Registry Catalogue

sORFdb is a dedicated database for short open reading frames (sORFs) and small proteins in bacteria. It integrates sequences from annotated genomes, curated protein resources, and experimentally supported small proteins, applies strict quality filtering, and provides an interactive web interface for searching, browsing, and downloading high-confidence small-protein data. The resource supports the exploration and annotation of bacterial small proteins that are often missed in standard genome annotation workflows. Key benefits Dedicated resource for bacterial sORFs and small proteins Integrates data from annotated genomes, curated resources, and experimental evidence Provides quality-filtered small-protein sequences and clustered protein families Includes alignments, HMMs, metadata, and functional hints where available Offers flexible search, browsing, and dataset download options Applications Discovery and annotation of bacterial small proteins Comparative genomics across bacterial taxa Enhancement of genome re-annotation workflows Exploration of small-protein families, sequence features, and taxonomy Prioritization of candidate small proteins for experimental validation Intended use sORFdb is intended for microbiome researchers, genome annotators, molecular microbiologists, bacterial geneticists, and bioinformaticians who study bacterial coding potential. It is particularly suited for users who need reliable, structured data on short open reading frames, small proteins, and small-protein families for annotation, comparison, or experimental follow-up.

### SpatialData – Data Framework for Spatial Omics **SpatialData** is a data framework, schema, and serialization format for spatial omics datasets. It supports the representation of uni- and multi-modal spatial data, including data generated by common spatial omics technologies. The core Python implementation is developed and maintained by EMBL and scverse and enables structured storage, access, manipulation, visualization, and analysis of spatial datasets. Community implementations for R and JavaScript/TypeScript are available and coordinated with the SpatialData ecosystem. ### Key benefits - Provides a common data model for spatial omics datasets - Supports uni- and multi-modal spatial data representation - Enables structured storage and serialization of spatial datasets - Facilitates access, manipulation, visualization, and analysis workflows - Supports interoperability across tools and programming environments ### Applications - Management of spatial transcriptomics and spatial omics datasets - Integration of data from different spatial omics technologies - Faciliates the development of reproducible spatial data analysis workflows - Interactive exploration and annotation of spatial data - Visualization and sharing of spatial omics results ### Included tools - **spatialdata** – core Python library implementing the SpatialData framework, developed and maintained by EMBL and scverse - **spatialdata-io** – import of data from common spatial omics technologies - **spatialdata-plot** – static plotting for SpatialData objects - **napari-spatialdata** – interactive exploration and annotation in napari - **SpatialData R / SpatialData.js** – third-party implementations for R and JavaScript/TypeScript ### Intended use SpatialData is intended for **spatial omics researchers, bioinformaticians, computational biologists, software developers, and single-cell researchers** who need a standardized way to store, process, analyse, and visualize spatial omics datasets. It is particularly suited for projects that require interoperable data structures across tools, programming languages, and spatial omics workflows.

The Spatial Transcriptomics Toolbox is a collection of tools for processing, quality control, analysis, and visualization of spatial transcriptomics data. The toolbox supports common challenges in imaging-based and high-dimensional spatial omics workflows, including cell overlap detection, spatial clustering, spatially informed dimensionality reduction, cell-segmentation-free analysis, and automated color assignment for complex datasets. Key benefits Collection of complementary tools for spatial transcriptomics workflows Supports quality control, processing, analysis, and visualization tasks Addresses imaging-based spatial transcriptomics data challenges Includes methods for spatial clustering and dimensionality reduction Provides tools for both cell-based and cell-segmentation-free analysis Applications Detection of overlapping cells in imaging-based spatial transcriptomics data Spatial clustering of cells or spatial observations Spatially informed dimensionality reduction of high-dimensional datasets Cell-segmentation-free analysis of spatial transcriptomics data Automated color assignment for visualization of high-dimensional results Included tools ovrl.py – cell overlap detection in imaging-based spatial transcriptomics data SpatialLeiden – spatial clustering for spatial transcriptomics analysis multiSPAETI – spatially informed dimensionality reduction sainsc – cell-segmentation-free analysis clrmappy – automatic color assignment to high-dimensional data Intended use The Spatial Transcriptomics Toolbox is intended for spatial transcriptomics researchers, bioinformaticians, computational biologists, bioimage analysts, and single-cell researchers working with high-dimensional spatial omics data. It is particularly suited for users who need modular tools for spatial data quality control, clustering, dimensionality reduction, visualization, or analysis workflows that do not rely strictly on prior cell segmentation.

SPIRE (Searchable Planetary-scale mIcrobiome REsource) is an integrated metagenomics resource for exploring microbial diversity across global habitats. It brings together consistently processed metagenomic data from approximately 100,000 samples across 739 studies, including 35 billion predicted protein sequences and 1.16 million metagenome-assembled genomes (MAGs). These MAGs are clustered into more than 100,000 species-level clusters, enabling large-scale comparative analysis across habitats, geography, and phylogeny. Key benefits Planetary-scale resource for consistently processed metagenomic data Covers diverse global habitats, studies, and microbial communities Provides access to predicted proteins, MAGs, and species-level genome clusters Supports exploration across habitat, geography, taxonomy, and phylogeny Enables large-scale comparative microbiome and metagenomics research Applications Exploration of microbial diversity across global environments Comparative analysis of metagenome-assembled genomes Search and interpretation of metagenomic protein sequences Construction of habitat- or taxonomy-specific microbiome datasets Functional and phylogenetic analysis of microbial communities Intended use SPIRE is intended for microbiome researchers, microbial ecologists, metagenomics researchers, bioinformaticians, and comparative genomics researchers who need access to large-scale, consistently processed metagenomic resources. It is particularly suited for users who want to explore microbial diversity across habitats, compare MAGs or protein sequences, and place local microbiome findings into a global ecological context.

Statistical, bioinformatics and machine learning consulting for proteomics data provides comprehensive support for proteomics research, covering the full data lifecycle from raw data processing to interpretable results. The consulting service combines bioinformatics and biostatistics expertise to advise on study design, software selection, analytical strategy, and downstream interpretation. Analyses can also be performed directly, including preprocessing, identification, quantification, statistical modelling, machine learning, visualization, and publication support. Key benefits End-to-end support from raw proteomics data to biological interpretation Combined expertise in bioinformatics, biostatistics, and proteomics workflows Support for study design, software selection, and analytical strategy Direct execution of analyses, including statistics and machine learning Publication-ready reporting, visualizations, and methods support Applications Preprocessing, quality control, normalization, and format conversion Peptide identification, protein inference, and protein quantification Statistical analysis including regression, survival analysis, and biomarker studies Machine learning for classification, clustering, validation, and model interpretation Development or application of user-friendly proteomics workflows Intended use This consulting service is intended for proteomics researchers, life scientists, clinical researchers, bioinformaticians, and research groups who need expert support for the planning, analysis, interpretation, or publication of proteomics studies. It is particularly suited for projects requiring robust statistical design, reproducible workflows, machine learning approaches, or publication-ready analysis outputs.

STRING is a database of known and predicted protein–protein interactions. It covers both direct physical interactions and indirect functional associations, integrating evidence from experimental data, computational predictions, knowledge transfer between organisms, and curated interaction databases. STRING enables users to explore protein interaction networks, functional associations, and biological context across a broad range of organisms. The current STRING database covers more than 20 billion interactions between over 59 million proteins from over 12,500 organisms. Key benefits Integrates known and predicted protein–protein associations Covers direct physical and indirect functional interactions Combines evidence from experiments, databases, text mining, co-expression, and genomic context Large organism coverage with searchable protein interaction networks Provides web access, downloads, APIs, and Cytoscape integration Applications Exploration of protein interaction networks Functional interpretation of gene or protein lists Identification of interaction partners and pathway context Comparative analysis of functional associations across organisms Integration of interaction data into bioinformatics workflows Intended use STRING is intended for molecular biologists, bioinformaticians, systems biologists, and life science researchers who want to investigate protein interactions and functional associations. It is particularly suited for users analyzing gene or protein lists, exploring biological pathways, or integrating interaction networks into systems biology and functional genomics studies.

StructureProfiler is a tool for the automated quality assessment of protein–ligand complex structures. It evaluates sets of three-dimensional structures using multiple quality indicators covering the overall structural model, the binding site, and the ligand. These indicators include model-level parameters such as the R factor, active-site properties such as bond-length deviations, and ligand-specific criteria such as electron density support and torsion-angle validity. Key benefits Automated quality profiling of protein–ligand complex structures Combines model-, binding-site-, and ligand-level quality indicators Supports consistent comparison of multiple structures Helps identify structures that may be unsuitable for downstream computational analyses Reduces the effort required for manual structure inspection Supports transparent and reproducible structure selection Applications Selection of protein–ligand complexes for docking and modelling studies Quality control before structure-based drug design Assessment of experimental structure quality Identification of problematic ligands or binding-site geometries Evaluation of benchmark datasets for method development Comparison and prioritization of alternative structural models Intended use StructureProfiler is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery who need to assess the quality and suitability of protein–ligand complex structures before using them in computational experiments, benchmarking, or method evaluation.

TelomereHunter – Sequence-Based Telomere Analysis TelomereHunter is a tool for sequence-based telomere analysis that measures telomere content and telomere repeat composition from sequencing data. It uses BAM or CRAM files as input and supports a wide range of sequencing protocols. Originally developed for human cancer genome analysis, TelomereHunter has been expanded for comparative analysis of vertebrate genomes and for research questions in telomere biology, immunology, aging, and population genomics. Key benefits Provides comprehensive analysis of telomere content and telomere variant repeats Supports input data in BAM and CRAM format Can be installed as a Docker container or Python package Tested across cancer genomes and multiple application scenarios Supports ongoing development and user support Applications Detection of alternative lengthening of telomeres Cohort-based comparison of telomere content and composition Analysis of telomere maintenance mechanisms Use in precision oncology and cancer genome analysis Telomere-focused studies in aging, immunology, vertebrate genomics, and population genomics Intended use TelomereHunter is intended for cancer genomics researchers, telomere biologists, bioinformaticians, precision oncology researchers, ageing researchers, immunologists, and population genomics researchers working with sequencing data. It is particularly suited for users who need to quantify telomere content, analyse telomere variant repeats, compare telomere patterns across cohorts, or investigate telomere maintenance mechanisms in human or other vertebrate genomes.

Tracy is a command-line tool for the analysis of Sanger chromatogram trace files. It supports basecalling, alignment, assembly, and deconvolution of Sanger sequencing data, enabling robust processing of common capillary sequencing outputs. As part of the GEAR toolbox, Tracy can be used through genome analysis workflows and integrated into automated or reproducible pipelines. Key benefits Processes Sanger chromatogram trace files Supports basecalling and sequence alignment Enables assembly of Sanger sequencing reads Provides deconvolution functionality for mixed trace signals Suitable for integration into command-line and pipeline-based workflows Applications Analysis of Sanger sequencing traces Basecalling from chromatogram files Alignment of Sanger reads to reference sequences Assembly of sequencing reads from capillary sequencing experiments Deconvolution of mixed or overlapping chromatogram signals Intended use Tracy is intended for molecular biologists, geneticists, sequencing facilities, and bioinformaticians working with Sanger sequencing data. It is particularly suited for users who need reproducible command-line processing of chromatogram trace files, either as a standalone tool or as part of broader genome analysis workflows.

Trimmomatic is a flexible and efficient tool for quality trimming and preprocessing of sequencing reads. It removes adapters, trims low-quality bases, and filters reads before downstream analysis. Supporting both single-end and paired-end data, Trimmomatic is widely used as an early quality-control step in reproducible sequencing workflows. Key benefits Fast and configurable preprocessing of sequencing reads Adapter clipping using built-in or user-provided adapter files Quality-based trimming to improve downstream analyses Paired-end aware processing that preserves read pairing Easy integration into automated workflows and HPC environments Applications Preprocessing of reads for genomics, metagenomics, RNA-seq, and amplicon studies Removal of adapters, low-quality bases, and short reads Improving mapping, assembly, and downstream analysis performance Standardized read cleaning in large-scale sequencing workflows Integration into reproducible bioinformatics pipelines Intended use Trimmomatic is intended for bioinformaticians, genomics researchers, sequencing facilities, and life science researchers who need a robust and configurable tool for sequencing read preprocessing. It is particularly suited for users building reproducible sequencing workflows that require reliable adapter removal and quality trimming.

tRNA Database – Collection of Genomic and Mitochondrial tRNAs The tRNA Database provides a comprehensive collection of genomic and mitochondrial transfer RNAs (tRNAs), including their sequences, secondary structures, and genomic locations. It enables researchers to explore and compare tRNA genes across a wide range of organisms, supporting studies in molecular biology, evolution, and functional genomics. Key benefits Centralized repository of genomic and mitochondrial tRNA data Includes sequences, structural information, and genomic coordinates Supports comparative and evolutionary analyses across species Web-accessible resource for quick data retrieval Facilitates research in translation and RNA biology Applications Comparative analysis of tRNA genes across organisms Investigation of tRNA structure and function Studies on mitochondrial and nuclear-encoded tRNAs Annotation and validation of tRNA genes in genomic data Evolutionary analysis of RNA sequences and structures Intended use The tRNA Database is intended for molecular biologists, bioinformaticians, and evolutionary researchers working with RNA sequences and genome annotations. It is particularly suited for users who require structured access to curated tRNA data for analysis and comparison.

TYGS – Type (Strain) Genome Server for Genome-Based Prokaryote Taxonomy TYGS (Type Strain Genome Server) is a web-based platform for genome-based taxonomy of Bacteria and Archaea. Building on the Genome-to-Genome Distance Calculator (GGDC), TYGS provides automated, high-throughput taxonomic analyses using whole-genome comparisons to identify and classify prokaryotic organisms. The platform integrates genome distance calculations, phylogenetic analyses, and comparisons against type strain genomes to support reliable species identification and taxonomic placement. TYGS is complemented by VICTOR for virus phylogeny and classification and is closely linked to the LPSN nomenclature resource. Key benefits Automated genome-based taxonomy for bacteria and archaea High-throughput comparison against type strain genomes Accurate species identification using genome-to-genome distance calculations Phylogenomic tree reconstruction and taxonomic placement User-friendly web interface requiring no local software installation Integration with established resources including GGDC, VICTOR, and LPSN Based on state-of-the-art genome comparison methods replacing conventional DNA–DNA hybridization Applications Identification and classification of bacterial and archaeal isolates Genome-based species delineation Phylogenomic analysis of prokaryotic genomes Taxonomic characterization of newly sequenced isolates Comparative microbial genomics Support for microbial systematics and taxonomy Intended use TYGS is intended for microbiologists, microbial taxonomists, bioinformaticians, and life science researchers working with bacterial and archaeal genome sequences. It is particularly suited for users seeking reliable genome-based species identification, phylogenetic analysis, and taxonomic classification using reference type strain genomes.