Registry Catalogue

MapMan is a desktop tool for visualizing plant high-throughput omics data in the context of biological pathways and processes. It maps protein function annotation results, generated with Mercator4, together with gene expression data onto schematic pathway diagrams. This visual approach enables researchers to inspect global biological responses and identify altered biological processes within their experiments. Key benefits Desktop application for Windows, macOS, and Linux Visualization of gene expression data together with protein function annotations Mapping of experimental data onto schematic pathway and process diagrams Compatible with current and legacy Mercator4 annotation outputs Supports custom diagrams using pathway images and protein/gene mapping files Applications Visualization of plant gene expression data in biological context Exploration of metabolic pathways and other biological processes Inspection of global response patterns across experiments Use of predefined MapMan diagrams, including Mercator4-compatible diagram sets Creation of custom pathway diagrams for project-specific analyses Intended use MapMan is intended for plant scientists, molecular biologists, bioinformaticians, and omics researchers working with plant gene expression data and protein function annotations. It is particularly suited for users who want to visually explore large datasets using pathway-based diagrams and Mercator4-compatible classification outputs.

The MdOA toolbox is a collection of browser-based applications that support end-to-end analysis and interpretation of omics datasets – from mass-spectrometry metaproteomics to feature selection for biomarker panels and interpretable clinical risk modeling. All tools run in the browser (supported by de.NBI Cloud); no local installation is required. Key Features Web-first access: Use the tools directly in your browser; no setup on local machines. Purpose-built apps: Each app tackles a concrete task (MS/MS metaproteomics analysis, molecule/feature panel selection, interpretable sepsis risk prediction). Transparent & interpretable outputs: Interactive visuals (PCA/cluster plots, model metrics, SHAP explanations) to aid result interpretation. Hosted on German research infrastructure with clear ownership/contact Tools MPA Cloud – MetaProteomeAnalyzer Cloud – Web version of the MetaProteomeAnalyzer for metaproteomics/proteomics MS/MS data processing and visualization. Ideal for taxonomic/functional insights from complex communities. (Funded by de.NBI) Prophane - Provides a tailored and fully automated workflow for metaproteomics analysis with special focus on metaprotein taxonomic and functional annotation. For the annotations you can choose between different databases and algorithm. (Funded by DFG) OMEx – Omics Molecule Extractor – Open-source web app for selecting molecules and multi-marker panels from large omics tables using a stepwise, ML-based feature-selection workflow; includes interactive plots and performance metrics. (Funded by Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V.) SBC-SHAP – A lightweight clinical utility app for real-time sepsis risk prediction using only age, sex, and complete blood count (CBC); provides SHAP-based explanations. (Funded by OVGU Magdeburg) Applications Metaproteomics & proteomics: Process and explore high-resolution MS/MS datasets; derive community composition and functional signals (MPA Cloud). Biomarker discovery & panel building: Identify reproducible multi-marker panels for classification tasks in omics studies (OMEx). Interpretable clinical modeling (research use): Rapid, explainable risk scoring from routine lab values to support methodological exploration and education (SBC-SHAP). Target Audience Researchers in microbiome/metaproteomics and translational omics needing browser-based analysis/selection tools. Method developers & educators demonstrating interpretable ML on clinical-style lab data (with clear non-diagnostic disclaimers). Training & workshops within the de.NBI ecosystem (e.g., metaproteomics courses highlighting MPA Cloud). The Applied Metaproteomics Workshop takes places yearly in Magdeburg, for further information take a look at our training course calendar.

MeDeCom is a computational method within DecompPipeline for the decomposition of heterogeneous DNA methylation data. It applies matrix factorization with biologically motivated constraints and regularization to discover and quantify latent methylation components bulk DNA methylation samples. The workflow is complemented by preprocessing functionality in DecompPipeline and interactive result exploration through the FactorViz Shiny application. Key benefits Reference-free deconvolution of heterogeneous bulk DNA methylation profiles Detects latent methylation components without predefined cell-type references Uses matrix factorization with biologically motivated constraints and regularization Includes associated preprocessing support through DecompPipeline Supports interactive exploration of results with FactorViz Applications Decomposition of bulk DNA methylation data Discovery and quantification of latent methylation components Exploration of sample composition and methylation patterns Visualization and interpretation of deconvolution results Intended use MeDeCom is intended for epigenomics researchers, bioinformaticians, computational biologists, and DNA methylation researchers working with heterogeneous methylation datasets. It is particularly suited for users who need reference-free deconvolution of bulk DNA methylomes and want to identify underlying methylation components for downstream biological interpretation.

MediaDive is a comprehensive resource for cultivation media and growth conditions of microorganisms. It provides more than 3,000 standardized and manually curated media recipes for bacteria, archaea, fungi, yeasts, algae, and protists. In addition to searching and comparing established recipes, users can adapt existing media or create and share custom formulations using the integrated Medium Builder. Key benefits Extensive collection of standardized cultivation media Expert curation by DSMZ specialists Search options based on taxonomy and isolation source Tools for comparing, modifying, and sharing media recipes Integration with related DSMZ microbial resources Programmatic access through a RESTful API Accessible and user-friendly web interface Applications Identification of suitable cultivation media for microorganisms Comparison of media formulations and growth conditions Design and adaptation of custom media recipes Support for microbial isolation and cultivation experiments Linking cultivation information with strain and taxonomy data Integration of media data into laboratory and bioinformatics workflows Intended use MediaDive is intended for microbiologists, culture collection staff, microbial ecologists, biotechnology researchers, and laboratory scientists who need reliable information on cultivation media and growth conditions. It is particularly suited for users planning microbial cultivation experiments, optimizing media formulations, or integrating standardized media information into experimental and computational workflows.

MeltDB is an open-source platform for the analysis of raw GC-MS and LC-MS metabolomics data. It supports flexible workflows for mass spectrometry data processing, metabolite identification, normalization, statistical analysis, and visualization. MeltDB integrates established open-source tools such as XCMS, MassSpecWavelet, and metaB, and enables metabolite identification using the Golm Metabolome Database (GMD) or user-defined NIST libraries. Key benefits Open-source platform for GC-MS and LC-MS metabolomics data analysis Flexible workflows for raw data processing and integration of preprocessed datasets Metabolite identification via GMD and user-defined NIST libraries Normalization using internal standards such as ribitol, dry weight, or cell volume Integration with genomics and transcriptomics resources for multi-omics interpretation Applications Processing and analysis of raw metabolomics mass spectrometry data Metabolite identification and abundance normalization Statistical analysis using t-tests, ANOVA, and hierarchical clustering Exploratory visualization with PCA, ICA, heatmaps, and cluster analysis Integration of metabolite and transcript data in biological pathway contexts Intended use MeltDB is intended for metabolomics researchers, systems biologists, bioinformaticians, and mass spectrometry users working with GC-MS or LC-MS datasets. It is particularly suited for users who need an integrated platform for metabolomics data processing, statistical analysis, visualization, and connection to transcriptomics or genomics data.

Mercator4 is an online tool for assigning functional annotations to protein sequences from land plants, including flowering plants, ferns, horsetails, mosses, liverworts, and hornworts. It can also annotate highly conserved proteins from green algae groups of Archaeplastida. User-submitted protein sequences can be visualized online or downloaded for further analysis, for example in combination with MapMan. Key benefits Functional annotation of plant protein sequences Coverage of major land plant lineages and conserved green algae proteins Online visualization of annotation results Downloadable outputs for downstream analysis Compatible with MapMan-based pathway visualization workflows Applications Functional annotation of newly sequenced plant proteins Classification of proteins for pathway-based interpretation Preparation of annotation files for MapMan visualization Exploration of functional profiles in plant omics datasets Comparative analysis of plant protein function categories Intended use Mercator4 is intended for plant scientists, molecular biologists, bioinformaticians, and omics researchers working with plant protein sequence data. It is particularly suited for users who want to assign functional categories to plant proteins and use these annotations for downstream visualization, pathway interpretation, or comparative analysis.

The Metagenomics Toolkit is a scalable, data-agnostic workflow for the automated analysis of metagenomic datasets derived from both short (Illumina) and long (Oxford Nanopore Technologies) reads. Designed for versatility, reproducibility, and minimal hardware requirements, the Toolkit streamlines the complete metagenomic analysis pipeline – from raw reads to annotated, interpretable genomes. Key Benefits: * End-to-end automation: Covers all core steps of metagenome processing, from quality control to annotation. * Cross-platform support: Compatible with Illumina and Oxford Nanopore datasets. * Resource-efficient assembly: Integrates a machine-learning–optimized assembler that adjusts RAM requests dynamically to match actual needs, reducing dependence on high-memory servers. * Comprehensive feature set: Beyond classical analysis, MGtk identifies plasmids, recovers unassembled taxa, and models microbial interdependencies. * Modular and reproducible: Fully containerized for scalable use on local systems, HPC clusters, or the cloud. Tools; Modules: * Quality Control; Preprocessing – Adapter trimming, quality filtering, and contamination screening. * Assembly; Binning – Optimized short- and long-read assemblers with automatic parameter tuning. * Annotation – Taxonomic and functional genome annotation with standardized outputs. * Plasmid Identification – Integrates multiple tools for reliable plasmid detection and classification. * Community Reconstruction – Recovery of unassembled members and dereplication for non-redundant genome sets. * Microbial Interaction Modeling – Combines co-occurrence analysis with genome-scale metabolic models to explore ecological dependencies. Applications: * Reconstruction of microbial community structure and function from complex metagenomes. * Discovery of novel plasmids, symbionts, and microbial interactions in environmental or host-associated samples. * Benchmarking and teaching of scalable metagenomics workflows. * Predictive modeling of community metabolism and interspecies relationships. Intended Use: The Metagenomics Toolkit is ideal for microbiome researchers, bioinformaticians, and systems biologists seeking a reproducible, resource-efficient workflow for analyzing metagenomic datasets from any sequencing platform.

METALizer is a tool for predicting and evaluating the coordination geometry of metal ions in metalloproteins. It compares potential coordination geometries with the arrangement observed in a protein structure and enables interactive assessment of metal–ligand distances against statistics derived from structures in the Protein Data Bank (PDB). Key benefits Predicts plausible coordination geometries for metal ions in proteins Compares predicted and observed metal-binding arrangements Evaluates metal–ligand interaction distances using PDB-derived statistics Provides interactive visualization and comparison of coordination geometries Supports objective assessment of metal-binding site quality Web-based analysis without local software installation Applications Characterization of metal-binding sites in metalloproteins Validation of metal coordination geometry in experimental structures Comparison of observed interaction distances with structural reference data Identification of unusual or potentially incorrect metal-binding arrangements Analysis of protein–metal and protein–ligand interfaces Support for structural biology, enzyme research, and structure-based modelling Intended use METALizer is intended for structural biologists, crystallographers, bioinorganic chemists, enzymologists, and computational chemists working with metalloprotein structures. It is particularly suited for users who want to evaluate whether an observed metal coordination geometry and its interaction distances are consistent with known structural patterns in the PDB.

Metalog is a repository of manually curated metadata for metagenomics samples from diverse habitats across the globe. It provides consistently annotated sample metadata for human, animal, marine, and other environmental microbiome studies. The resource contains 80,423 human samples, including 66,527 gut microbiome samples, 10,744 animal samples, 5,547 ocean water samples, and 23,455 samples from other environmental habitats such as soil, sediment, and fresh water. Key benefits Manually curated metadata repository for metagenomics samples Covers human, animal, marine, and environmental microbiome datasets Provides consistent annotation of habitat-specific core features Supports global exploration and comparison of metagenomics samples Helps improve metadata quality and reuse in microbiome research Applications Discovery of metagenomics samples by habitat or metadata features Comparative microbiome studies across humans, animals, and environments Selection of datasets for reanalysis or meta-analysis Exploration of disease status, medication, host species, captivity, salinity, and other variables Support for harmonized metadata use in large-scale microbiome research Intended use Metalog is intended for microbiome researchers, metagenomics researchers, bioinformaticians, microbial ecologists, and data curators who need consistently annotated metadata for sample discovery, dataset comparison, and large-scale microbiome analyses. It is particularly suited for users who want to identify relevant metagenomics datasets across habitats and reuse metadata in a standardized way.

MPA Cloud provides web-based access to the MetaProteomeAnalyzer (MPA), an open-source platform for metaproteomics data analysis and interpretation. It enables researchers to identify taxonomic and functional relationships between proteins from tandem mass spectrometry experiments and supports comprehensive analysis of microbial community composition and function. Key benefits Web-based and user-friendly metaproteomics analysis platform Supports multiple protein database search engines Identification of taxonomic and functional protein relationships Metaprotein grouping to reduce redundancy and improve interpretation Integration of metadata from UniProt, KEGG, and NCBI Taxonomy Tools for sample comparison, visualization, and data export Applications Analysis of tandem mass spectrometry metaproteomics datasets Taxonomic and functional profiling of microbial communities Protein identification and quantification workflows Comparison of multiple biological samples Generation of publication-ready exports and visualizations BLAST-supported downstream analysis and validation Intended use MPA Cloud is intended for researchers in microbiome research, proteomics, and systems biology who require accessible and reproducible metaproteomics analysis workflows. It is particularly suited for users who want to analyze mass spectrometry data without installing and maintaining local analysis infrastructure. Resources Intro Videos : Intro videos are available here (http://www.mpa.ovgu.de/index.php/tutorials/video-tutorials/) to provide guidance on using the MetaProteomeAnalyzer software.

This consulting service supports researchers in planning and executing metaproteomics projects, covering the entire workflow from experimental design to bioinformatic analysis and interpretation. Support includes both wet-lab planning (e.g. sample handling, study design, metadata collection, common pitfalls) and computational analysis (e.g. database strategy, peptide/protein identification and quantification, quality control, statistics, and reporting). Consulting and analysis services are offered free of charge for scientific users. Key benefits End-to-end support for metaproteomics projects Integration of experimental design and computational strategy Expert guidance on database construction and search strategies Quality control, troubleshooting, and reproducible analysis workflows Support for statistical analysis, visualization, and interpretation Free-of-charge consulting for scientific users Applications Study design and metadata planning for metaproteomics experiments Selection and optimization of protein databases and search parameters Peptide and protein identification and quantification workflows Quality assessment and troubleshooting of LC-MS/MS data Statistical analysis and data visualization Support in preparing figures and methods text for publications Intended use This service is intended for researchers in microbiome research, proteomics, and systems biology who plan or conduct metaproteomics studies and require expert guidance throughout the workflow. It is particularly suited for scientific users who seek structured support in both experimental planning and bioinformatic data analysis.

metaTraits – Unified Microbial Trait Resource metaTraits is a unified microbial trait resource that integrates experimentally derived trait information with genome-based trait predictions. It combines culture-derived data from resources such as BacDive, BV-BRC, JGI IMG, and GOLD with predictions for isolate genomes and metagenome-assembled genomes from proGenomes and SPIRE. Covering more than 2.2 million genomes and over 140 harmonized traits, metaTraits enables users to explore microbial morphology, physiology, metabolism, environmental preferences, and lifestyle features in a standardized framework. Key benefits Integrates culture-derived traits and genome-based trait predictions Covers more than 2.2 million isolate and metagenome-assembled genomes Provides over 140 harmonized microbial traits mapped to standardized ontologies Links records to original evidence and source databases Cross-referenced to both NCBI and GTDB taxonomies Applications Exploration of microbial traits across genomes and habitats Comparative analysis of morphology, physiology, metabolism, and lifestyle features Linking microbial genomes to environmental preferences such as temperature, salinity, and oxygen tolerance Trait-based interpretation of microbiome and metagenomics datasets Selection of organisms or genome groups based on functional or ecological traits Intended use metaTraits is intended for microbiome researchers, microbial ecologists, bioinformaticians, comparative genomics researchers, and data curators who need standardized access to microbial trait information. It is particularly suited for users who want to compare traits across microbial genomes, connect metagenomic findings with ecological or physiological properties, or integrate trait data into large-scale microbiome analyses.