Registry Catalogue

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 Uses established chemical structure drawing conventions Produces publication-quality 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 ligand poses and interaction patterns Interpretation of docking results and experimentally determined complexes Creation of standardized diagrams for structure-based research Support for structure-based drug discovery and medicinal chemistry 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.

proGenomes – Taxonomic and Functional Annotation of Prokaryotic Genomes proGenomes is a database providing consistent taxonomic and functional annotations for bacterial and archaeal genomes. It covers 87,920 genomes from more than 12,000 species, supporting comparative analyses across a broad range of prokaryotic diversity. Users can interactively explore the resource and download customized genome subsets, for example by taxonomic clade, species representatives, or habitat-specific organisms. Key benefits Consistent taxonomic and functional annotation of prokaryotic genomes Covers bacterial and archaeal genomes across more than 12,000 species Supports interactive exploration and customized data downloads Enables selection of genome subsets by taxonomy, representative species, or habitat Useful for comparative genomics and microbiome research Applications Comparative analysis of bacterial and archaeal genomes Selection of representative genome sets for downstream analyses Exploration of taxonomic and functional diversity in prokaryotes Construction of custom reference genome collections Support for metagenomics and microbiome interpretation workflows Intended use proGenomes is intended for microbiome researchers, microbial genomicists, bioinformaticians, comparative genomics researchers, and microbial ecologists who need consistently annotated bacterial and archaeal genome collections. It is particularly suited for users who want to explore prokaryotic diversity, build customized genome datasets, or integrate taxonomic and functional genome information into microbiome analyses.

ProteinsPlus is a comprehensive web platform for the analysis, visualization, and processing of protein structures and protein–ligand complexes. Designed for structure-based life science research, the platform integrates a broad collection of tools supporting protein preparation, binding site analysis, docking, structure validation, and protein–ligand interaction analysis. All services are accessible through an intuitive web interface, while many are also available via REST APIs and standalone software for automated or large-scale analyses. Key benefits Comprehensive suite of tools for protein structure-based research User-friendly web interface with REST API access for automated workflows Supports protein preparation, binding site analysis, docking, and structure validation Many tools available as standalone software for batch processing Widely used resource with hundreds of thousands of annual users Applicable to drug discovery, agrochemistry, biotechnology, and structural biology Applications Protein preparation and protonation (Protoss) Binding site detection, characterization, and druggability assessment (DoGSite) Protein–ligand docking and pose evaluation (JAMDA, PoseView) Structure quality assessment and electron density validation (EDIA, StructureProfiler) Protein ensemble generation and conformational analysis (SIENA) Protein–protein interface classification (HyPPI) Metal coordination analysis (METALizer) Water placement prediction and crystal channel analysis (WarPP, LifeSoaks) Structural pattern mining and mutation analysis (GeoMine, MicroMiner) Integration of structural and bioactivity information (ActivityFinder) Included tools Protoss – Automated hydrogen assignment and protonation state prediction DoGSite3 / DoGSiteScorer – Binding site detection and druggability prediction PoseView/Edit – Two-dimensional visualization of protein–ligand interactions SIENA – Automated generation of protein structure ensembles HyPPI – Classification of protein–protein interfaces EDIA – Evaluation of electron density support for protein structures METALizer – Analysis of metal coordination geometries ActivityFinder – Association of protein structures with ChEMBL bioactivity data LifeSoaks – Analysis of protein crystal channels for soaking experiments WarPP – Prediction of water molecule positions near binding sites StructureProfiler – Quality assessment of protein–ligand complexes GeoMine – Geometric pattern search in protein structures JAMDA – Automated protein–ligand docking workflow MicroMiner – Structural microenvironment search for mutation analysis Intended use ProteinsPlus is intended for structural biologists, medicinal chemists, pharmaceutical researchers, computational chemists, bioinformaticians, and life science researchers working with three-dimensional protein structures. It is particularly suited for users performing structure-based drug discovery, protein engineering, ligand design, molecular docking, and structural bioinformatics analyses.

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.

PubPlant is a continuously updated online resource that catalogs fully sequenced plant genomes published in peer-reviewed scientific journals. It provides interactive cladograms, timelines, and overview visualizations to explore the development and taxonomic distribution of published plant genome projects. PubPlant includes high-quality genome assemblies from the Archaeplastida group, including land plants and primary algae, provided that the assemblies contain structural gene annotations. Key benefits Continuously updated catalog of published plant genomes Focus on peer-reviewed, fully sequenced plant genome assemblies Inclusion criteria require high-quality assemblies and structural gene annotation Interactive cladograms and timelines for exploring plant genome publications Useful overview of genome sequencing progress across Archaeplastida Applications Searching for published plant genome assemblies Exploring the taxonomic distribution of sequenced plant genomes Tracking the chronology of plant genome publications Identifying reference publications for plant genome projects Supporting comparative plant genomics and genome resource surveys Intended use PubPlant is intended for plant scientists, genome researchers, bioinformaticians, comparative genomics researchers, and database curators who need an overview of published plant genome resources. It is particularly suited for users who want to identify sequenced plant species, explore genome publication trends, or retrieve references for peer-reviewed plant genome assemblies.

Rarr – R Interface for Reading and Writing Zarr Arrays Rarr is an R package that provides a simple interface for reading and writing individual Zarr arrays. Zarr is a format for chunked, compressed, N-dimensional arrays that enables efficient access to subsets of large datasets and supports both local and cloud-based storage. Rarr is implemented in R and C and does not rely on external libraries or APIs for interfacing with Zarr arrays, while bundled compression libraries provide support for compressed datasets. Key benefits Simple R interface for reading and writing Zarr arrays Supports chunked, compressed, N-dimensional array data Enables efficient access to subsets of large array datasets Works with local and cloud-compatible Zarr storage formats Includes bundled compression support, such as blosc Applications Accessing Zarr-based scientific datasets from R Reading and writing large multidimensional array data Working with chunked and compressed omics or imaging datasets Integrating Zarr data into R-based analysis workflows Supporting cloud-compatible data access patterns for large datasets Intended use Rarr is intended for bioinformaticians, data scientists, computational biologists, and R users working with large multidimensional array datasets. It is particularly suited for users who need lightweight access to Zarr arrays from R, including workflows involving chunked, compressed, or cloud-stored scientific data.

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.

rhdf5 – HDF5 Data Access for R rhdf5 and Rhdf5lib provide an interface between the HDF5 file format and R. HDF5 supports portable storage and access of very large or complex datasets together with rich metadata. The rhdf5 package enables R applications to exchange HDF5-based data with other software and to work with datasets that may exceed available memory. It is particularly important in single-cell data analysis, where HDF5-based formats are widely used to manage rapidly growing dataset sizes. Key benefits Provides HDF5 file access from R and Bioconductor workflows Supports storage and exchange of large and complex datasets Enables access to data that may be larger than available RAM Handles structured data and metadata in a portable file format Serves as infrastructure for many single-cell and large-scale omics tools Applications Reading and writing HDF5 files in R Exchange of large datasets between R and other software environments Management of complex omics and single-cell datasets Development of scalable R/Bioconductor analysis workflows Access to disk-based data structures for memory-efficient analysis Intended use rhdf5 is intended for bioinformaticians, computational biologists, single-cell researchers, R package developers, and data scientists who need reliable access to HDF5 files from R. It is particularly suited for workflows involving large or complex datasets, structured metadata, and interoperable data exchange between R and other analysis tools.