Registry Catalogue

The information system PANGAEA is operated as an Open Access library to archive, publish and distribute georeferenced data from biodiversity- and earth system research. PANGAEA guarantees TRUSTed long-term availability (greater than 10 years) of its content. PANGAEA focuses on georeferenced observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonisation of the data inventory, as well as the commitment of the hosting institutions ensures FAIRness of published data. The PANGAEA Editorial ensures the integrity and authenticity as well as a high usability of all data. Archived data are machine readable and mirrored into our data warehouse which allows efficient compilations of data products. PANGAEA is the designated German Polar Data Centre. Data is freely available and can be used under the terms of the license mentioned. A few data sets are under moratorium due to ongoing projects or sensitive content. The description of each data set is always visible which includes the principal investigator (PI) for contact. Each dataset can be identified, shared, published and cited by using a full citation including a persistent Digital Object Identifier (DOI Name). PANGAEA allows data to be published as independent, self-standing data publication supplements to science articles (example) or as citable data collections in combination with data journals like ESSD, Geoscience Data Journal, Nature Scientific Data, and others. PANGAEA is open to any project, institution, or individual scientist to archive, publish and use data. Start a data submission here. PANGAEA is the globally leading platform for FAIR data management, data curation, and data publication, in earth, environmental and biodiversity science. PANGAEA empowers researchers, communities, and organisations through cutting-edge data publication, data products, data dissemination, technology and active collaboration.

PIA is a toolbox for mass spectrometry-based protein inference and identification analysis. It enables users to inspect and combine results from common proteomics spectrum identification search engines and perform statistical analyses across datasets. A major focus of PIA lies in integrated protein inference algorithms that derive protein-level conclusions from identified spectra. In addition, PIA supports inspection of peptide-spectrum matches (PSMs), false discovery rate (FDR) calculation across multiple search engine outputs, and visualization of relationships between PSMs, peptides, and proteins. Key benefits Integrated toolbox for protein inference and identification analysis Supports multiple proteomics search engine outputs Combines and compares search engine results seamlessly Protein inference algorithms for robust protein-level interpretation FDR calculation and statistical analysis across datasets Visualization of PSM–peptide–protein relationships Applications Protein inference from tandem mass spectrometry data Inspection and comparison of peptide-spectrum matches Integration of multiple search engine identification results False discovery rate estimation and quality assessment Visualization and interpretation of proteomics identifications Intended use PIA is intended for proteomics researchers, bioinformaticians, and mass spectrometry users who require robust tools for protein inference, statistical validation, and interpretation of proteomics identification results. It is particularly suited for workflows integrating multiple search engine outputs.

Platon – Identification and Characterization of Plasmid Contigs Platon is a specialized tool for the detection and characterization of plasmid-derived contigs in bacterial draft genomes generated from whole-genome short-read assemblies. Using empirically derived Replicon Distribution Scores (RDS) together with an extensive protein marker database, Platon distinguishes chromosomal from plasmid contigs with high sensitivity and specificity. Additional analyses provide detailed insights into plasmid structure, mobility, and replication systems, enabling robust downstream interpretation. Key benefits Accurate identification of plasmid-derived contigs in bacterial draft genomes High sensitivity and specificity through Replicon Distribution Scores (RDS) Comprehensive plasmid characterization using curated protein marker databases Analysis of plasmid mobility, replication systems, and structural features Facilitates interpretation of complex bacterial genome assemblies Open-source and suitable for integration into automated genomics workflows Applications Identification of plasmids in draft bacterial genomes Characterization of plasmid structure, mobility, and replication systems Support for antimicrobial resistance (AMR) surveillance studies Comparative genomics and plasmid epidemiology Quality control and validation of plasmid assemblies in whole-genome sequencing projects Investigation of horizontal gene transfer and mobile genetic elements Intended use Platon is intended for microbiologists, bioinformaticians, microbial genomicists, and public health researchers working with bacterial whole-genome sequencing data. It is particularly suited for users who require reliable identification and characterization of plasmids in draft genome assemblies to support comparative genomics, AMR surveillance, and microbial evolution studies.

PoseEdit is a web-based tool for generating and interactively editing two-dimensional diagrams of protein–ligand complexes. It identifies interactions between protein and ligand using an interaction model based on atom types and geometric criteria. The same interaction model is also used by the structure-mining tool GeoMine to describe binding sites. In addition to automatic diagram generation, PoseEdit allows users to manually refine the visualization by translating, rotating, or mirroring parts of the structure, adding or removing interactions, and creating or adjusting labels. Final diagrams can be exported in JSON or SVG format. Key benefits Automatic generation of 2D protein–ligand interaction diagrams Interactive editing of molecular structures and interaction annotations Consistent interaction model shared with GeoMine Flexible adjustment of labels, orientation, and layout Export of editable JSON files and publication-ready SVG graphics Web-based use without local software installation Applications Visualization and refinement of protein–ligand binding modes Manual correction and annotation of interaction diagrams Preparation of figures for publications, presentations, and reports Comparison of binding-site interaction patterns Documentation of docking poses and experimentally determined complexes Creation of standardized diagrams for structure-based research Intended use PoseEdit is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery who need editable and publication-ready visualizations of protein–ligand interactions. It is particularly suited for users who want to combine automated interaction detection with manual control over the final diagram.

PoseView automatically generates two-dimensional diagrams of protein–ligand complexes, focusing on the interactions between the protein and its bound ligand. The tool identifies molecular interactions using atom types and geometric criteria and presents them according to established conventions for chemical structure diagrams. The resulting visualizations are designed to match the quality of manually prepared figures used in scientific publications and textbooks. Key benefits Fully automated generation of 2D protein–ligand interaction diagrams Clear visualization of molecular interactions within binding sites Interaction detection based on atom types and geometric criteria Uses established chemical structure drawing conventions Produces publication-quality diagrams Web-based analysis without local software installation Applications Visualization of protein–ligand binding modes Analysis of molecular interactions within protein binding sites Comparison of ligand poses and interaction patterns Preparation of figures for publications, presentations, and reports Interpretation of docking results and experimentally determined complexes Support for structure-based drug discovery and medicinal chemistry Intended use PoseView is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery working with protein–ligand complexes. It is particularly suited for users who need clear, standardized, and publication-ready visualizations of protein–ligand interactions.

ProtGraph is a Python package that converts protein entries from UniProtKB into protein graphs (directed acyclic graphs). It parses UniProtKB SP-EMBL-style entries (e.g. .txt / .dat) including the canonical sequence and rich feature annotations such as isoform sequences, specifically cleaved peptides (e.g. signal peptides, propeptides), and variational changes (e.g. variants, mutations, sequence conflicts). Depending on the configuration, ProtGraph generates graphs that represent all (or selected) combinations of these features, and can be extended with digestion information and post-translational modifications. This enables estimation of theoretical protein/peptide search spaces for species (e.g. human, mouse) or even the complete UniProtKB. Key benefits Graph-based representation of protein sequence variability: models isoforms, cleavage products, variants, and other UniProtKB features in a unified directed acyclic graph. Configurable feature inclusion: generate graphs that include all annotations or focus on selected feature types, depending on the analysis goal. Supports proteomics search-space estimation: helps assess theoretical protein/peptide search spaces across organisms and datasets. Extensible with digestion and PTMs: optionally enriches graphs to better reflect downstream proteomics workflows. Interoperable outputs: exports graphs and sequences in multiple formats, enabling inspection with external graph tools and reuse in downstream pipelines. Applications Generation of protein graphs capturing sequence variants, isoforms, and proteolytic processing Estimation of theoretical protein/peptide search spaces for specific species or UniProtKB-wide analyses Export of custom-tailored FASTA files for proteomics database search workflows Graph export and complexity inspection using external visualization/graph analysis tools End-to-end workflow usage demonstrated via ProGFASTAGen Intended use ProtGraph is intended for researchers in proteomics and computational biology who want to represent protein sequence diversity in a structured, configurable way and derive sequence resources for downstream analyses. Basic bioinformatics skills and command-line experience are recommended, since ProtGraph is a command line tool (CLI).

Protoss is a fully automated tool for adding hydrogen atoms to protein–ligand complexes and assigning chemically plausible protonation and tautomeric states. It optimizes the hydrogen-bond network of the complex to determine suitable hydrogen coordinates for both protein and ligand molecules, supporting reliable preparation of structural models for downstream analysis. Key benefits Fully automated placement of missing hydrogen atoms Assignment of plausible protonation and tautomeric states Joint treatment of protein and ligand molecules Optimization of the hydrogen-bond network Fast, web-based analysis without local installation Supports consistent preparation of protein–ligand structures Applications Preparation of protein–ligand complexes for molecular docking Structure-based drug design and virtual screening Analysis of hydrogen-bond networks Refinement of ligand-binding site models Preparation of structures for molecular modelling and simulation Assessment of protonation states in experimentally determined complexes Intended use Protoss is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery working with protein–ligand complexes. It is particularly suited for users who require automated and chemically consistent hydrogen placement and protonation-state assignment before docking, modelling, or interaction analysis.

The Research Data Management Kit (RDMkit) supports life scientists in managing their research data in accordance with the FAIR Principles (Findable, Accessible, Interoperable, Reusable). Structured along the data lifecycle, RDMkit provides practical guidance, domain-specific recommendations, and role-based advice for implementing effective research data management strategies. It also includes country-specific information and links to relevant infrastructures and resources. Key benefits Structured guidance along the full research data lifecycle Supports implementation of the FAIR Principles Domain-specific recommendations for life science disciplines Role-based advice (e.g. researchers, data stewards, infrastructure providers) Country-specific information and links to national resources Integration within the ELIXIR ecosystem Applications Planning and implementation of research data management strategies Preparation of data management plans (DMPs) Selection of appropriate standards, repositories, and tools Improving data interoperability and reusability Training and guidance in FAIR data practices Intended use RDMkit is intended for life science researchers, data stewards, research infrastructure providers, and project coordinators who aim to improve their research data management practices and ensure FAIR-compliant data handling. It is particularly suited for users seeking structured, practical guidance tailored to their scientific domain, professional role, and national context. If you used this service, please help us improve by completing our short user survey: https://de.surveymonkey.com/r/denbi-service?sc=nbi-sysbio&tool=rdmkit

ReferenceSeeker – Rapid Identification of Suitable Reference Genomes ReferenceSeeker is a tool for the rapid identification of closely related reference genomes for bacterial, archaeal, fungal, protozoan, and viral genome sequences. It applies a scalable hierarchical approach that combines fast k-mer profile-based database searches with the calculation of Average Nucleotide Identity (ANI) values to identify the most suitable reference genomes for downstream analyses. By reducing the number of candidate genomes requiring detailed comparison, ReferenceSeeker provides a fast and efficient solution for reference genome selection. Key benefits Rapid identification of closely related reference genomes Combines fast k-mer-based screening with accurate ANI calculations Scalable approach suitable for large reference genome collections Supports multiple taxonomic groups, including bacteria, archaea, fungi, protozoa, and viruses Reduces computational effort by focusing on the most promising candidates Facilitates reproducible and standardized reference genome selection Applications Selection of suitable reference genomes for comparative genomics Identification of closely related genomes for microbial characterization Support for genome assembly validation and quality assessment Reference genome selection for variant calling and phylogenetic analyses Taxonomic and evolutionary studies based on genome similarity Preparation of downstream bacterial and microbial genomics workflows Intended use ReferenceSeeker is intended for microbiologists, bioinformaticians, microbial genomicists, and comparative genomics researchers who require rapid and reliable identification of suitable reference genomes. It is particularly suited for users working with newly assembled genomes and seeking high-quality references for comparative analyses, annotation, phylogenetics, and genome characterization.

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

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.