with accuracy comparable to StrainPhlAn 4. Unlike dedicated strain-profiling workflows, however, protal performs strain analysis concurrently with species-level profiling, making it up to 40-fold faster without requiring additional steps. Together, these features make strain-resolved profiling of thousands of metagenomes feasible on commodity hardware. The software, databases and tutorials are available at https://github.com/4less/protal and http://protal.earlham.ac.uk.

The human gut microbiota has been explored for the last two decades and studies have uncovered its major role in the degradation of complex glycans from the diet as well as the host tissues. Strikingly, until very recently, no similar investigation has been carried out on the human oral cavity microbiota, which is increasingly recognized has an important parameter in gastrointestinal health. Prevotellaceae are an important and under-investigated family of bacteria in both the gut and the oral cavity microbiota. The oral cavity is particularly rich in glycosaminoglycans which could provide an abundant carbon source for the inhabiting microorganisms, and play a significant role in host-pathogen interactions. Hence, we analyzed Polysaccharide Utilization Loci in oral microbiota species and we identified a glycosaminoglycan-degrading Polysaccharide Utilization Locus in Segatella oris. We produced and characterized the four enzymes, confirmed their ability to degrade glycosaminoglycans, and we focused our efforts on the biochemical and structural characterization of the first reported Prevotellaceae endosulfatase.

The benzoxazolinate moiety is a key functional group found in a few natural products (NPs), exhibiting diverse bioactivities, including antitumor, antibacterial, and cytotoxic activities. Despite their clinical importance, only a few bacterial strains and NPs have been reported harboring this rare bis-heterocyclic moiety, underscoring a largely unexplored chemical space. Here, we performed large-scale genome mining and identified 277 putative biosynthetic gene clusters (BGCs) across diverse bacterial hosts, including previously unreported bacterial genera and strains. The BGCs were grouped into three compound classes: benzoxazolinate, benzobactin, and ashimides based on sequence similarity network clustering. Bioactivity predictions of the identified BGCs revealed the predominance of antibacterial and cytotoxic potential, highlighting promising candidates for future experimental validation and functional studies. This study also presents a neural network-based bioprospecting model that efficiently detects rare BGCs encoding benzoxazolinate-containing molecules from genomic sequences. Overall, our findings expand the known repertoire of bacterial hosts with the potential to produce benzoxazolinate-containing NPs and provide a comprehensive framework for the discovery and identification of candidate BGCs.

Genomic and metagenomic studies have revealed that microbes have the capacity to produce an enormous diversity of secondary metabolites. These compounds play important roles in microbial interactions and are also a major source of medicines and other useful natural products, yet only a small fraction of this biosynthetic potential has been experimentally characterized. BGC Atlas was developed to explore this largely uncharacterized diversity by placing biosynthetic gene clusters (BGCs) into genomic and environmental context. Here, we present BGC Atlas v2, which expands the collection nearly ninefold to more than 16 million predicted BGCs and extends it from metagenomic assemblies to MAGs, single-amplified genomes and isolate genomes. The new release provides taxonomic assignments for nearly all BGCs, harmonized environmental metadata, nested searches across biosynthetic, taxonomic, environmental and geographic properties, and protein-sequence searches against BGC-encoded genes. Despite its scale, the collection reveals how much microbial biosynthetic diversity remains unexplored. By connecting pathways to related families, organisms and environments, BGC Atlas v2 supports natural-product discovery and investigations of microbial biosynthetic diversity across taxa and ecosystems. BGC Atlas v2 is freely available at https://bgc-atlas.cs.uni-tuebingen.de.

Viruses shape microbial metabolism through auxiliary viral genes (AVGs) that reprogram host metabolism, physiology, and gene regulation during infection. Identifying AVGs is complicated by ambiguous viral versus cellular assignment, inconsistent curation, and reliance on sequence homology that misses divergent functions. Here we present CheckAMG, which pairs annotation-based AVG prediction, curated with a per-gene viral scoring system and ontological mapping of metabolic function, with a finetuned genome-language model for annotation-independent discovery. Benchmarked against DRAM-V and VIBRANT across three ecosystems, CheckAMG produced more strongly supported auxiliary metabolic gene (AMG) calls, and uniquely among AMG tools reports auxiliary physiological (APGs) and regulatory (AReGs) genes. Applied to 1,005,980 fragmented or complete soil and human-gut viral genomes, CheckAMG identified 541,199 AVGs tracking biome-specific protein clusters and functions, including 109,107 proteins with no detectable sequence similarity to any reference database. CheckAMG thus provides a reproducible, scalable framework for identifying the genes through which viruses reprogram host biology.

Microbes closely interact with every living organism, including meiofauna (i.e., microbial eukaryotes 38 m - 1 mm in length), and influence the development, life cycle, and evolution of diverse metazoans. Together, meiofauna and their microbiomes, collectively referred to as the holobiont, underpin biogeochemical cycles and drive decomposition of organic matter. However, our understanding of the ecological and evolutionary dynamics of meiofauna microbiomes are limited, typically owed to low-resolution 16S rRNA surveys, which cannot accurately delineate bacterial taxa. Single-specimen holobiont sequencing can help overcome the limitations of metabarcoding approaches by 1) generating metagenome-assembled genomes (MAGs) of the host microbiome and 2) recovering host single-copy genes (SCGs) to phylogenetically confirm the identity of the host organism. However, most bioinformatics pipelines for the assembly of metagenomic datasets have been developed for the assembly of high-complexity microbial communities of bulk sediment or soil samples (and cannot be used for the assembly of host genomes), rely on co-assembly approaches (which collapses strain-level genomic information of bacterial taxa), and focus on binning either prokaryotic or eukaryotic taxa. Therefore, there is a tremendous need for a computational workflow for the dual analysis of host genomes and their microbiomes. Here, we developed MeioBIOME, a modular Snakemake pipeline for the reproducible analysis of holobiont metagenomes obtained from individually sequenced microbial metazoa. We analyze publicly available single-specimen metagenomics datasets to show the utility of MeioBIOME and recover host-associated symbiont MAGs and host SCGs. Additionally, we integrate state-of-the-art binning algorithms which generate more MAGs than the DOE Joint Genome Institute metagenomic pipeline. We anticipate that MeioBIOME will facilitate studies of phylosymbiosis by generating high-quality host genome skims (to build well-supported host phylogenetic trees) and host-associated prokaryotic MAGs obtained from single specimens.

FigTree is a long-standing phylogenetic tree viewer, but its GUI-centered workflow does not itself provide a versioned, batch-replayable record of styling operations. We present FigTreeKit, a Python package that serializes a supported subset of FigTree 1.4.4 annotations (!hilight, !color, and !font), audits taxonomy mappings before topology-gated clade collapse, retains selected BEAST-style metadata in the tested fixtures, and invokes a patched FigTree renderer for headless PNG, PDF, and SVG output. Across 60 independently generated balanced trees with 50-10,000 taxa (10 trees per size, each timed 10 times as technical replicates), the tree-level log-log slope of export time was 0.96 (95% confidence interval [CI], 0.91-1.01), which is compatible with approximately linear scaling over the tested range but does not prove it. The 189,801-taxon GTDB R232 bacterial reference tree was parsed and exported as a large-data scalability demonstration. On the 10,122-taxon GTDB R232 archaeal reference tree, the scripted workflow assessed 179 order-level groups; 142 multi-tip groups produced non-trivial collapses, whereas 37 singleton groups did not alter the display. The software is accompanied by 796 passing tests, a golden conformance corpus that includes acceptance tests against the bundled FigTree JAR, deterministic scenario-based topology checks, and an overall statement coverage of 81%, reported as a descriptive engineering metric. FigTreeKit is released under the GPL-2.0-or-later license as the figtreekit package on PyPI, with source code, documentation, and benchmark data archived on Zenodo.