Registry Catalogue

GenomeCRISPR is a database for high-throughput screening experiments performed with the CRISPR/Cas9 system. It provides a dynamic web interface that helps users explore published CRISPR screens and retrieve information on observed hits, phenotypes, and experimental conditions. The database also contains information on the performance of individual single guide RNAs (sgRNAs), supporting the interpretation and comparison of CRISPR screening results. Key benefits Collects data from published high-throughput CRISPR/Cas9 screens Provides information on observed hits and associated phenotypes Includes sgRNA-level performance information across experimental conditions Offers a dynamic web interface for searching and exploring screen data Supports interpretation and reuse of CRISPR screening results Applications Exploration of published CRISPR/Cas9 screening experiments Identification of genes associated with screened phenotypes Assessment of sgRNA performance across different conditions Comparison of hits from high-throughput genome editing screens Support for planning and interpretation of CRISPR screening studies Intended use GenomeCRISPR is intended for molecular biologists, genome engineers, functional genomics researchers, bioinformaticians, and CRISPR screening researchers. It is particularly suited for users who want to explore published CRISPR/Cas9 screens, investigate phenotype-associated hits, or evaluate sgRNA performance for experimental design and data interpretation.

GenomeRNAi is a web-accessible database that supports exploration of RNA interference phenotype data for human and Drosophila. It provides manually curated data from published RNAi screens as well as datasets submitted directly by data producers. In addition to phenotype information, GenomeRNAi includes RNAi reagent data and assessments of reagent efficiency and specificity, supporting both gene-focused queries and large-scale studies of loss-of-function phenotypes. Key benefits Provides curated RNAi phenotype data from published and submitted screens Covers RNAi screens in human and Drosophila Includes information on RNAi reagents, efficiency, and specificity Supports both single-gene lookup and large-scale phenotype exploration Helps connect loss-of-function phenotypes with gene function Applications Exploration of RNAi screening results Analysis of loss-of-function phenotypes across biological assays Investigation of gene function in human and Drosophila datasets Assessment of RNAi reagent performance and specificity Selection and interpretation of RNAi screening data for follow-up studies Intended use GenomeRNAi is intended for functional genomics researchers, molecular biologists, geneticists, bioinformaticians, and RNAi screening researchers who work with loss-of-function data. It is particularly suited for users who want to retrieve RNAi phenotype information for selected genes, compare screening results across assays, or use curated RNAi data to support gene function annotation.

GeoMine is a tool for the automated mining of protein–ligand binding sites. It enables users to define custom queries and search large collections of protein–ligand complexes and binding pockets for specific spatial interaction patterns. GeoMine supports ligand-based radius pockets as well as predicted pockets generated with DoGSite3 and provides matched binding-site superpositions together with geometric statistics. Key benefits Automated search for spatial patterns in protein–ligand binding sites Custom query design for specific interaction geometries Searches large, regularly updated collections of protein structures and pockets Supports ligand-based and DoGSite3-predicted binding pockets Available through a graphical interface and REST service Provides structural superpositions and statistics on distances, angles, and matching points Applications Identification of recurring interaction patterns in protein binding sites Comparison of geometrically similar binding pockets Analysis of conserved ligand-binding arrangements Discovery of structurally related binding sites across different proteins Support for structure-based drug discovery and ligand design Generation of reference sets for binding-site classification and validation Intended use GeoMine is intended for structural biologists, medicinal chemists, computational chemists, and bioinformaticians working with protein–ligand complexes and binding-site geometries. It is particularly suited for users who want to search large structural datasets for custom spatial interaction patterns and compare matched binding sites in detail.

Goslin is a software tool that facilitates the parsing and normalization of lipid nomenclatures. It provides a standardized way to represent lipids in a computationally accessible format. Key Features: Lipid ontology : Goslin uses a comprehensive lipid ontology to cover various lipid classes and subclasses. Nomenclature parsing : The tool can parse different lipid nomenclatures, including shorthand notations and systematic names. Normalization : Goslin normalizes lipid structures to a standardized format for efficient comparison and analysis. Benefits: Standardization : Goslin enables researchers to easily compare and integrate lipidomics data across different studies and platforms. Improved data quality : The tool's normalization capabilities help ensure accurate and consistent lipid structures. Availability: Goslin is available as a software library that can be downloaded from the LIFS Tools website, or used through its web-based application.

Helixer is an ab initio gene prediction tool for identifying protein-coding genes directly from raw eukaryotic DNA sequences. It combines deep learning-based base-wise predictions with a Hidden Markov Model to generate structural gene models without requiring additional experimental evidence such as RNA-seq data or protein alignments. Helixer is available as an online service and as open-source software for local installation. Key benefits Ab initio prediction of protein-coding genes from genomic DNA Combines deep learning with Hidden Markov Model-based postprocessing Does not require RNA-seq data or protein homology evidence Suitable for structural annotation of eukaryotic genomes Available via web interface and GitHub Applications Structural gene annotation of newly sequenced eukaryotic genomes Prediction of primary protein-coding gene models Genome annotation workflows without additional experimental evidence Comparative genomics and functional genomics projects Preparation of gene models for downstream biological interpretation Intended use Helixer is intended for genome annotation researchers, bioinformaticians, eukaryotic genomics researchers, and plant scientists who need to predict protein-coding genes from genomic DNA sequences. It is particularly suited for projects where RNA-seq or protein evidence is unavailable, incomplete, or not intended to be used.

HyPPI is a web-based tool for classifying protein–protein complexes as permanent, transient, or crystal artifacts. Permanent complexes are stable only in their associated state, whereas transient complexes can remain stable both as complexes and as individual subunits. Crystal artifacts are non-biological contacts formed during crystallization. HyPPI performs the classification using two structural properties of the protein–protein interface: its hydrophobicity (ΔG hydrophobic) and the interface quotient (IF-quotient), which reflects the symmetry of the interacting surfaces. Key benefits Automated classification of protein–protein complexes Distinguishes permanent and transient interactions from crystal artifacts Uses interpretable structural interface descriptors Supports assessment of the biological relevance of crystallographic contacts Web-based analysis without local software installation Applications Classification of protein–protein interactions in crystal structures Identification of potentially biologically relevant protein complexes Detection of non-functional crystallization contacts Analysis of interface hydrophobicity and symmetry Support for structural interpretation and protein interaction studies Intended use HyPPI is intended for structural biologists, protein scientists, bioinformaticians, and computational biologists working with three-dimensional protein complex structures. It is particularly useful for researchers who need to assess whether an observed protein–protein interface represents a permanent or transient biological interaction or is likely to be a crystal artifact.

IHW – Independent Hypothesis Weighting for Multiple Testing IHW (Independent Hypothesis Weighting) is an R/Bioconductor package for multiple testing with false discovery rate control. It increases statistical power compared to commonly used FDR procedures by assigning data-driven weights to individual hypotheses. The package provides a flexible implementation that can be applied to different data types and analysis settings where large numbers of hypotheses are tested. Key benefits Supports false discovery rate control in multiple testing Increases detection power through data-driven hypothesis weighting Flexible implementation applicable to different data types Integrates into R and Bioconductor-based analysis workflows Useful for high-throughput biological data analysis Applications Multiple testing correction in high-throughput experiments Differential expression and differential abundance analyses Genome-wide, transcriptome-wide, or proteome-wide statistical testing Increasing power in large-scale hypothesis testing workflows Integration into reproducible statistical analysis pipelines Intended use IHW is intended for bioinformaticians, biostatisticians, statistical genomics researchers, and life science researchers performing large-scale hypothesis testing. It is particularly suited for analyses where false discovery rate control is required and additional covariate information can be used to improve statistical power.

Indel Calling is a workflow for detecting insertions and deletions in human sequencing data. It is based on Platypus and includes additional quality control steps for robust variant detection and assessment. The workflow is designed for use with the Roddy workflow management system and is tailored to human reference genomes, including hg37/hg19 and, in some later versions, hg38, because genomic annotations play an important role in the analysis. Key benefits Detects insertions and deletions in human sequencing data Based on the established Platypus variant calling approach Includes extensive workflow-specific quality control additions Designed for integration with the Roddy workflow management system Supports human reference genome builds such as hg37/hg19 and selected hg38 versions Applications Insertion and deletion calling from human sequencing datasets Variant detection in whole-genome or targeted sequencing workflows Quality-controlled analysis of indel candidates Integration of indel calling into Roddy-based NGS processing pipelines Human genome analysis using annotation-aware variant workflows Intended use Indel Calling is intended for bioinformaticians, cancer genomics researchers, sequencing facilities, and genomics research groups working with human sequencing data. It is particularly suited for users who need a Roddy-compatible workflow for Platypus-based indel detection with integrated quality control and annotation-aware processing.

iPath (Interactive Pathway Explorer) is a web-based tool for the visualization, customization, and analysis of cellular pathways. It provides comprehensive pathway maps for primary metabolism, secondary metabolite biosynthesis, and regulatory processes, and allows users to map their own data onto these networks using identifiers such as KEGG Orthology groups, COGs, or EC numbers. Map elements can be customized by adjusting color, width, and opacity, and the resulting visualizations can be exported in both bitmap and vector formats. Key benefits Interactive visualization of metabolic and regulatory pathways Extensive customization of pathway elements Supports multiple identifier types, including KEGG KOs, COGs, and EC numbers Enables direct mapping of user-generated data onto pathway maps Suitable for comparative analysis of genomic and metagenomic datasets Export of pathway figures in bitmap and vector formats Web-based access without local installation Applications Visualization of metabolic pathways and functional networks Mapping of gene, protein, or enzyme abundance data Comparison of functional profiles across samples or conditions Exploration of metabolic capabilities in genomic and metagenomic datasets Analysis of primary metabolism, secondary metabolism, and regulatory pathways Preparation of publication-ready pathway figures Intended use iPath is intended for systems biologists, microbiologists, bioinformaticians, metabolomics researchers, and researchers working with genomic or metagenomic data. It is particularly suited for users who want to explore and communicate functional or metabolic patterns by mapping their data onto curated pathway networks.

iTOL (Interactive Tree of Life) is a web-based tool for displaying, annotating, and exploring phylogenetic trees. It supports a wide range of tree layouts, including rectangular, circular, and unrooted representations, and can visualize very large trees with 50,000 or more leaves. Interactive navigation, advanced search, and extensive annotation options make iTOL suitable for both exploratory analysis and the preparation of publication-quality tree figures. Key benefits Interactive visualization and manipulation of phylogenetic trees Supports rectangular, circular, and unrooted tree layouts Scales to very large trees with tens of thousands of leaves Extensive annotation and styling options Advanced search and navigation within complex trees Export in bitmap and vector graphic formats Web-based access without local installation Applications Visualization of phylogenetic and taxonomic trees Annotation of trees with metadata, symbols, colors, and labels Exploration of large-scale evolutionary datasets Comparison of clades, taxa, and tree topologies Preparation of publication-ready phylogenetic figures Presentation of cladograms and phylograms in different layouts Intended use iTOL is intended for evolutionary biologists, microbiologists, taxonomists, bioinformaticians, and other life science researchers working with phylogenetic trees. It is particularly suited for users who need an interactive and scalable environment for exploring, annotating, and presenting large or complex tree datasets.

JAMDA is a tool for preparing protein structures and docking small molecules into selected, preprocessed binding sites. It simplifies protein–ligand docking through automated workflows for protein preparation, binding-site setup, ligand conformer generation, docking, and scoring. Users can apply default settings or customize the preparation process to account for protein ensembles, relevant water molecules, and cofactors. Key benefits Automated preparation of proteins and binding sites Integrated docking and scoring of small-molecule ligands Flexible configuration for protein ensembles, water molecules, and cofactors Automated ligand conformer generation using Conformator Supports externally generated ligand conformations Designed for reproducible and streamlined docking workflows Applications Prediction of protein–ligand binding poses Virtual screening of small-molecule compounds Evaluation of alternative ligand conformations Docking into single structures or protein ensembles Investigation of the influence of binding-site waters and cofactors Support for structure-based drug discovery and ligand optimization Intended use JAMDA is intended for structural biologists, medicinal chemists, computational chemists, and researchers in structure-based drug discovery who need an automated and configurable workflow for protein–ligand docking. It is particularly suited for users who want to combine standardized preprocessing with flexible control over protein structures, binding-site components, and ligand conformations.

JSpeciesWS is a cloud-based bioinformatics service for pairwise comparison of microbial genomes at the whole-DNA level. It supports microbial systematics by calculating genome identity measures that are widely used for species circumscription and taxonomic assessment. The service provides established computational approaches that serve as faster in silico alternatives to traditional DNA-DNA hybridization methods formerly used for bacterial species delineation. Key benefits * Cloud-based genome comparison service without registration * Supports microbial species delineation and taxonomic assessment * Enables pairwise comparison of uploaded genomes and public references * Provides access to an internal database of more than 70,000 quality-controlled bacterial genomes * Allows previous analyses to be re-accessed and continued using anonymous session codes Applications * Comparison of microbial genomes at whole-genome level * Bacterial species delineation and taxonomic placement * Identification of closely related reference organisms * Analysis of newly sequenced or taxonomically ambiguous genomes * Generation of pairwise genome comparison matrices for multiple organisms Intended use JSpeciesWS is intended for microbiologists, microbial taxonomists, genome researchers, bioinformaticians, and researchers working with bacterial genome data. It is particularly suited for users who need accessible genome identity calculations to support microbial species circumscription, taxonomic decisions, or placement of unknown genomes among sequenced bacterial reference species. Service provision This service is offered by the de.NBI Industrial Forum member Ribocon GmbH and is fully free for users from academia and industry, without registration. Development and maintenance are not funded by de.NBI.