Registry Catalogue

RnBeads is an open-source R package for the comprehensive analysis of DNA methylation data at single-CpG resolution. It supports Infinium and EPIC microarrays, bisulfite sequencing protocols, and preprocessed MeDIP-seq and MBD-seq data. RnBeads combines quality control, normalization, filtering, exploratory analysis, covariate assessment, and differential methylation analysis in a modular workflow that scales to large cohort studies. Results are documented in highly annotated HTML reports containing method descriptions, publication-ready plots, and detailed data tables. Key benefits Supports multiple DNA methylation assays and input formats Implements state-of-the-art normalization and flexible CpG and sample filtering Identifies sample outliers, potential sample mix-ups, batch effects, and phenotype-associated covariates Analyzes methylation distributions and within- and between-group variability Performs differential methylation analysis at individual CpGs and predefined or custom genomic regions Generates comprehensive, shareable HTML reports with publication-ready visualizations Scales to large sample numbers and can be run through a master command, individual pipeline modules, or a graphical user interface Applications Quality control and preprocessing of DNA methylation datasets Analysis of Infinium, EPIC, and supported mouse methylation arrays Analysis of whole-genome and reduced-representation bisulfite sequencing data Detection of batch effects, phenotype covariates, outliers, and sample mix-ups Identification and characterization of differentially methylated CpGs and regions Comparison of methylation variability within and between sample groups Export of methylation data in multiple formats, including genome-browser-compatible views Intended use RnBeads is intended for epigenetics researchers, molecular biologists, bioinformaticians, and core facilities working with DNA methylation data. Its automated workflow and graphical interface make it accessible to first-time users, while its modular design and extensive configuration options support advanced and customized analyses in R.

SABIO-RK is a manually curated database containing information about biochemical reactions and their kinetic properties. Designed for the needs of systems biology and computational modelling, the database provides kinetic rate equations, parameters, reaction participants and modifiers, together with detailed experimental and environmental conditions. The structured and standardized data are manually extracted from scientific literature and linked to controlled vocabularies, biological ontologies, and external databases. SABIO-RK supports both interactive access via a web interface and automated integration through web services and APIs. Data can be exported in standardized formats such as SBML and JSON. Key benefits Manually curated database of biochemical reactions and kinetic data Includes experimental conditions and environmental context Standardized annotations linked to ontologies and external resources Web interface with full-text and advanced search capabilities Programmatic access via web services and APIs Export in standard formats including SBML and JSON Applications Construction and parameterization of kinetic models Systems biology and metabolic pathway modelling Simulation of biochemical and signalling networks Integration of kinetic data into computational workflows Reuse of curated literature-derived reaction parameters Intended use SABIO-RK is intended for systems biologists, bioinformaticians, computational modellers, and life science researchers working with biochemical reaction networks and kinetic simulations. It is particularly suited for users who require standardized and curated kinetic data for model development, validation, and integration into simulation workflows.

scverse is an open-source Python ecosystem for single-cell and spatial omics data analysis. It provides shared data structures, analysis frameworks, and community support for researchers working with high-throughput molecular data. Key benefits: - Interoperable data structures (AnnData, MuData, SpatialData) enabling seamless tool integration across the single-cell and spatial omics Python ecosystem - Scalable to >1M cells, with GPU acceleration available via rapids-singlecell - Community support through a public forum, chat, regular community meetings, and training courses Research questions addressed: - Cell type identification, trajectory inference, and differential expression from single-cell RNA-seq, ATAC-seq, and multimodal omics - Spatial tissue organization, cellular niches, and cell-cell communication from spatially resolved transcriptomics and proteomics - Multi-condition, multi-modal, and multi-batch data integration at scale Target audience: Computational biologists, bioinformaticians, and experimentalists in life science research working with single-cell or spatial omics data. Tools: AnnData - Annotated data matrices for single-cell data (data structure) MuData - Multimodal annotated datasets (data structure) SpatialData - FAIR data framework for spatial omics (data structure) Scanpy - Single-cell gene expression analysis, preprocessing, clustering, visualization Squidpy - Spatial molecular data analysis and visualization scvi-tools - Deep probabilistic modeling for single-cell and spatial omics Muon - Multimodal omics analysis Scirpy - T-cell and B-cell receptor repertoire analysis SnapATAC2 - Single-cell ATAC-seq analysis rapids-singlecell - GPU-accelerated drop-in replacement for Scanpy/Squidpy Pertpy - Perturbation experiment analysis Decoupler - Enrichment analysis and pathway/TF activity inference anndataR - AnnData interoperability in R

SeqAn is an open-source C++ library providing efficient algorithms and data structures for biological sequence analysis. It uses a generic library design that supports high performance, flexibility, extensibility, and integration with other software libraries. SeqAn helps developers build new bioinformatics tools for sequence analysis while keeping performance overhead low. Key benefits Open-source C++ library for biological sequence analysis Provides efficient algorithms and data structures for sequence-based workflows Generic design supporting performance, flexibility, and extensibility Facilitates development of new bioinformatics software tools Integrates with other libraries and computational workflows Applications Development of sequence analysis software Implementation of high-performance bioinformatics algorithms Analysis of DNA, RNA, or protein sequence data Construction of custom tools for genomics and computational biology Integration of sequence analysis functionality into larger software projects Intended use SeqAn is intended for bioinformatics software developers, computational biologists, algorithm developers, and researchers who need efficient C++ components for biological sequence analysis. It is particularly suited for users developing high-performance tools that rely on reusable sequence algorithms and data structures.

SIENA is a software pipeline for the fully automated generation of protein structure ensembles from the Protein Data Bank (PDB). Starting from a single query structure, SIENA identifies binding sites with high sequence similarity, extracts the corresponding structures, and aligns and superimposes them to create comparable protein ensembles. The pipeline also supports complex structural scenarios, including binding sites located at protein domain interfaces or within multimeric proteins. Key benefits Fully automated generation of protein structure ensembles Systematic search of the PDB for sequence-similar binding sites Automated extraction, alignment, and superposition of structures Supports binding sites at domain interfaces and in multimeric proteins Reduces manual effort in collecting and preparing comparable structures Enables reproducible analysis of structural variability Applications Analysis of protein binding-site flexibility and conformational diversity Generation of structure ensembles for molecular docking Comparison of related protein–ligand complexes Investigation of conserved and variable binding-site features Preparation of structural datasets for drug discovery and modelling Study of binding sites in multidomain and multimeric proteins Intended use SIENA is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery who require curated protein structure ensembles for comparative analysis, docking, or modelling. It is particularly suited for users who want to explore binding-site variability across related structures without manually searching, aligning, and superimposing PDB entries.

SILVA – Comprehensive Resource for Ribosomal RNA Sequence Data SILVA is a comprehensive, quality-controlled resource for aligned ribosomal RNA (rRNA) gene sequences from the domains Bacteria, Archaea, and Eukaryota. It provides regularly updated reference datasets, taxonomic classifications, and curated guide trees that support a wide range of microbial and phylogenetic analyses. As an ELIXIR Core Data Resource, SILVA is a widely used reference database for microbial community studies and taxonomic assignment workflows. In addition to its data products, SILVA offers a suite of online tools and services for sequence alignment, taxonomic classification, phylogenetic analysis, primer and probe evaluation, and amplicon sequencing data analysis. Key benefits Comprehensive and quality-controlled rRNA reference database Regularly updated sequence datasets and taxonomic classifications Curated guide trees reflecting current taxonomy and nomenclature Supports Bacteria, Archaea, and Eukaryota ELIXIR Core Data Resource with broad community adoption Integrated web tools for sequence analysis and phylogenetics Freely accessible resource for research and education Applications Taxonomic classification of ribosomal RNA sequences Microbiome and microbial community analysis Amplicon sequencing data processing and interpretation Phylogenetic tree construction and visualization Primer and probe evaluation for molecular biology experiments Reference database for bioinformatics pipelines and workflows Intended use SILVA is intended for microbiologists, microbial ecologists, bioinformaticians, evolutionary biologists, and life science researchers working with ribosomal RNA sequence data. It is particularly suited for users requiring high-quality reference sequences and taxonomic information for microbial identification, phylogenetic analyses, and microbiome research.

SILVAngs – rRNA Amplicon Analysis Service SILVAngs is an online data analysis service for ribosomal RNA (rRNA) gene amplicon sequencing data generated by next-generation sequencing (NGS) technologies. Based on an automated analysis pipeline, SILVAngs uses the high-quality SILVA reference databases, taxonomies, and alignments to classify rRNA sequences and provide comprehensive analysis results. The service generates a wide range of downloadable outputs, including taxonomic tables, graphical summaries, and processed sequence files. Key benefits Automated analysis pipeline for rRNA amplicon sequencing data Classification based on the curated SILVA reference databases Supports bacterial, archaeal, and eukaryotic rRNA datasets Comprehensive output including tables, visualizations, and sequence files Web-based service requiring no local software installation Standardized and reproducible microbiome analysis workflows Applications Taxonomic classification of amplicon sequencing reads Microbiome and microbial community profiling Analysis of 16S, 18S, and other rRNA gene amplicon datasets Generation of publication-ready tables and graphical summaries Quality-controlled processing of high-throughput sequencing data Support for microbial ecology and environmental sequencing studies Intended use SILVAngs is intended for microbiologists, microbial ecologists, bioinformaticians, and life science researchers working with rRNA gene amplicon sequencing data. It is particularly suited for users seeking an easy-to-use, web-based solution for standardized taxonomic classification and microbiome analysis using the SILVA reference databases.

SimpleVM – Easy Access to Cloud Computing Resources SimpleVM is a user-friendly platform that makes cloud computing accessible to researchers with different levels of technical experience. It enables users to launch and manage virtual machines, clusters, and browser-based research environments with only a few clicks, while handling technical tasks such as network configuration, SSH access, and volume mounting in the background. SimpleVM also supports the organization of cloud-based training events and simplifies the administration of users, resources, and projects. Key benefits Easy creation and management of virtual machines without complex cloud configuration Accessible to users with little or no cloud-computing experience Central administration of access to virtual machines, clusters, and projects Support for browser-based research environments such as RStudio Simplified setup and management of training courses and workshops Sharing of configured environments through reusable snapshots Suitable for individual analyses as well as collaborative projects Applications Launching virtual machines for bioinformatics and life science research Providing temporary computing environments for data analysis Hosting browser-based research environments and scientific software Preparing standardized virtual machines for collaborative projects Managing cloud resources for hands-on training events Sharing preconfigured analysis environments through snapshots Intended use SimpleVM is intended for life science researchers, bioinformaticians, trainers, students, and project administrators who require flexible cloud computing resources without having to manage the underlying infrastructure in detail. It is particularly suited for users who want to quickly start individual virtual machines, provide reproducible analysis environments, or organize cloud-based training events.

SMART (Simple Modular Architecture Research Tool) is a web-based resource for the identification and annotation of protein domains and the analysis of domain architectures. It detects more than 1,400 domain families, particularly in signalling, extracellular, chromatin-associated, and genetically mobile proteins. SMART provides extensive domain annotations, including phyletic distribution, functional class, tertiary structure information, and functionally important residues. Key benefits Identification and annotation of protein domains from sequence data Detection of more than 1,400 curated domain families Analysis of protein domain architectures and domain combinations Rich domain annotations including function, structure, taxonomy, and conserved residues Searchable database for proteins with specific domain combinations in defined taxa Applications Protein domain annotation and architecture analysis Identification of functional regions in protein sequences Comparative analysis of domain combinations across taxa Exploration of signalling, extracellular, chromatin-associated, and mobile domains Functional interpretation of newly characterized or predicted proteins Intended use SMART is intended for molecular biologists, bioinformaticians, protein scientists, and comparative genomics researchers who need to identify protein domains and explore domain architectures. It is particularly suited for users interested in functional protein annotation, conserved domain combinations, and the taxonomic distribution of domain families.

Statistical, bioinformatics and machine learning consulting for proteomics data provides comprehensive support for proteomics research, covering the full data lifecycle from raw data processing to interpretable results. The consulting service combines bioinformatics and biostatistics expertise to advise on study design, software selection, analytical strategy, and downstream interpretation. Analyses can also be performed directly, including preprocessing, identification, quantification, statistical modelling, machine learning, visualization, and publication support. Key benefits End-to-end support from raw proteomics data to biological interpretation Combined expertise in bioinformatics, biostatistics, and proteomics workflows Support for study design, software selection, and analytical strategy Direct execution of analyses, including statistics and machine learning Publication-ready reporting, visualizations, and methods support Applications Preprocessing, quality control, normalization, and format conversion Peptide identification, protein inference, and protein quantification Statistical analysis including regression, survival analysis, and biomarker studies Machine learning for classification, clustering, validation, and model interpretation Development or application of user-friendly proteomics workflows Intended use This consulting service is intended for proteomics researchers, life scientists, clinical researchers, bioinformaticians, and research groups who need expert support for the planning, analysis, interpretation, or publication of proteomics studies. It is particularly suited for projects requiring robust statistical design, reproducible workflows, machine learning approaches, or publication-ready analysis outputs.

STRING is a database of known and predicted protein–protein interactions. It covers both direct physical interactions and indirect functional associations, integrating evidence from experimental data, computational predictions, knowledge transfer between organisms, and curated interaction databases. STRING enables users to explore protein interaction networks, functional associations, and biological context across a broad range of organisms. The current STRING database covers more than 20 billion interactions between over 59 million proteins from over 12,500 organisms. Key benefits Integrates known and predicted protein–protein associations Covers direct physical and indirect functional interactions Combines evidence from experiments, databases, text mining, co-expression, and genomic context Large organism coverage with searchable protein interaction networks Provides web access, downloads, APIs, and Cytoscape integration Applications Exploration of protein interaction networks Functional interpretation of gene or protein lists Identification of interaction partners and pathway context Comparative analysis of functional associations across organisms Integration of interaction data into bioinformatics workflows Intended use STRING is intended for molecular biologists, bioinformaticians, systems biologists, and life science researchers who want to investigate protein interactions and functional associations. It is particularly suited for users analyzing gene or protein lists, exploring biological pathways, or integrating interaction networks into systems biology and functional genomics studies.

StructureProfiler is a tool for the automated quality assessment of protein–ligand complex structures. It evaluates sets of three-dimensional structures using multiple quality indicators covering the overall structural model, the binding site, and the ligand. These indicators include model-level parameters such as the R factor, active-site properties such as bond-length deviations, and ligand-specific criteria such as electron density support and torsion-angle validity. Key benefits Automated quality profiling of protein–ligand complex structures Combines model-, binding-site-, and ligand-level quality indicators Supports consistent comparison of multiple structures Helps identify structures that may be unsuitable for downstream computational analyses Reduces the effort required for manual structure inspection Supports transparent and reproducible structure selection Applications Selection of protein–ligand complexes for docking and modelling studies Quality control before structure-based drug design Assessment of experimental structure quality Identification of problematic ligands or binding-site geometries Evaluation of benchmark datasets for method development Comparison and prioritization of alternative structural models Intended use StructureProfiler is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery who need to assess the quality and suitability of protein–ligand complex structures before using them in computational experiments, benchmarking, or method evaluation.