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.

Roddy is a workflow management system for large-scale next-generation sequencing data processing. It is designed to manage complex NGS pipelines at petabyte scale and has been used in large collaborative genomics projects such as the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. Roddy supports reproducible execution of workflows with IBM LSF, SLURM, and few other batch processing systems. Key benefits * Framework for managing large-scale NGS processing pipelines * Designed for petabyte-scale sequencing data workflows * Submit analyses to IBM LSF, SLURM, Sun Grid Engine, or Torque PBS * Supports reproducible workflow execution with Apptainer * Powerful configuration system for genomics projects and consortia * Proven use in the Pan-Cancer Analysis of Whole Genomes project Applications * Management of large-scale NGS analysis workflows * Processing of whole-genome sequencing datasets * Coordination of pipeline execution in cancer genomics projects * Reproducible analysis of high-throughput sequencing data * Infrastructure support for large collaborative genomics studies * Stand-alone and or executed by the One-Touch-Pipeline (OTP) workflow automation platform Intended use Roddy is intended for bioinformaticians, genomics researchers, workflow developers, and infrastructure teams working with large-scale NGS data. It is particularly suited for projects that require robust workflow management, reproducible pipeline execution, and scalable processing of very large sequencing datasets.

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.

SANS – Alignment-Free Phylogenetic Reconstruction SANS is a freely available command-line tool for phylogenetic reconstruction from sequence data. It is alignment- and reference-free and accepts one or multiple FASTA or FASTQ files containing complete genomes, assembled contigs, or raw reads. SANS uses shared sequence segments, represented as k-mers, as indicators of phylogenetic splits. These splits are combined, weighted, filtered, and exported in tab-separated format, Newick, or Nexus format for downstream visualization. Key benefits Performs phylogenetic reconstruction without alignment or reference genomes Accepts complete genomes, assembled contigs, or raw sequencing reads Supports input in FASTA and FASTQ format Generates phylogenetic splits based on shared k-mer patterns Available as a C++ command-line tool and as a CloWM workflow Applications Alignment-free phylogenetic analysis of genome-scale data Comparative analysis of complete genomes or assembled contigs Phylogenetic reconstruction directly from raw reads Generation of trees or networks for visualization in tools such as SplitsTree Integration into automated workflows for large-scale sequence comparison Intended use SANS is intended for bioinformaticians, microbial genomicists, evolutionary biologists, and phylogenomics researchers who need alignment-free methods for reconstructing phylogenetic relationships from sequence data. It is particularly suited for users working with large genome datasets, draft assemblies, or raw reads where reference-based or alignment-based approaches may be impractical.

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.

SNV Calling is a workflow for detecting single-nucleotide variants in human sequencing data. It was developed in the Applied Bioinformatics and Theoretical Bioinformatics groups at the DKFZ, and an earlier version of the workflow was used in the Pan-Cancer project. The workflow is designed for annotation-aware SNV detection in human datasets and is suited for human reference genome builds such as hg37/hg19 and, in some later versions, hg38. Key benefits Detects single-nucleotide variants in human sequencing data Developed for large-scale cancer genomics and NGS analysis workflows Earlier workflow version was used in the Pan-Cancer project Includes annotation-aware processing for human genome data Supports human reference builds such as hg37/hg19 and selected hg38 versions Applications SNV detection in human sequencing datasets Variant calling in cancer genomics projects Analysis of whole-genome or targeted sequencing data Integration into standardized NGS processing environments Human genome analysis requiring annotation-aware variant workflows Intended use SNV Calling is intended for bioinformaticians, cancer genomics researchers, sequencing facilities, and genomics research groups working with human sequencing data. It is particularly suited for users who need a workflow for single-nucleotide variant detection in human datasets where genome annotations play an important role in the analysis.