Abstract Summary SeqUIaSCOPE is an open-source platform designed for routine clinical oncology diagnostics through case-centric integration and visualization of genomic variants, fusion events, and expression profiles. The platform combines molecular-level validation via embedded genome browsing with systems-level interpretation through dynamic pathway visualization, enabling geneticists to assess how alterations converge across biological networks. Flexible reporting with customizable templates accommodates diverse institutional requirements, while secure cluster-based or local deployment ensures compliance with data protection policies, making advanced multi-omics diagnostics accessible to academic and clinical institutions. Availability and Implementation SeqUIaSCOPE is freely available on GitHub at https://github.com/BioIT-CEITEC/sequiascope under the MIT license and archived at Zenodo (https://zenodo.org/records/21338445). Due to the sensitive nature of patient data, the repository provides simulated datasets that mimic the structure of real clinical data for testing and exploration. Documentation and a live demo accompany these datasets, allowing users to explore the application without any prior setup. The repository also includes a Helm chart for Kubernetes deployment and Docker containers for local deployment, ensuring compatibility across Linux, macOS, and Windows. No user registration is required, and all data remains on local or institutional infrastructure.
Kateřina Jurásková, P. Pokorna, Kristián Kováč et al.· Bioinformatics· 0 citations
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
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