Registry Catalogue

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.

The de.NBI Cloud Berlin AI Hub provides artificial intelligence services of the de.NBI Cloud Berlin site, hosted at the Berlin Institute of Health @ Charité (BIH). Its core service is a large language model inference API that allows researchers to access hosted LLMs through simple, OpenAI-compatible REST calls. The service supports research tasks such as literature search, text mining, summarization, and code generation, while running on dedicated on-premises servers in Germany. The model portfolio is updated regularly and extended with new models. Key benefits Provides hosted LLM inference services for research use Offers an OpenAI-compatible REST API for easy integration Runs on on-premises infrastructure in Germany Supports literature search, text mining, summarization, and code generation Regularly updated with additional models and AI services Applications Integration of LLMs into research workflows and applications Automated summarization and text mining of scientific content Support for literature exploration and information extraction Code generation and assistance for computational research tasks Development of AI-enabled tools using hosted inference endpoints Intended use The de.NBI Cloud Berlin AI Hub is intended for life science researchers, bioinformaticians, data scientists, software developers, and research infrastructure teams who want to use hosted AI services in scientific workflows. It is particularly suited for users who need programmatic access to LLMs through a familiar API format while relying on infrastructure operated on-premises in Germany.

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.

EBImage – Image Processing and Analysis in R EBImage is an R/Bioconductor package for general-purpose image reading, writing, processing, and analysis. It provides tools for working with biological image data and is particularly useful in high-throughput microscopy-based cellular assays. EBImage supports image transformation, cell segmentation, and extraction of quantitative cellular descriptors for downstream analysis. Key benefits R/Bioconductor package for image processing and analysis Supports reading, writing, and manipulation of image data Provides tools for microscopy image transformation and segmentation Enables extraction of quantitative cellular descriptors Integrates image analysis into reproducible R-based workflows Applications Processing and analysis of microscopy images Cell segmentation in high-throughput cellular assays Quantification of cellular phenotypes from image data Image transformation, filtering, and feature extraction Integration of image-derived measurements with downstream statistical analysis Intended use EBImage is intended for bioimage analysts, cell biologists, bioinformaticians, microscopy researchers, and R users who need programmable tools for image processing and quantitative image analysis. It is particularly suited for users working with high-throughput microscopy assays who want to segment cells, extract cellular features, and analyse image-derived data within the Bioconductor ecosystem.

E-CRISP is a computational tool for designing and evaluating guide RNAs for CRISPR/Cas9-mediated genome editing. The web application uses fast algorithms to identify suitable target sequences and assess the specificity of candidate guide RNAs. E-CRISP also considers genomic context, such as exons, transcripts, and CpG islands, and supports different experimental applications, including knock-out and tagging experiments. Key benefits Designs guide RNAs for CRISPR/Cas9 genome editing Evaluates target specificity of candidate guide RNAs Considers genomic context such as exons, transcripts, and CpG islands Supports different experimental designs, including knock-out and tagging Covers genomes from more than 30 organisms Applications CRISPR/Cas9 guide RNA design Evaluation of potential off-target effects Planning of gene knock-out experiments Design of guide RNAs for tagging experiments Genome editing projects across supported model and non-model organisms Intended use E-CRISP is intended for molecular biologists, genome engineers, geneticists, bioinformaticians, and life science researchers planning CRISPR/Cas9 experiments. It is particularly suited for users who need web-based support for selecting guide RNAs, assessing target specificity, and adapting guide design to specific experimental contexts.

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.

EDGAR (Efficient Database Framework for Comparative Genome Analyses) is an online platform for comparative analysis of prokaryotic genomes. It supports the identification of orthologous genes and enables users to analyse core genomes, pan-genomes, and singletons across multiple bacterial genomes. EDGAR provides tools for exploring conserved and variable gene content, genome organization, synteny, and functional categories in comparative genomics studies. Key benefits Supports comparative analysis of bacterial and prokaryotic genomes Identifies orthologous genes across multiple genomes Calculates core genomes, pan-genomes, and singleton genes Provides visualizations such as synteny plots and Venn diagrams Enables functional categorization using resources such as KEGG, COG, and GO Applications Comparative genomics of related bacterial species or strains Analysis of conserved and variable gene content Core- and pan-genome analysis Bacterial Taxonomy and Phylogenomics Investigation of gene conservation, genome organization, and synteny Functional interpretation of core and accessory genome components Intended use EDGAR is intended for microbiologists, microbial genomicists, bioinformaticians, geneticists, and comparative genomics researchers working with bacterial or other prokaryotic genome data. It is particularly suited for users who want to compare multiple genomes, identify orthologous genes, analyse core and pan-genomes, and explore gene conservation, evolution, and function.

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.

eggNOG – Orthology and Functional Annotation Resource eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) is a database of hierarchically organized orthologous gene groups and their functional and evolutionary annotations. It provides orthology predictions across thousands of bacterial, archaeal, eukaryotic, and viral genomes, together with multiple sequence alignments, phylogenetic trees, and annotations from established functional resources. eggNOG supports comparative genomics and the transfer of functional information between evolutionarily related genes. Key benefits Hierarchically organized orthologous groups across numerous taxonomic levels Broad coverage of bacterial, archaeal, eukaryotic, and viral genomes Functional annotations linked to resources such as Gene Ontology, KEGG, UniProtKB, Pfam, CAZy, and CARD Multiple sequence alignments and phylogenetic trees for orthologous groups Interactive access through a web interface and bulk data downloads Supports precise functional annotation based on orthology rather than general sequence similarity Applications Identification of orthologous genes and gene families Functional annotation of genes and proteins Comparative analysis of genomes, transcriptomes, and metagenomic gene catalogues Investigation of gene-family evolution across taxa Exploration of multiple sequence alignments and phylogenetic relationships Transfer of functional annotations to newly sequenced organisms Intended use eggNOG is intended for comparative genomicists, evolutionary biologists, microbiologists, bioinformaticians, and researchers analysing genomes, transcriptomes, or metagenomes. It is particularly suited for users who need orthology-based functional predictions and evolutionary context for genes or proteins.