Registry Catalogue

BakRep is a comprehensive, scalable web repository that aggregates and standardizes millions of publicly available bacterial genomes from e.g. AllTheBacteria. Each genome is enriched with uniform quality metrics, taxonomic classification, sequence typing, and annotation, enabling rapid and reproducible comparative analyses across large datasets, and integrated with accompanying submission metadata. Key Benefits Extensive data coverage with consistently processed bacterial genomes. Integrated Metadata: original submission metadata comprising e.g. sampling location, data, source. Standardized genome characterizations, including QC, taxonomy, MLST, and annotation. Powerful search and filtering to compile custom genome sets based on genomic or metadata attributes. Web interface and command-line access for both exploratory and automated high-throughput workflows. Features Unified pipeline for QC, taxonomic assignment, sequence typing, and annotation. Advanced search by species, genome size, GC content, contig count, sequence type, and more. Downloadable genome subsets for downstream computational analyses. CLI integration for large-scale or reproducible workflows. Applications Comparative genomics, phylogenetics, and population genomics. Large-scale surveys of resistance genes, virulence factors, or metabolic traits. Building curated genome datasets for benchmarking or tool development. Supporting epidemiological investigations and outbreak analyses. Intended Use BakRep is ideal for microbial genomics researchers, bioinformaticians, and epidemiologists who need reliable, standardized access to large bacterial genome collections.

bHLH Annotator is a web-based tool for the automated identification and functional annotation of the basic Helix–Loop–Helix (bHLH) transcription factor family in plants. The tool analyzes coding or protein sequences derived from genome or transcriptome assemblies and combines homology searches, phylogenetic analysis, orthology inference, and motif detection to provide reliable functional annotations. By automating the annotation workflow, bHLH Annotator enables rapid and reproducible characterization of bHLH gene families in newly sequenced plant species. Key benefits Automated identification of plant bHLH transcription factors Functional annotation based on orthology to experimentally characterized bHLHs Combines BLAST or HMMER searches with phylogenetic classification Detects conserved bHLH domains, DNA-binding properties, and subfamily-specific motifs Supports genome and transcriptome assemblies Web-based service with reproducible analysis workflow Applications Annotation of bHLH gene families in newly sequenced plant genomes Functional characterization of transcription factors Comparative genomics of plant transcription factor families Evolutionary and phylogenetic analyses of bHLH proteins Candidate gene identification for plant functional genomics Analysis of de novo genome and transcriptome assemblies Intended use The bHLH Annotator is intended for plant biologists, genome annotation specialists, bioinformaticians, and evolutionary researchers working with plant genome or transcriptome data. It is particularly suited for researchers who require accurate and automated annotation of bHLH transcription factors for comparative genomics, gene family evolution, and functional genomics studies.

BIIGLE is a web-based software for image and video annotation that enables collaborative research on large datasets. It offers tools for manual and computer-assisted annotation, quality control and the collaboration on custom taxonomies to describe objects. BIIGLE is freely available and can be installed in cloud environments, a local network or on mobile platforms during research expeditions. The public instance on biigle.de is free for non-commercial use.

Bioconda is a community-driven software distribution for bioinformatics and life-science tools based on the Conda package manager. It provides easy access to thousands of curated software packages and supports reproducible installation and management of complex bioinformatics workflows. Bioconda packages are tightly integrated with BioContainers, enabling container-based execution of the same software. Key Benefits: Simple installation with automatic dependency resolution. Reproducible environments through versioned packages and containers. Broad coverage across genomics, transcriptomics, metagenomics, proteomics, and systems biology. Flexible execution via Conda environments or containers. Suitable for local systems, HPC, and cloud infrastructures. Features: Large, community-maintained repository of bioinformatics software packages. Automated building and testing to improve consistency and reliability. Generation of BioContainers from Bioconda packages for container-based workflows. Support for isolated Conda environments and container runtimes. Compatibility with Linux and macOS systems. Applications: Installation and management of bioinformatics software stacks. Building reproducible analysis environments for research and training. Running workflows in container-based systems and workflow managers. Standard software provisioning in research infrastructures and core facilities. Intended Use: For bioinformaticians, life-science researchers, and infrastructure providers who need reliable, reproducible software installation. Supports both Conda-based environments and containerized execution using BioContainers.

BioContextAI is a community hub that connects agentic artificial intelligence with biomedical resources and software via Model Context Protocol (MCP) servers. Its main goal is to foster the development of MCP servers for biomedical research applications that comply with the FAIR4RS principles (Findable, Accessible, Interoperable, and Reusable for Research Software). BioContextAI provides the BioContextAI Registry, a community-driven catalogue of MCP servers supporting AI-based biomedical research workflows. The Registry enables researchers and developers to discover, access, and contribute specialized MCP-based tools and databases enriched with structured metadata. Key benefits Community-driven registry of MCP servers for biomedical AI applications Supports FAIR4RS-compliant software development Enables integration of AI agents with biomedical databases and tools Rich metadata to improve discoverability and interoperability Encourages collaboration between AI developers and biomedical researchers Applications Discovery and reuse of MCP servers for AI-supported biomedical workflows Integration of large language models (LLMs) with biomedical tools and databases Development of interoperable AI-driven research applications FAIR4RS-oriented software development and community contribution Exploration of agent-based AI approaches in life science research Intended use BioContextAI is intended for biomedical researchers, AI developers, research software engineers, and data infrastructure providers who aim to integrate agent-based AI systems with biomedical resources in a FAIR-compliant manner. Optional knowledge about LLM tool usage via MCP is beneficial for advanced development and integration scenarios. Website Contact If you used this service, please help us improve by completing our short user survey.

The Cloud-based Workflow Manager (CloWM) is a web platform for scalable and reproducible execution of bioinformatics workflows. It combines curated Nextflow pipelines, integrated cloud computing, and S3-based data storage in an easy-to-use interface, enabling researchers to run complex analyses without command-line expertise. Key benefits Web-based access to scalable and reproducible workflow execution Curated and version-controlled Nextflow workflows Integrated S3-based data storage and cloud computing infrastructure No command-line expertise required for workflow execution Supports a wide range of omics and genome analysis applications Authentication via Life Science Login and NFDI Login Applications Metagenomics and metabarcoding analysis Genome assembly and annotation workflows Transcriptomics and phylogenomics analyses Reproducible execution of Nextflow pipelines Cloud-based large-scale bioinformatics analyses Accessible deployment of computational workflows for non-expert users Intended use CloWM is intended for life science researchers, bioinformaticians, and research groups who require scalable and reproducible execution of bioinformatics workflows without extensive infrastructure or command-line expertise.

CNApy (CellNetAnalyzer for Python) is an open-source, cross-platform desktop application for the intuitive exploration, editing, and computational analysis of metabolic network models. Written in Python, CNApy provides a modern graphical user interface for constraint-based (COBRA) modelling approaches, including flux balance analysis, metabolic flux analysis, elementary modes analysis, and computational strain design. CNApy builds upon the interactive network-map concept of the MATLAB-based toolbox CellNetAnalyzer (CNA) and extends it with enhanced features for interactive model exploration and analysis. The software supports import and export of metabolic models in SBML format. Key benefits Intuitive graphical interface for COBRA-based metabolic modelling Interactive exploration and visualization of metabolic networks Supports multiple analysis approaches including FBA, MFA, and elementary modes analysis Open-source and cross-platform desktop application written in Python Compatible with SBML model exchange standards Extends and modernizes concepts from CellNetAnalyzer Applications Constraint-based metabolic network analysis Flux balance and metabolic flux analysis Exploration and editing of genome-scale metabolic models Computational strain and pathway design Visualization of metabolic pathways and network states Import/export and exchange of SBML-based models Intended use CNApy is intended for systems biologists, metabolic engineers, bioinformaticians, and computational life scientists working with constraint-based metabolic network models. It is particularly suited for users who require an interactive graphical environment for model exploration, visualization, and advanced COBRA analyses without relying exclusively on command-line workflows.

COPASI - Creating and solving mathematical models of biological processes COPASI is an open-source software application designed for creating and solving mathematical models of various biological processes. It provides a comprehensive platform for researchers to define, simulate, analyze, and visualize complex biological systems. Key Features Model Definition: COPASI allows users to define models of biological processes using ordinary differential equations (ODEs), algebraic equations, or stochastic simulations. Simulation and Analysis: The software includes features for simulating and analyzing these models, including steady-state analysis, time-course simulation, parameter estimation, and sensitivity analysis. Analysis Reports: COPASI can generate comprehensive reports on the results of analyses, providing insights into the behavior of biological systems. Import/Export in SBML Format: Models created in COPASI can be exported in SBML (Systems Biology Markup Language) format, allowing for easy sharing and collaboration with other researchers. Applications COPASI is widely used in various fields of biology and medicine, including: Metabolic Networks: COPASI can simulate the behavior of metabolic pathways, helping researchers understand how cells respond to different environmental conditions. Cell-Signaling Pathways: The software can model signaling cascades that regulate cellular responses to external stimuli. Regulatory Networks: COPASI can be used to study gene regulatory networks and their role in controlling biological processes. Infectious Diseases: Researchers use COPASI to develop mathematical models of infectious diseases, helping them understand disease dynamics and develop effective treatments. Availability COPASI is available for download at no cost. The software is compatible with various operating systems, including Windows, macOS, and Linux. Community Support The COPASI community provides extensive support to users through online forums, tutorials, and documentation. Researchers can also contribute to the development of the software by submitting bug reports, feature requests, or participating in coding projects.

DoGSite3 was developed for predicting robust and reliable small molecule binding sites and computing their geometrical and chemical descriptors. It is based on the grid-based DoGSite algorithm for predicting pockets and their sub-pockets. The new tool is largely rotation- and translation-invariant due to a normalization procedure before binding site prediction. Known ligands in the structure can be used to bias the grid by sufficiently buried ligand fragments. The output encompasses novel chemical binding site descriptors considering solvent accessibility. Compared to its predecessor, it shows increased robustness through comprehensive parameter optimization. DoGSite3 runs finish within seconds.

DoGSiteScorer is a grid-based tool for the automated detection, characterization, and druggability assessment of protein binding pockets. It applies a Difference of Gaussian filter to the three-dimensional protein structure to identify potential pockets and subdivide them into sub-pockets. For each predicted pocket, the tool calculates descriptors covering size, shape, enclosure, and chemical properties. DoGSiteScorer provides two complementary druggability assessments: a simple score based on pocket volume, hydrophobicity, and enclosure, and a support vector machine model using a broader set of pocket descriptors. Scores range from zero to one, with higher values indicating a greater estimated likelihood that the pocket can bind drug-like molecules. Key benefits Fully automated detection of binding pockets and sub-pockets Requires only the three-dimensional protein structure Calculates geometric and physicochemical pocket descriptors Provides interpretable druggability scores between zero and one Combines a simple descriptor-based score with machine-learning prediction Supports rapid comparison and prioritization of potential binding sites Applications Identification of potential small-molecule binding sites Druggability assessment of protein pockets Comparison of alternative pockets and sub-pockets Selection of binding sites for docking and virtual screening Support for target assessment in early-stage drug discovery Structural characterization of protein cavities Intended use DoGSiteScorer is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery who need to identify and prioritize potential ligand-binding pockets. It is particularly suited for users who want an automated, structure-based assessment of pocket geometry, physicochemical properties, and estimated druggability.

EDIA (Electron Density Score for Individual Atoms) is a tool for quantifying how well individual atoms in a crystallographically resolved structure are supported by the experimental electron density. Scores for multiple atoms can be combined using a power mean to calculate EDIAm, which summarizes the electron density support for a group of atoms, such as a ligand, amino acid residue, or active site. Key benefits Quantifies electron density support at the level of individual atoms Provides combined EDIAm scores for selected groups of atoms Supports the assessment of ligands, residues, and complete active sites Enables objective and reproducible evaluation of structural model quality Helps identify poorly supported atoms or molecular regions Facilitates comparison of structural components across protein–ligand complexes Applications Validation of ligands in crystallographic protein structures Assessment of electron density support for amino acid residues Quality evaluation of protein binding sites and active sites Identification of potentially mis-modelled atoms or molecular fragments Selection of reliable protein–ligand complexes for docking and modelling studies Quality control of structural datasets used for method development Intended use EDIA is intended for structural biologists, crystallographers, medicinal chemists, and computational chemists working with crystallographically resolved molecular structures. It is particularly suited for users who need an objective measure of electron density support for individual atoms or defined groups of atoms before using structures in downstream analysis.

The European Galaxy Server is a publicly available, web-based analysis platform that enables researchers to perform complex data analyses without the need for local software installation or advanced programming skills. It provides access to a wide range of bioinformatics tools and workflows in a reproducible, transparent, and user-friendly environment, running on the de.NBI Cloud infrastructure and operated as part of the European Galaxy ecosystem. Key Benefits: Web-based access to bioinformatics tools without local installation. Reproducible analyses through recorded workflows and histories. Free access to substantial computational resources for research use. User-friendly graphical interface suitable for beginners and experts. Integrated data management and sharing capabilities. Features: Large collection of tools covering genomics, transcriptomics, proteomics, metagenomics, and more. Workflow editor for building, reusing, and sharing analysis pipelines. History tracking to ensure transparency and reproducibility. Support for data upload, visualization, and result export. Integration with training materials and community workflows. Applications: Data analysis in genomics, transcriptomics, proteomics, and metagenomics. Teaching and training in bioinformatics and data analysis. Rapid prototyping and testing of analysis workflows. Collaborative research through shared datasets and workflows. Intended Use: The European Galaxy Server is intended for life-science researchers, educators, and students who want to analyze biological data in a reproducible way using a web-based platform, without requiring command-line expertise or local computing infrastructure. While its primary focus within de.NBI is the life sciences, Galaxy is also used in other domains such as the humanities and astrophysics. Help us improve our de.NBI services – take 2 minutes to fill in our survey (https://de.surveymonkey.com/r/denbi-service?sc=rbc&tool=EuropeanGalaxyServer). Thank you!