Registry Catalogue

**CellNetAnalyzer (CNA)** is a MATLAB toolbox for the computational analysis of biological networks, including metabolic, signaling, and gene regulatory systems. Models can be explored through an interactive graphical user interface with network maps or analyzed programmatically via command-line API functions. CNA supports stoichiometric and constraint-based modelling of metabolic networks as well as Boolean, logical, and interaction-graph-based analysis of signaling and regulatory networks. ### Key benefits - Integrated environment for metabolic, signaling, and regulatory network analysis - Interactive GUI with network maps as well as command-line access via API functions - Supports major constraint-based modelling methods such as FBA, FVA, MFA, and elementary modes analysis - Enables analysis of logical and Boolean models of signaling and regulatory networks - Compatible with SBML model exchange standards ### Applications - Flux balance analysis, flux variability analysis, and metabolic flux analysis - Production envelope and yield space analysis - Elementary-modes analysis and computational strain design - Analysis of signaling paths, feedback loops, and global interdependencies - Logical simulations and prediction of qualitative input–output behavior ### Intended use CellNetAnalyzer is intended for systems biologists, metabolic engineers, bioinformaticians, and computational life scientists working with biological network models. It is particularly suited for users who need a MATLAB-based environment for interactive exploration and computational analysis of metabolic, signaling, or regulatory networks.

ChrAccR is an R package for the comprehensive analysis of chromatin accessibility data from bulk and single-cell experiments. It supports quality control, exploratory analysis, dimension reduction, clustering, transcription factor activity estimation, and the identification and characterization of differentially accessible regions. ChrAccR is designed to scale from bulk datasets with hundreds of samples to single-cell datasets containing tens or hundreds of thousands of cells. With only a limited number of R commands, ChrAccR generates interactive analysis reports that provide a structured overview of the dataset and can easily be shared with collaborators. In addition, the package includes utility functions for customized and more advanced R-based analyses. Key benefits Comprehensive workflow for bulk and single-cell chromatin accessibility analysis Supports quality control, exploratory analysis, clustering, and dimension reduction Includes transcription factor activity estimation Identifies and characterizes differentially accessible regions Scales to large datasets with hundreds of samples or many thousands of cells Generates interactive and shareable analysis reports Requires only a limited number of R commands for standard analyses Provides utility functions for advanced custom scripting Applications Analysis of bulk ATAC-seq datasets Analysis of single-cell chromatin accessibility data Quality assessment of chromatin accessibility experiments Unsupervised dimension reduction and clustering Estimation of transcription factor activities Identification of differentially accessible regions Generation of interactive reports for collaborative projects Development of customized chromatin accessibility analysis workflows in R Intended use ChrAccR is intended for researchers working with chromatin accessibility data, bioinformaticians, and institutional core facilities providing ATAC-seq services. It is particularly suited for users with limited bioinformatics experience, researchers new to chromatin accessibility analysis, and advanced users who want to extend standard analyses with custom R scripts.

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.

DESeq2 is an R/Bioconductor package for differential analysis of high-throughput sequencing count data. It is widely used for RNA-seq differential expression analysis and can also be applied to other sequencing-based assays that produce count data, including ChIP-seq, ribosome profiling, CLIP, metagenomics, and HT-CRISPR screens. DESeq2 models count data using the negative binomial, also known as Gamma-Poisson, distribution and provides robust methods for normalization, dispersion estimation, statistical testing, and result interpretation. Key benefits Established R/Bioconductor package for sequencing count data analysis Robust differential expression and differential abundance testing Suitable for RNA-seq and other count-based high-throughput assays Includes normalization, dispersion estimation, statistical testing, and shrinkage methods Integrates well into reproducible R and Bioconductor workflows Applications Differential expression analysis of RNA-seq data Differential analysis of ChIP-seq, CLIP, and ribosome profiling count data Differential abundance analysis in metagenomics workflows Analysis of HT-CRISPR screen count data Statistical comparison of sequencing-based experiments across conditions Intended use DESeq2 is intended for bioinformaticians, transcriptomics researchers, genomics researchers, and life scientists working with sequencing-based count data. It is particularly suited for users who need statistically robust differential analysis within the R/Bioconductor ecosystem.

DEXSeq is an R/Bioconductor package for the analysis of differential exon usage from high-throughput sequencing data. It extends the DESeq methodology from gene-level count analysis to exon-level count analysis, enabling users to identify exons that are used differently between conditions or time points. DEXSeq is particularly useful for studying alternative splicing and transcript regulation from RNA-seq data. Key benefits Detects differential exon usage between experimental conditions Supports analysis of alternative exon inclusion and exclusion Builds on the established DESeq statistical framework Models exon-level count data using the Gamma-Poisson distribution Available as an R/Bioconductor package for reproducible workflows Applications Differential exon usage analysis from RNA-seq data Investigation of alternative splicing events Identification of condition-specific exon inclusion or exclusion Analysis of transcript regulation beyond gene-level expression Comparison of exon usage across conditions, treatments, or time points Intended use DEXSeq is intended for bioinformaticians, transcriptomics researchers, genomics researchers, and molecular biologists who want to analyse exon-level changes in RNA-seq data. It is particularly suited for users interested in alternative splicing, differential exon usage, and transcript-level regulation within the R/Bioconductor ecosystem.

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. Key benefits Fully automated detection of binding pockets and sub-pockets Requires only the three-dimensional protein structure Calculates geometric and physicochemical pocket descriptors Robust due to largely rotation- and translation-invariant prediction Much faster than its predecessor DoGSiteScorer Applications Identification of potential small-molecule binding sites 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 DoGSite3 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 and physicochemical properties.

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 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.

e!DAL-PGP is a research data repository for publishing and preserving plant genomics, phenomics, and other cross-domain research data. It is particularly suited for large or heterogeneous datasets that cannot be deposited in conventional domain-specific repositories because of their volume or data type. The repository supports structured metadata, persistent publication, data discovery, and programmatic access in line with the FAIR Principles. Key benefits Publication and long-term preservation of diverse plant research datasets Supports large, complex, and cross-domain data collections Structured metadata to improve findability, interoperability, and reuse Searchable and browsable repository with dataset access and download statistics Institutional authentication and data submission via ELIXIR AAI Suitable for data accompanying scientific publications and collaborative projects Applications Publication of plant phenotyping and microscopy image collections Deposition of unfinished genome assemblies and genotyping data Sharing of mass spectrometry and other experimental datasets Publication of plant-model visualizations, software, and research documents Preservation of datasets that are too large or unsuitable for central domain repositories Provision of FAIR research data for reuse in plant science and bioinformatics Intended use e!DAL-PGP is intended for plant scientists, bioinformaticians, data stewards, and research projects that need a reliable repository for publishing, sharing, and preserving heterogeneous research data. It is particularly suited for large-scale or cross-domain datasets that require persistent access, rich metadata, and integration into FAIR research data workflows.

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.

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.