Registry Catalogue

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.

MOFA-FLEX – Flexible Bayesian Factor Analysis for Omics Data MOFA-FLEX is a versatile factor analysis framework designed to streamline the construction and training of complex matrix factorization models for omics data. It is built on a probabilistic programming-based Bayesian factor analysis framework that integrates concepts from multiple existing methods while remaining modular and extensible. MOFA-FLEX generalizes widely used matrix factorization tools by incorporating flexible prior options, including structured sparsity priors for multi-omics data and covariate-informed priors for spatio-temporal data, as well as non-negativity constraints and diverse data likelihoods. This allows users to combine model components according to their specific analysis needs. Key benefits Combines concepts from many published matrix factorization methods in one package Allows flexible combination of model components, priors, constraints, and likelihoods Naturally handles missing data within a Bayesian modelling framework Supports interpretable latent factors that summarize sample structure and identify driving molecular features Enables fast inference using GPUs Applications Exploratory analysis of complex omics datasets Multi-omics integration of bulk and single-cell data Spatio-temporal modelling using continuous covariates across space or time Pathway-informed analysis using known gene sets and biological prior knowledge Semi-supervised and guided factor analysis for technical covariates and CRISPR perturbation experiments Intended use MOFA-FLEX is intended for bioinformaticians, computational biologists, systems biologists, single-cell researchers, and multi-omics researchers who need flexible matrix factorization models for complex omics datasets. It is particularly suited for users who want to combine features from different factor analysis methods, analyse bulk or single-cell data, incorporate prior biological knowledge, or model structured datasets such as spatio-temporal data and CRISPR perturbation experiments.

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.

nf-core/epigenomesegmentation is a reproducible Nextflow pipeline for running chromatin and epigenome segmentation analyses using EpiSegMix and EpiSegMixMeth. Rather than providing a new segmentation algorithm itself, the pipeline organizes the complete computational workflow around these methods. It standardizes input handling, execution, software dependencies, and output generation and can be run reproducibly on local systems, HPC clusters, and cloud infrastructure. Built according to nf-core community standards, the pipeline uses containerized and version-controlled software environments and supports established deployment technologies such as Docker, Singularity/Apptainer, and Conda. Key Benefits Reproducible execution of EpiSegMix and EpiSegMixMeth analyses Standardized workflow for epigenome segmentation Portable across local, HPC, and cloud environments Containerized and version-controlled software dependencies Integration into established nf-core and Nextflow infrastructures Applications Running genome-wide chromatin state segmentation workflows Reproducible analysis of histone ChIP-seq, DNA methylation, and open-chromatin data Standardized execution across research groups and computing environments Scaling epigenome segmentation analyses to larger datasets Integration into automated and reproducible bioinformatics workflows Intended Use nf-core/epigenomesegmentation is intended for researchers who want to run EpiSegMix/EpiSegMixMeth in a standardized, reproducible workflow without manually setting up the complete software environment and computational pipeline.

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).

OTP – One Touch Pipeline for NGS Project Processing OTP (One Touch Pipeline) is a comprehensive framework for organizing and processing next-generation sequencing projects. It supports the full workflow from data transfer and project administration to quality monitoring, read alignment, and variant calling. OTP enables automated processing, structured project management, and detailed processing control for operators handling large-scale sequencing data. Key benefits Provides a framework for NGS project organization and processing Supports automated data transfer from temporary to final storage Includes data quality monitoring, read alignment, and variant calling Enables extended project administration and processing control Suitable for standardized operation of large-scale sequencing workflows Applications Management of NGS projects and sequencing data processing Automated transfer and organization of sequencing data Quality monitoring of sequencing datasets Alignment of reads to reference genomes Variant calling within structured NGS processing workflows Intended use OTP is intended for bioinformaticians, sequencing facilities, data managers, infrastructure teams, and genomics research groups who need a structured framework for operating NGS processing pipelines. It is particularly suited for environments where sequencing projects require automation, traceable project administration, and operator-level control across multiple processing steps.

The information system PANGAEA is operated as an Open Access library to archive, publish and distribute georeferenced data from biodiversity- and earth system research. PANGAEA guarantees TRUSTed long-term availability (greater than 10 years) of its content. PANGAEA focuses on georeferenced observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonisation of the data inventory, as well as the commitment of the hosting institutions ensures FAIRness of published data. The PANGAEA Editorial ensures the integrity and authenticity as well as a high usability of all data. Archived data are machine readable and mirrored into our data warehouse which allows efficient compilations of data products. PANGAEA is the designated German Polar Data Centre. Data is freely available and can be used under the terms of the license mentioned. A few data sets are under moratorium due to ongoing projects or sensitive content. The description of each data set is always visible which includes the principal investigator (PI) for contact. Each dataset can be identified, shared, published and cited by using a full citation including a persistent Digital Object Identifier (DOI Name). PANGAEA allows data to be published as independent, self-standing data publication supplements to science articles (example) or as citable data collections in combination with data journals like ESSD, Geoscience Data Journal, Nature Scientific Data, and others. PANGAEA is open to any project, institution, or individual scientist to archive, publish and use data. Start a data submission here. PANGAEA is the globally leading platform for FAIR data management, data curation, and data publication, in earth, environmental and biodiversity science. PANGAEA empowers researchers, communities, and organisations through cutting-edge data publication, data products, data dissemination, technology and active collaboration.

PATCH – Toolbox for Computational Pangenomics PATCH is a freely available collection of command-line tools and web-based services for computational pangenomics, comparative genomics, and graph-based genome analysis. The toolbox supports key pangenome tasks such as alignment-free phylogenetic reconstruction, local sequence alignment search in graphical pangenomes, core genome detection, prediction of pangenome openness, and benchmarking of pangenome software. PATCH helps researchers analyse large collections of genomes, contigs, raw sequencing reads, and sequence graphs in a scalable and reproducible way. Key benefits Provides a toolbox for computational pangenomics and comparative genomics Supports graph-based genome analysis and pangenome-aware sequence search Enables scalable analysis of genomes, contigs, raw reads, and sequence graphs Promotes interoperability through the SOCKS standard interface for colored k-mer sets Includes web-based resources for tool comparison, benchmarking, and exploration Applications Alignment-free phylogenetic reconstruction from FASTA or FASTQ data Local alignment search of nucleotide queries in graphical pangenomes Core genome detection in sequence graphs Prediction of pangenome openness and core genome size Benchmarking and comparison of computational pangenomics tools Included tools SANS – alignment-free and reference-free phylogenetic reconstruction using shared k-mers PLAST – heuristic local alignment search between nucleotide queries and graphical pangenomes Corer – detection of core genome sequences in sequence graphs based on genome frequency thresholds Pangrowth – prediction of pangenome openness and estimation of core genome size PanBench – benchmarking and comparison platform for computational pangenomics tools Intended use PATCH is intended for bioinformaticians, comparative genomics researchers, microbial genomicists, pangenome researchers, and software developers working with large genome collections or pangenome graphs. It is particularly suited for users who need interoperable tools for pangenome analysis, graph-based sequence search, phylogenetic reconstruction, core genome analysis, or benchmarking of pangenomics software.

PIA is a toolbox for mass spectrometry-based protein inference and identification analysis. It enables users to inspect and combine results from common proteomics spectrum identification search engines and perform statistical analyses across datasets. A major focus of PIA lies in integrated protein inference algorithms that derive protein-level conclusions from identified spectra. In addition, PIA supports inspection of peptide-spectrum matches (PSMs), false discovery rate (FDR) calculation across multiple search engine outputs, and visualization of relationships between PSMs, peptides, and proteins. Key benefits Integrated toolbox for protein inference and identification analysis Supports multiple proteomics search engine outputs Combines and compares search engine results seamlessly Protein inference algorithms for robust protein-level interpretation FDR calculation and statistical analysis across datasets Visualization of PSM–peptide–protein relationships Applications Protein inference from tandem mass spectrometry data Inspection and comparison of peptide-spectrum matches Integration of multiple search engine identification results False discovery rate estimation and quality assessment Visualization and interpretation of proteomics identifications Intended use PIA is intended for proteomics researchers, bioinformaticians, and mass spectrometry users who require robust tools for protein inference, statistical validation, and interpretation of proteomics identification results. It is particularly suited for workflows integrating multiple search engine outputs.

PiGx (Pipelines in Genomics) is a suite of reproducible and user-friendly genomics pipelines. All pipelines are easily configured with a simple sample sheet and a descriptive settings file. The result is a set of comprehensive, interactive HTML reports with interesting findings about your samples. Designed to streamline the analysis of high-throughput sequencing data, PiGx offers several specialized pipelines: PiGx BSseq: Processes raw FASTQ files from bisulfite sequencing experiments to analyze DNA methylation patterns. PiGx RNAseq: Handles RNA sequencing data, providing comprehensive analyses including differential expression and quality control. PiGx scRNAseq: Caters to single-cell RNA sequencing data, offering preprocessing, quality control, and downstream analyses. PiGx ChIPseq: Analyzes ChIP sequencing data, performing tasks such as peak calling and generating detailed reports. PiGx CRISPR: (In development) Aims to analyze sequence mutations in CRISPR-Cas9 targeted amplicon sequencing data. Each pipeline is configured using a simple sample sheet and a descriptive settings file, resulting in interactive HTML reports that summarize key findings. PiGx is distributed through GNU Guix, ensuring reproducibility and ease of installation. The source code is available under the GNU General Public License, inviting community contributions.

PLAST – Local Alignment Search in Pangenome Graphs PLAST is a freely available command-line tool for fast heuristic local alignment search in graphical pangenomes. It identifies high-scoring local alignments between nucleotide query sequences and pangenome graphs that can represent thousands of individual genomes. Input pangenomes can be provided as existing sequence graphs or generated from FASTA or FASTQ files containing complete genomes, assembled contigs, or raw sequencing reads. Key benefits Performs fast local alignment search in graphical pangenomes Supports nucleotide queries against pangenome graphs representing many genomes Accepts existing sequence graphs or FASTA/FASTQ-based genome collections Uses a seed-and-extend approach for heuristic sequence alignment Can enrich matches with information about contributing individual genomes Applications Graph-based pangenome analysis Nucleotide sequence search across large genome collections Comparative analysis of complete genomes, contigs, or raw reads Identification of local sequence matches in pangenome contexts Region- or subset-restricted searches based on genome presence or abundance Intended use PLAST is intended for bioinformaticians, comparative genomics researchers, microbial genomicists, pangenome researchers, and evolutionary biologists who need fast local sequence search in graph-based pangenome representations. It is particularly suited for users working with large genome collections who want to search nucleotide sequences in a pangenome-aware way and connect alignment results to the genomes contributing each match.