Registry Catalogue

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.

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.

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.

methylKit is an R package for the analysis and annotation of DNA methylation data from high-throughput bisulfite sequencing experiments. It is designed for reduced representation bisulfite sequencing (RRBS) and related protocols, targeted capture approaches such as Agilent SureSelect Methyl-Seq, and base-resolution hydroxymethylation data generated with methods such as TAB-seq or oxBS-seq. Whole-genome bisulfite sequencing (WGBS) data are also supported through compatible input formats, including direct import of methylation calls from Bismark-aligned BAM files. Key benefits Comprehensive R-based workflow for DNA methylation analysis Supports RRBS, targeted bisulfite sequencing, TAB-seq, oxBS-seq, and WGBS data Handles base-pair resolution methylation and hydroxymethylation data Enables quality assessment, coverage filtering, coverage normalization, and exploratory analysis Supports identification of differentially methylated cytosines (DMCs) and regions (DMRs) between sample groups Provides annotation and visualization functions for downstream interpretation Available through Bioconductor and GitHub Applications Analysis of DNA methylation profiles from bisulfite sequencing Identification of differentially methylated cytosines (DMCs) and regions (DMRs) Comparative methylation analysis across conditions or sample groups Analysis of 5hmC data from TAB-seq or oxBS-seq Quality control and exploratory analysis of methylation datasets Annotation of methylation changes with genomic features Integration into reproducible R/Bioconductor workflows Intended use methylKit is intended for epigenetics researchers, molecular biologists, bioinformaticians, and computational biologists working with high-throughput bisulfite sequencing data. It is particularly suited for users who need an R-based, reproducible workflow for methylation analysis, differential methylation testing, and annotation of base-resolution methylation data.

MicroMiner is a tool for identifying and analyzing single-residue substitutions in protein structures. Based on the SIENA methodology, it searches for residue environments with local sequence and structural similarity across the Protein Data Bank (PDB), user-provided structure collections, or selected subsets of the AlphaFold Protein Structure Database. This enables researchers to explore mutation landscapes in both experimentally determined and predicted protein structures. Key benefits Identifies structurally similar residue environments for single-residue substitutions Combines local sequence and structural similarity Searches the PDB, private structure collections, and AlphaFold Database subsets Supports both experimental and predicted protein structures Applicable to individual domains as well as protein–protein and protein–ligand interfaces Provides filtering options to facilitate downstream interpretation Applications Structural interpretation of amino acid substitutions Exploration of protein mutation landscapes Comparison of local residue environments across related structures Analysis of substitutions in protein domains and binding interfaces Investigation of mutations in experimental and AlphaFold-predicted structures Prioritization of structurally relevant mutation examples for downstream studies Intended use MicroMiner is intended for structural biologists, protein scientists, bioinformaticians, and researchers studying protein variants or mutations. It is particularly suited for users who want to investigate how single-residue substitutions relate to known structural environments, including mutations located in protein–protein or protein–ligand interfaces.

MYB Annotator is a web-based tool for the automated identification and functional annotation of MYB transcription factors from coding DNA (CDS) or protein sequence collections. The tool enables rapid characterization of MYB gene families in newly sequenced plant species by combining homology-based candidate identification with motif analysis and orthology-based functional prediction. It requires no manual preparation of reference datasets, as curated MYB sequences are provided as built-in search baits. Key benefits Automated identification of MYB transcription factors from CDS or protein sequences Functional annotation based on orthology to experimentally characterized MYB proteins Detection of conserved MYB repeats and additional diagnostic sequence motifs No manual preparation of reference sequences required Fast and reproducible annotation workflow suitable for newly sequenced plant genomes Generates both annotated FASTA files and comprehensive summary tables Applications Identification of MYB gene family members in plant genomes and transcriptomes Functional annotation of MYB transcription factors Comparative genomics of MYB gene families across plant species Evolutionary analysis of plant transcription factors Candidate gene identification for functional genomics and breeding studies Annotation of newly assembled genome and transcriptome datasets Intended use The MYB Annotator is intended for plant biologists, genome annotation specialists, bioinformaticians, and evolutionary researchers working with plant genome or transcriptome data. It is particularly suited for users seeking an automated and standardized workflow to identify, classify, and functionally annotate MYB transcription factors in newly sequenced plant species.

A Comprehensive Toolbox for Mass Spectrometry Data Analysis OpenMS is a powerful framework that provides an open-source software library and python bindings, as well as an infrastructure for rapid development of mass spectrometry-related software. The toolbox offers a rich set of tools that can be flexibly combined into powerful analysis workflows, supporting several workflow systems including KNIME, Galaxy, and Nextflow. OpenMS Tools: TOPP: The OpenMS Proteomics Pipeline provides a range of tools for protein identification, quantification, and characterization. TOPPView: TOPPView enables the visualization of MS data, allowing users to explore and understand their results in an intuitive way. MetaProSIP: MetaProSIP is a tool for automated inference of elemental fluxes in microbial communities, enabling researchers to study metaproteomics data. DIAMetAlyzer: DIAMetAlyzer generates targeted assays for metabolomics, streamlining the analysis of small molecules. DIAproteomics: DIAproteomics is a workflow for Data-Independent Acquisition (DIA) MS and statistical post-analysis, enabling researchers to analyze complex proteomic data sets. Nucleic Acid Analysis: NASE is a nucleic acid search engine that supports RNA and DNA mass spectrometry analysis. Protein-Protein Interactions: OpenPepXL enables the study of protein-protein interactions, providing insights into biological processes. Benefits: Flexibility: OpenMS provides a modular framework that allows users to combine tools in various ways to create customized workflows. Reproducibility: The toolbox enables reproducible computational analysis, ensuring that results are consistent and reliable. Comprehensive Analysis: OpenMS offers a wide range of tools for different types of mass spectrometry data analysis, making it a one-stop solution for researchers. FLASH Suite for Top Down Proteomics: Top-Down MS Data Analysis: FLASHDeconv, FLASHIda, and FLASHQuant provide tools for top-down MS data deconvolution, intelligent data acquisition, and quantification. See details here. Selected Workflows: HLA Ligand Atlas: A comprehensive collection of tissue and HLA allele-specific HLA ligands that are naturally presented. MHCquant: A workflow for identification and quantification of HLA ligands. quantMS: A workflow for quantitative mass spectrometry analysis. By using OpenMS, researchers can streamline their mass spectrometry data analysis, gain deeper insights into biological processes, and accelerate discovery in various fields. If you have used this service, please help us improve by taking our short survey (https://de.surveymonkey.com/r/denbi-service?sc=cibi&tool=openms).