Registry Catalogue

BioContextAI is a community hub that connects agentic artificial intelligence with biomedical resources and software via Model Context Protocol (MCP) servers. Its main goal is to foster the development of MCP servers for biomedical research applications that comply with the FAIR4RS principles (Findable, Accessible, Interoperable, and Reusable for Research Software). BioContextAI provides the BioContextAI Registry, a community-driven catalogue of MCP servers supporting AI-based biomedical research workflows. The Registry enables researchers and developers to discover, access, and contribute specialized MCP-based tools and databases enriched with structured metadata. Key benefits Community-driven registry of MCP servers for biomedical AI applications Supports FAIR4RS-compliant software development Enables integration of AI agents with biomedical databases and tools Rich metadata to improve discoverability and interoperability Encourages collaboration between AI developers and biomedical researchers Applications Discovery and reuse of MCP servers for AI-supported biomedical workflows Integration of large language models (LLMs) with biomedical tools and databases Development of interoperable AI-driven research applications FAIR4RS-oriented software development and community contribution Exploration of agent-based AI approaches in life science research Intended use BioContextAI is intended for biomedical researchers, AI developers, research software engineers, and data infrastructure providers who aim to integrate agent-based AI systems with biomedical resources in a FAIR-compliant manner. Optional knowledge about LLM tool usage via MCP is beneficial for advanced development and integration scenarios. Website Contact If you used this service, please help us improve by completing our short user survey.

BiocWorkflowTools – Continuous Integration for Bioconductor Workflows BiocWorkflowTools is a Bioconductor authoring tool that connects the journal manuscript management system with the Bioconductor nightly build system. This enables continuous integration, automated quality control, and reliable workflow functionality as underlying software tools, packages, and resources evolve. The tool supports authors in preparing reproducible Bioconductor workflow manuscripts and helps maintain their technical validity over time. Key benefits Links workflow authoring with the Bioconductor nightly build system Enables continuous integration for workflow manuscripts Supports automated quality control during publication workflows Helps ensure workflow functionality as dependencies evolve Supports community authors in creating reproducible workflows Applications Authoring Bioconductor workflow manuscripts Automated checking of workflow functionality Continuous integration for reproducible bioinformatics tutorials Quality control of executable analysis workflows Support for journal-linked workflow publication processes Intended use BiocWorkflowTools is intended for Bioconductor workflow authors, bioinformatics educators, package developers, journal editors, and reviewers involved in publishing reproducible computational workflows. It is particularly suited for use cases where tutorials or workflow manuscripts should remain executable, testable, and robust despite changes in underlying software dependencies.

biomaRt – R Interface to BioMart and Ensembl biomaRt is an R/Bioconductor package that provides programmatic access to BioMart services. It is widely used to query EMBL-EBI’s Ensembl database from within R, enabling users to retrieve gene, transcript, protein, sequence, annotation, and identifier information in reproducible analysis workflows. biomaRt supports flexible database queries and helps integrate biological annotations directly into downstream bioinformatics analyses. Key benefits Provides an R interface to BioMart services Enables programmatic access to Ensembl annotations Supports reproducible retrieval of gene, transcript, and protein information Facilitates identifier mapping and annotation enrichment Integrates smoothly into R and Bioconductor workflows Applications Querying Ensembl from R Gene, transcript, and protein annotation retrieval Identifier conversion and mapping between database accessions Extraction of genomic coordinates and sequence-related metadata Integration of annotation data into omics analysis pipelines Intended use biomaRt is intended for bioinformaticians, genomics researchers, transcriptomics researchers, Bioconductor users, and life scientists who need programmatic access to biological database annotations. It is particularly suited for workflows that require reproducible annotation retrieval, identifier mapping, or integration of Ensembl data into R-based analyses.

Bork Microbiome Toolbox – Resources for Microbiome and Functional Genomics Analysis The Bork Microbiome Toolbox brings together web-based resources for microbiome research, comparative genomics, functional annotation, pathway exploration, and biological data visualization. The tools support researchers in analysing microbial communities, interpreting functional potential, exploring protein and genome relationships, and visualizing complex biological datasets in accessible, interactive formats. Key benefits Collection of established resources for microbiome and comparative genomics research Supports functional annotation, pathway exploration, and phylogenetic visualization Enables analysis of microbial communities, genomes, proteins, and biological traits Provides interactive web-based access for users with different levels of bioinformatics expertise Useful for both exploratory analysis and integration into microbiome interpretation workflows Applications Functional annotation of genes and proteins Exploration of microbial genomes, traits, and community patterns Visualization of phylogenetic trees and biological networks Interpretation of metagenomic and microbiome datasets Pathway-level and trait-level analysis of microbial functions Included tools eggNOG – orthology-based functional annotation and evolutionary analysis of genes and proteins iPATH – interactive visualization and exploration of biological pathways iTOL – display, annotation, and visualization of phylogenetic trees Enterotypes – classification and exploration of human gut microbiome enterotypes proGenomes – microbial genome resource for comparative and functional genomics Spire, Metalog, and Metatraits – resources supporting microbiome data interpretation, metadata exploration, and trait-level analysis Intended use The Bork Microbiome Toolbox is intended for microbiome researchers, bioinformaticians, microbial ecologists, systems biologists, and life science researchers who need accessible resources for interpreting microbial sequence, genome, pathway, phylogenetic, or trait data. It is particularly suited for users working with metagenomic datasets, microbial genome collections, functional annotations, and microbiome-related comparative analyses. If you use a tool of the Toolbox please take the time to answer our short survey to help us improve our services: https://www.surveymonkey.de/r/denbi-service?sc=hd-hub&tool=borkmicrobiometoolbox

BRENDA – Comprehensive Enzyme Information System BRENDA is one of the world's most comprehensive databases of enzyme functional information. It provides manually curated data on enzymes classified according to the Enzyme Commission (EC) nomenclature, covering thousands of EC numbers across all domains of life. The database contains experimentally validated information extracted from primary literature, including enzyme functions, kinetics, substrate specificity, regulation, and optimal reaction conditions. All entries are critically reviewed by experts to ensure high data quality and consistency. Key benefits Comprehensive, manually curated enzyme information database Covers enzymes from more than 8,800 EC numbers Includes experimentally validated functional and kinetic data Information on substrates, products, inhibitors, cofactors, and enzyme regulation Provides pH and temperature optima as well as expression data Links to metabolic pathways and related biological databases Freely accessible through a user-friendly web interface Applications Enzyme function annotation and characterization Identification of enzyme substrates, products, and inhibitors Investigation of enzyme kinetics and catalytic properties Metabolic pathway analysis and reconstruction Comparative enzymology and systems biology Support for biotechnology, metabolic engineering, and drug discovery Intended use BRENDA is intended for biochemists, molecular biologists, bioinformaticians, systems biologists, and biotechnology researchers seeking comprehensive, high-quality enzyme information. It is particularly suited for users requiring curated experimental data on enzyme function, kinetics, regulation, and metabolism for research, annotation, and computational modelling.

CalibraCurve is an R package for generating and visualizing calibration curves from targeted mass spectrometry-based quantitative data. It supports applications in proteomics, lipidomics, metabolomics, and related omics fields. The package enables users to assess linearity, determine upper and lower limits of quantification, and predict concentrations from measured intensities for absolute quantification. Key benefits Generates calibration curves for targeted quantitative MS data Supports proteomics, lipidomics, metabolomics, and related data Assesses linearity and upper/lower limits of quantification Predicts concentrations from measured intensities Produces customizable, publication-ready ggplot2-based visualizations Applications Absolute quantification in targeted mass spectrometry experiments Evaluation of calibration curve quality and measurement linearity Determination of quantification limits for omics assays Visualization of single or multiple calibration curves Integration into automated workflows using the accompanying Nextflow pipeline Intended use CalibraCurve is intended for proteomics, lipidomics, metabolomics, and mass spectrometry researchers who need reproducible tools for calibration curve generation, quality assessment, and absolute quantification. It is particularly suited for users who want flexible R-based visualization and integration into existing analysis pipelines.

CATS (Crop Analysis Tools Suite) is an online platform that brings together a diverse collection of tools and resources for plant genome analysis, crop research, and research data management. The suite supports sequence similarity searches, microsatellite identification, genome and pan-genome exploration, crop diversity analysis, and the creation of FAIR and ISA-compliant metadata. Key benefits Central access to complementary tools for crop genome analysis and data management Supports sequence search, genome annotation, visualization, and comparative genomics Provides access to current crop reference and pan-genome resources Includes tools for molecular marker and primer development Facilitates barley genomics, genebank exploration, and crop diversity research Supports FAIR and standardized metadata annotation for life science experiments Applications Homology searches against crop genome and pan-genome resources Identification of microsatellites and development of molecular markers Exploration of barley genes, orthologous groups, and genome annotations Analysis of crop diversity, phenotypic traits, and sequence polymorphisms Creation of standardized experimental metadata following ISA and MIAPPE principles Included tools Web BLAST Server – Web-based sequence similarity searches against current crop genome and pan-genome resources MISA Web – Identification of microsatellites and generation of molecular markers or primers PanBARLEX – Exploration of genes and orthologous groups across the barley pan-genome ISA Wizard – User-friendly creation of FAIR and ISA-compliant metadata for life science experiments, including support for MIAPPE-based plant phenotyping metadata BRIDGE – Exploration of the IPK barley genebank collection based on genetic diversity, phenotypic traits, and sequence polymorphisms Intended use CATS is intended for plant geneticists, crop scientists, breeders, molecular biologists, bioinformaticians, and research data managers working with crop genome data and experimental metadata. It is particularly suited for researchers who need integrated access to tools for sequence analysis, genome exploration, marker development, crop diversity analysis, and FAIR data documentation.

**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.

CLUES (Climate, Urbanicity, Environment and Society) is a modular workflow for systematically integrating open-access geospatial environmental data with individual-level health research datasets. It automates the download, harmonisation, and management of data across climate, urbanicity, built and natural environments, air pollution, and regional socioeconomic conditions. CLUES helps researchers enrich health datasets with standardized environmental exposure information for reproducible environmental and public health analyses. Key benefits Modular workflow for linking geospatial environmental data to health datasets Automates data download, harmonisation, and management Covers climate, air pollution, urbanicity, environment, and socioeconomic indicators Supports individual-level exposure assessment in health research Uses open-access data sources for reproducible and transparent analyses Applications Environmental exposure assessment in epidemiological studies Integration of climate and air pollution data with health cohorts Analysis of urbanicity, built environment, and socioeconomic context Preparation of harmonized geospatial covariates for statistical modelling Support for reproducible environmental health and exposome research Intended use CLUES is intended for epidemiologists, public health researchers, environmental health scientists, bioinformaticians, and data analysts who need to integrate geospatial environmental information with health research data. It is particularly suited for studies investigating how climate, urbanicity, air pollution, natural and built environments, or regional socioeconomic conditions relate to individual-level health outcomes.

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.