Registry Catalogue

Enterotyper – Reference-Based Classification of Human Gut Microbiome Enterotypes Enterotyper is a web-based tool for classifying human fecal metagenomic samples into established gut microbiome enterotypes. Using a robust XGBoost machine learning model, the tool provides accurate and reproducible enterotype classification based on a large global dataset of fecal metagenomes. It accepts taxonomic profiles generated with mOTUs, MetaPhlAn, or GTDB-based genus-level profiling and enables users to compare their samples with a global reference dataset. Key benefits Automated, reference-based enterotype classification Robust XGBoost model trained on a large global collection of fecal metagenomes Supports mOTUs, MetaPhlAn, and GTDB genus-level taxonomic profiles Accurate and reproducible classification of gut microbiome community types Standardized comparison of samples across studies and cohorts Web-based access without local software installation Applications Classification of human gut microbiome samples into established enterotypes Comparison of enterotype distributions across cohorts and study groups Investigation of associations with disease, medication, diet, and lifestyle Stratification of participants in clinical and epidemiological studies Standardized analysis of taxonomic profiles from fecal metagenomes Comparison of individual samples with a global microbiome reference dataset Intended use Enterotyper is intended for microbiome researchers, bioinformaticians, epidemiologists, and clinical scientists working with taxonomic profiles derived from human fecal metagenomes. It is particularly suited for users who want to assign samples to established gut microbiome enterotypes and compare microbial community patterns across individuals, cohorts, or study conditions.

EURISCO (European Search Catalogue for Plant Genetic Resources) is a central gateway for information on plant genetic resources held in European collections. It provides access to more than two million germplasm accessions of cultivated plants and their wild relatives, which are conserved under ex situ or in situ conditions in over 450 collections across Europe and some neighbouring countries. EURISCO combines passport data with phenotypic information, enabling users to explore the diversity, origin and characteristics of plant genetic resources. EURISCO is operated on behalf of the European Cooperative Programme for Plant Genetic Resources (ECPGR). Key benefits Central access to information from hundreds of European PGR collections More than two million germplasm accessions covering crops and crop wild relatives Broad taxonomic coverage across thousands of genera and species Integration of passport and phenotypic data Supports discovery and comparison of plant genetic resources across institutions Contributes to the conservation and sustainable use of agrobiodiversity Applications Identification of germplasm accessions with specific geographic, taxonomic or phenotypic characteristics Support for crop breeding and pre-breeding research Exploration of crop wild relatives and underutilised plant diversity Comparative analysis of plant genetic resources across collections Selection of material for phenotyping, genotyping and conservation studies Research on agrobiodiversity, adaptation and genetic resource management Intended use EURISCO is intended for plant breeders, genebank curators, crop scientists, geneticists, conservation researchers and bioinformaticians working with plant genetic resources. It is particularly suited for users who need a comprehensive overview of germplasm conserved in European collections and want to identify accessions for breeding, research, conservation or comparative 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!

FAIRDOMHub – Platform for managing research data and models FAIRDOMHub is a publicly available repository managed and supported by the FAIRDOM consortium. Built on the open-source FAIRDOM-SEEK software, it enables researchers to store, organize, share, and publish research assets from biology projects. These assets include data files, standard operating procedures (SOPs), models, documents, and publications. FAIRDOMHub enables users to link research results to the biological studies and investigations from which they originated, to customize the sharing of individual assets, and provides API access for querying and reusing data. Key benefits • FAIR data management: Supports the organization and documentation of research assets according to FAIR principles. • Yellow pages: Community registry to help researchers find collaborators, active consortia, and organizational affiliations. • Centralized research resources: Provides a common environment for managing datasets, models, simulations, workflows and associated metadata. • Traceability and provenance: Links research outcomes and their relationships to improve transparency and reproducibility. • Collaboration and sharing: Facilitates structured exchange of research resources within teams and across projects. • Reuse of research assets: Enables researchers to discover and reuse existing data and models. • One-Click Publishing: Allows the generation of DOI for whole Investigation or individual Studies and Assays to be cited in a publication as supplementary information. Applications • Collaborative research projects requiring structured research data management • Management of research data and metadata, models, and protocols for systems biology and beyond. • Publication of FAIR data and reproducible research outputs by generating snapshots and DOIs. • Preservation and reuse of research results beyond individual projects Intended use FAIRDOMHub is intended for researchers, research groups, projects, institutions, and research consortia that need to systematically manage, document, share and reuse research data and computational resources. It is particularly suitable for collaborative projects that want to organize, preserve, interlink, and publish data, models, protocols, and related outputs according to the FAIR principles.

Flexynesis is an end-to-end framework for multi-omics data integration and predictive modeling, combining data preprocessing, feature selection, Bayesian hyperparameter optimization, model training, evaluation, and interpretation within a unified workflow. It supports both deep learning and classical machine learning methods through a standardized interface, enabling classification, regression, survival analysis, multi-task learning, and cross-modality prediction from heterogeneous molecular datasets. Flexynesis is designed with interpretability in mind. By integrating methods such as integrated gradients through Captum, it helps researchers identify informative molecular markers and better understand model predictions beyond black-box classification or regression. Key benefits Deep learning framework for multi-omics data integration Supports multiple neural architectures for different prediction tasks Flexible fusion of heterogeneous omics layers Automated feature selection and hyperparameter optimization Interpretability support for marker discovery using integrated gradients Applicable to classification, regression, survival, and cross-modality prediction tasks Continuously benchmarked on public datasets, especially in oncology Applications Prediction of clinical and preclinical endpoints from multi-omics data Drug response prediction in patients and preclinical models such as cell lines and PDXs Cancer subtype classification Survival and outcome prediction Cross-modality prediction between molecular data layers Discovery of predictive biomarkers and molecular signatures Benchmarking of deep learning approaches for biomedical multi-omics integration Intended use Flexynesis is intended for computational biologists, bioinformaticians, translational researchers, and machine learning researchers working with multi-omics datasets and clinically relevant endpoints. It is particularly suited for users who want to build interpretable deep learning models for biomedical prediction tasks, especially in oncology and preclinical research.

The Freiburg RNA Toolbox provides a comprehensive set of online tools for RNA research, covering various aspects such as sequence-structure alignments, clustering, interaction prediction, identification of homologs, sequence design, and more. Tools Interaction Prediction CopraRNA : A tool for sRNA target prediction that combines whole genome IntaRNA predictions using homologous sRNA sequences from distinct organisms. IntaRNA : A tool for predicting RNA-RNA interactions, including mRNA target sites for non-coding RNAs (ncRNAs) like eukaryotic microRNAs (miRNAs) or bacterial small RNAs (sRNAs). metaMIR : A framework to predict interactions in human between miRNAs and clusters of genes. Seq-Str Alignment LocARNA : A tool for multiple alignments of RNAs based on their sequence and structure similarity, considering the whole ensemble of secondary structures for each RNA. CARNA : A tool for multiple alignment of RNA molecules based on their full ensembles of structures, computing the alignment that fits best to all likely structures simultaneously. MARNA : A tool for multiple sequence-structure alignments considering a single fixed structure for each sequence only. ExpaRNA : A fast, motif-based comparison and alignment tool for RNA molecules. CRISPR CRISPRmap : A tool providing a quick and detailed insight into repeat conservation and diversity of both bacterial and archaeal systems. CRISPRloci : A full suite for CRISPR locus characterization that includes CRISPR array orientation, detection of conserved leaders, cas gene annotation, and subtype classification. Sequence Design AntaRNA : An Ant-Colony Optimization based tool solving the RNA inverse folding problem, designing RNA sequences satisfying a set of constraints. INFORNA : A server for the design of RNA sequences that fold into a given pseudo-knot free RNA secondary structure. SECISDesign : A server for the design of SECIS-elements within the coding sequence of an mRNA with both structure and sequence constraints. Splicing NIPU : A tool allowing to display splicing regulatory motifs and single-stranded regions. SNP & Mutation CopomuS : A tool rating and ranking possible compensatory mutations of IntaRNA predictions. MutaRNA : A tool predicting and visualizing the mutation-induced structure changes of a single-nucleotide polymorphism (SNP) in an RNA sequence. RaSE : An offline tool using the graph vectorization technique to compute a score indicative of the structural stability responsibility of each nucleotide in an RNA sequence. Classification BrainDead : A tool learning a two-class model for short RNA sequences based on accessibility-enhanced k-mer features and applying it for class prediction of unknown RNAs. Gene set extension DiGI : A tool predicting and ranking a list of unknown genes based on their probabilities to be related to a given genetic disease. Additional Resources The Freiburg Bioinformatics Group also provides: MoDPepInt Server : A simple and interactive webserver for predicting the binding partners of three different modular protein domains. CPSP-Tools Server : An exact and efficient approach to identify optimal structures of lattice proteins within the hydrophobic-polar (HP) model. Galaxy Project - Uni Freiburg : A framework for scientists on e.g. NGS data analyses, genome annotation analyses, proteomics, and metabolomics analysis. CMV server : Tools for the visualization of RNA family models, also known as co-variance models (CM) and Hidden Markov Models (HMM).

Fusion (Omics Fusion) is a web-based platform for the integrative analysis, visualization, and exploration of omics data. It supports researchers in analysing single- and multi-level datasets, particularly from transcriptomics, proteomics, and metabolomics experiments. The platform provides tools for data import, management, normalization, transformation, filtering, statistical and cluster analysis, visualization, and pathway mapping. Key benefits Web-based platform for single- and multi-level omics data analysis Supports integration of transcriptomics, proteomics, and metabolomics datasets Provides data preprocessing, filtering, statistical analysis, and clustering Offers interactive visualizations for exploring complex omics data Includes pathway mapping for biological interpretation Applications Analysis and visualization of transcriptomics, proteomics, and metabolomics data Exploration of single-omics and multi-omics datasets Statistical comparison and clustering of experimental data Cross-omics validation of biological patterns Pathway-based interpretation of omics results Intended use Fusion is intended for life science researchers, bioinformaticians, and systems biology researchers working with omics datasets from transcriptomics, proteomics, metabolomics, or combined multi-level experiments. It is particularly suited for users who want to explore, visualize, and interpret omics data in an integrated web-based environment.

Galaxy Docker provides a ready-to-use Docker-based distribution of the Galaxy platform, enabling users to deploy Galaxy quickly and reproducibly on local machines, servers, or cloud environments. It offers a convenient way to start a fully functional Galaxy instance without manual installation of dependencies, making it well suited for testing, training, and portable analysis setups. Key benefits: Fast and reproducible deployment of a Galaxy instance using Docker No complex local installation—dependencies and configuration are packaged into the container Portable setup for local use, servers, and cloud/HPC-adjacent environments Useful for training courses, workshops, demos, and prototyping Open-source and maintained in a public repository Applications: Rapid setup of Galaxy for teaching, workshops, and hands-on tutorials Testing Galaxy configurations, tools, or workflows in an isolated environment Providing a portable Galaxy environment for teams or collaborations Quick prototyping of analysis environments prior to production deployment Intended use: Galaxy Docker is intended for bioinformatics users, trainers, and service operators who want to deploy Galaxy quickly for temporary or reproducible environments. It is particularly suited for users who prefer a containerized approach for setup and maintenance, or who need a reliable Galaxy instance for training and testing scenarios.

The Galaxy Help Forum provides an open, community-driven platform for user support, troubleshooting, and discussion related to the Galaxy platform. Researchers can ask questions about workflows, tools, installations, errors, and best practices, and receive guidance from Galaxy developers, service operators, and experienced community members. The forum serves as a central knowledge hub for resolving technical issues and improving Galaxy-based analyses. Key benefits: Open and transparent support platform for Galaxy-related questions Direct interaction with developers, administrators, and experienced users Publicly searchable knowledge base of previously answered questions Community-driven troubleshooting and best-practice guidance No cost for scientific users Applications: Troubleshooting Galaxy workflows and tool execution errors Guidance on workflow design and optimization Support for Galaxy installation and configuration Advice on tool selection and parameter settings Exchange of best practices for reproducible analysis Intended use: The Galaxy Help Forum is intended for researchers, bioinformaticians, and Galaxy service users who require assistance with Galaxy workflows, installations, or analysis strategies. It is particularly suited for users seeking community-based support and transparent knowledge exchange in an open forum environment.

Galaxy Image Analysis enables web-based and cloud-based analysis of cell microscopy images and quantification of cellular phenotypes. The service provides tools and workflows for image enhancement, cell segmentation, feature extraction, image registration, object tracking, visualization, and performance evaluation. By building on the European Galaxy server and its HPC infrastructure, Galaxy Image Analysis supports reproducible and accessible image analysis workflows for high-content and high-throughput microscopy data. Key Benefits Supports creation, provision, and maintenance of image analysis workflows Enables web-based analysis without local software installation Uses HPC infrastructure provided through the European Galaxy server Provides reproducible workflows for microscopy image analysis Supports FAIR4RS-aligned software and workflow practices Applications Automated analysis of microscopy image data High-content and high-throughput cellular phenotype analysis Cell segmentation, cell counting, feature extraction, and object tracking Registration of multi-modal microscopy images Analysis of temporal live-cell microscopy data Intended use Galaxy Image Analysis is intended for bioimage analysts, researchers in microscopy, cell biologists, bioinformaticians, and life science researchers who need accessible and reproducible workflows for automated image analysis. The software is particularly suited for users working with fluorescence microscopy data, histological microscopy images, live-cell image data, or multi-modal microscopy data who want to quantify cellular phenotypes and spatial or temporal information.

Galaxy TIaaS (Training Infrastructure as a Service) is provided by the European Galaxy Server (usegalaxy.eu) to support hands-on training events within the Galaxy training community. TIaaS offers dedicated computational infrastructure for workshops and courses, ensuring stable performance and sufficient resources for all participants during practical sessions. Key benefits: Dedicated computing resources for Galaxy training events Stable and predictable performance during hands-on sessions No local installation required for trainers or participants Scalable infrastructure supporting small and large courses Integrated within the European Galaxy ecosystem Applications: Hands-on bioinformatics workshops using Galaxy University courses and training schools Online and hybrid Galaxy training events Community-driven training initiatives within ELIXIR and de.NBI Intended use: Galaxy TIaaS is intended for trainers, educators, and workshop organizers who conduct hands-on Galaxy courses and require reliable computational infrastructure for participants. It is particularly suited for events where consistent performance and centralized resource management are essential for a smooth training experience.

GEAR is a genome analysis web server that provides easy-to-use applications for molecular biologists working with DNA sequence data. It supports routine laboratory workflows such as Sanger trace analysis, PCR primer design, and general sequence analysis through an intuitive web-based interface. GEAR combines accessible web applications with reusable open-source backend tools, including the de.NBI services Tracy, Wally, and Alfred. Key benefits Web-based access to genome and sequence analysis tools Supports routine workflows such as Sanger sequencing and PCR primer design Includes open-source backend tools for reproducible analyses Suitable for both browser-based use and command-line integration Useful for research, teaching, and method development Applications Analysis and visualization of Sanger sequencing traces DNA sequence inspection and quality assessment PCR primer design for experimental workflows Visualization of aligned reads and contigs NGS alignment quality control and feature counting Included tools Tracy – basecalling, alignment, assembly, and deconvolution of Sanger chromatogram trace files Wally – visualization of aligned sequencing reads and contigs Alfred – NGS alignment quality control, RNA/DNA feature counting, and flexible feature annotation Intended use GEAR is intended for molecular biologists, geneticists, sequencing facilities, bioinformaticians, and life science researchers who need accessible tools for common genome and sequence analysis tasks. It is particularly suited for users who prefer a web-based interface while still benefiting from robust, open-source tools that can also be reused in automated or reproducible workflows.