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At least 19 recordsLinked to original sources

PubMed2025

DescribePROT Database of Residue-Level Protein Structure and Function Annotations.

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8 billion predictions for 1.4 million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Databases, Protein↗

PubMed2025

SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences.

SUMMARY: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use interface. AVAILABILITY AND IMPLEMENTATION: SBMLtoOdin is published under the open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under the MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.

Software↗

PubMed2025

Easy and interactive taxonomic profiling with Metabuli App.

SUMMARY: Accurate metagenomic taxonomic profiling is critical for understanding microbial communities. However, computational analysis often requires command-line proficiency and high-performance computing resources. To lower these barriers, we developed Metabuli App, an all-in-one desktop application that efficiently runs taxonomic profiling locally on a consumer-grade computer. It features user-friendly graphical interfaces for custom database curation, raw read quality control (QC), taxonomic profiling, and interactive result visualization. AVAILABILITY AND IMPLEMENTATION: GPLv3-licensed source code and prebuilt apps for Windows, macOS, and Linux are available at https://github.com/steineggerlab/Metabuli-App and are archived at https://doi.org/10.5281/zenodo.15876171. Analysis scripts are available at https://github.com/jaebeom-kim/metabuli-app-analysis. The Sankey-based taxonomy visualization component is available at https://github.com/steineggerlab/taxoview for easy integration into other web projects.

Software↗

PubMed2026

Plotgardener App: a graphical interface for publication-ready genomic visualization.

SUMMARY: Plotgardener is an R package used for generating high-quality genomic visualizations. Despite its broad range of functions and versatility, its reliance on code presents a barrier for many potential users. To address this, we developed a macOS desktop application version of Plotgardener that enables users to create publication-ready genomic plots with no programming experience. The application employs a modular architecture comprising an Electron.js backend, a React frontend, and a Python parser that dynamically analyzes the Plotgardener package to ensure interface fields remain synchronized with package updates. By lowering the technical barrier to advanced genomic visualization, the Plotgardener desktop application broadens access to powerful visualization workflows for researchers and clinicians. AVAILABILITY: The current release of the Plotgardener App is an open source macOS desktop application built with Electron (Node.js), featuring a React frontend and a Python-based parser. The download link is available at https://phanstiellab.github.io/plotgardener/articles/guides/plotgardenerApp.html and on Zenodo (doi: https://doi.org/10.5281/zenodo.21684228). The source code is hosted on GitHub at https://github.com/rishabhsvemuri/ThePlotgardenerApp.

Genomics↗

PubMed2026

Practicing Data Science in Interactive Notebooks.

The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit and transparent structure greatly reproducibility while its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.

Software↗

PubMed2026

From complexity to clarity: Building dashboards for hit selection in high throughput screens.

High throughput screening produces large, complex datasets that are difficult to interrogate without programming expertise, making hit selection time-consuming and inflexible. While instrument software and commercial tools offer partial solutions, they often lack adaptability or require costly infrastructure. Interactive dashboards provide an effective alternative by enabling dynamic filtering and integrated visualization within a single interface. Here, we present simple R Markdown-based templates for creating customizable, modular dashboards for screen data analysis. Built using the flexdashboard and crosstalk R packages, and HTML widgets, these lightweight, easy-to-build HTML dashboards require no complex installation process or installation of licensed software. They support linked visualizations, threshold-based filtering (e.g., Z-score, p-value, fold change), and interactive data exploration and are shared as a standalone HTML file. This framework enables rapid, flexible hit selection across diverse high throughput screening applications and is designed for users with basic R experience.

High-Throughput Screening Assays↗

PubMed2025

OLS4: a new Ontology Lookup Service for a growing interdisciplinary knowledge ecosystem.

SUMMARY: The Ontology Lookup Service (OLS) is an open source search engine for ontologies which is used extensively in the bioinformatics and chemistry communities to annotate biological and biomedical data with ontology terms. Recently, there has been a significant increase in the size and complexity of ontologies due to new scales of biological knowledge, such as spatial transcriptomics, new ontology development methodologies, and curation on an increased scale. Existing Web-based tools for ontology browsing such as BioPortal and OntoBee do not support the full range of definitions used by today's ontologies. In order to support the community going forward, we have developed OLS4, implementing the complete OWL2 specification, internationalization support for multiple languages, and a new user interface with UX enhancements such as links out to external databases. OLS4 has replaced OLS3 in production at EMBL-EBI and has a backward compatible API supporting users of OLS3 to transition. AVAILABILITY AND IMPLEMENTATION: The source code of OLS is available at https://github.com/EBISPOT/ols4 and DOI 10.5281/zenodo.14960290 with Apache 2.0 License. A freely available implementation is accessible at https://www.ebi.ac.uk/ols4.

Biological Ontologies↗

PubMed2025

monarchr: an R package for querying biomedical knowledge graphs.

SUMMARY: Biomedical knowledge graphs (KGs) aggregate and provide a wealth of information, linking genes and their variants, diseases, phenotypes, and much more. While these data are available in raw and API-hosted form, to date, functionality for working with KGs in the R programming language has been limited. We introduce monarchr, a package for querying and manipulating KG data. Support for the expansive Monarch Initiative KG is built in, and monarchr can accommodate any KG in the Knowledge Graph eXchange (KGX) format. This tidy-inspired interface offers researchers an intuitive, iterative approach to querying and visualizing KG data. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and installation instructions are available at https://github.com/monarch-initiative/monarchr.

Software↗

PubMed2026

OmicsQ: a user-friendly platform for interactive quantitative omics data analysis.

MOTIVATION: High-throughput omics technologies generate complex datasets with thousands of features that are quantified across multiple experimental conditions, but often suffer from incomplete measurements, missing values, and individually fluctuating variances. This requires analytical tools for accurate, deep and insightful biological interpretation, capable of dealing with a large variety of data properties and different amounts of completeness. Software capable of handling such data complexity and integrating with external applications for downstream analysis remains rare and mostly relies on programming-based environments, limiting accessibility for researchers without computational expertise. RESULTS: We present OmicsQ, an interactive, web-based platform designed to streamline quantitative omics data analysis. OmicsQ provides an intuitive, browser-based visualization interface that integrates established statistical processing tools. Those include robust batch correction, automated experimental design annotation, and handling of missing data without imputation, which maintains data integrity and avoids artifacts from a priori assumptions. OmicsQ seamlessly interacts with external applications (e.g. PolySTest, VSClust, ComplexBrowser) for statistical testing, clustering, analysis of protein complex behavior, and pathway enrichment, offering a comprehensive and flexible workflow from data import to biological interpretation that is broadly applicable across domains. AVAILABILITY AND IMPLEMENTATION: OmicsQ is implemented in R and Shiny and is available at https://computproteomics.bmb.sdu.dk/app_direct/OmicsQ. Source code and installation instructions: https://github.com/computproteomics/OmicsQ, DOI: 10.5281/zenodo.17778420.

Software↗

PubMed2026

ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data.

MOTIVATION: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualization and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data. RESULTS: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualization and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualize phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customizable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. AVAILABILITY AND IMPLEMENTATION: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

Software↗

PubMed2026

igv-reports: embedding interactive genomic visualizations in HTML reports to aid variant review.

SUMMARY: We present igv-reports, a command-line tool to create standalone HTML pages embedding interactive genomic visualizations of read alignments and associated annotations to support variant inspection workflows. The reports contain all data and code required for visualization of the variant sites, with no dependencies on the input data files. AVAILABILITY AND IMPLEMENTATION: igv-reports is a command-line application written in Python. It is freely available at https://github.com/igvteam/igv-reports under an MIT license.

Software↗

PubMed2026

VIJB: a companion of the JBROWSE genome browser for the visually impaired people.

MOTIVATION: The availability of touch-sensitive and haptic devices has been a keystone development for the inclusion of visually impaired people (VIPs) in modern, highly digitized work environments. Braille displays have proven efficient and versatile enough to parse large and complex text files, making bioinformatics and text-heavy programming accessible to VIPs. However, the complex graphical objects -combining numerous datasets- typically generated during data integration remain challenging, even with the aid of descriptive AI. This is particularly true in functional genomics. Here, we present VIJB, a simple application that displays the multilayered output of the JBROWSE genome browser on a Braille reader, enabling VIPs to fully participate in data integration in functional genomics. AVAILABILITY AND IMPLEMENTATION: VIJB is programmed in Python and relies on the scientific library NumPy, the braillegraph and pyBigWig libraries, and the TABIX software. The architecture is summarized in Supplementary Material 1, available as supplementary data at Bioinformatics online. VIJB is available for download at the GitHub repository https://GitHub.com/NiBuMNHN/VIJB and is licenced under the GPL 3.0.

Persons with Visual Disabilities↗

PubMed2026

Hotgenes: an R package for reducing bottlenecks in bulk omics data exploration and collaboration.

SUMMARY: A critical part of omics analysis is the transition from early data exploration to final interpretation, often including different analytical platforms and the proliferation of figures, tables, and files. To minimize potential errors and delays that can occur during this process, we have developed an R package called "Hotgenes" that contains a wide range of flexible utilities available in a single modular Shiny application. With Hotgenes, differential expression results generated from bulk omics platforms can be imported and shared among collaborators with minimal coding. Furthermore, the modular Hotgenes user interface can be customized by advanced users to fit the needs of their evolving pipelines. AVAILABILITY AND IMPLEMENTATION: Hotgenes is implemented in R and is freely available at https://github.com/pfizer-opensource/Open-Hotgenes. A permanent archived version is available at https://doi.org/10.5281/zenodo.20129460.

Software↗

PubMed2026

BRIDGE: an interactive application for multi-omics data analysis, visualization and integration.

SUMMARY: BRIDGE is a Shiny-based application that provides an accessible, modular platform for individual and integrative multi-omics analysis. Using an independent SQLite database backend, it offers a local, private, and user-friendly environment that requires no prior computational expertise. The application supports proteomics, phospho-proteomics, and RNA-seq analyses through a comprehensive suite of visualization and analytical modules, together with an integrated multi-omics analysis pipeline. Built-in caching and asynchronous processing improve responsiveness, enabling efficient exploration, analysis, and visualization of multi-omics datasets on moderate hardware. AVAILABILITY AND IMPLEMENTATION: BRIDGE is implemented in R using Shiny and is freely available as a Docker container at https://ghcr.io/paulilab/bridge. A public demonstration server with example datasets is available at https://bridge.imp.ac.at. Code and datasets are also available at https://github.com/paulilab/BRIDGE and under DOI: https://doi.org/10.5281/zenodo.20215824.

Multiomics↗

PubMed2004

EnsMart: a generic system for fast and flexible access to biological data.

The EnsMart system (www.ensembl.org/EnsMart) provides a generic data warehousing solution for fast and flexible querying of large biological data sets and integration with third-party data and tools. The system consists of a query-optimized database and interactive, user-friendly interfaces. EnsMart has been applied to Ensembl, where it extends its genomic browser capabilities, facilitating rapid retrieval of customized data sets. A wide variety of complex queries, on various types of annotations, for numerous species are supported. These can be applied to many research problems, ranging from SNP selection for candidate gene screening, through cross-species evolutionary comparisons, to microarray annotation. Users can group and refine biological data according to many criteria, including cross-species analyses, disease links, sequence variations, and expression patterns. Both tabulated list data and biological sequence output can be generated dynamically, in HTML, text, Microsoft Excel, and compressed formats. A wide range of sequence types, such as cDNA, peptides, coding regions, UTRs, and exons, with additional upstream and downstream regions, can be retrieved. The EnsMart database can be accessed via a public Web site, or through a Java application suite. Both implementations and the database are freely available for local installation, and can be extended or adapted to 'non-Ensembl' data sets.

Animals↗

PubMed2019

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): a cloud-based platform for curating and classifying germline variants.

Variant interpretation in the era of massively parallel sequencing is challenging. Although many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here, we present the Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE), a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in variant call format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4000 pediatric cancer patients and serves as a repository for the expert-reviewed results. PeCanPIE was originally developed for pediatric cancer but can be easily extended for use for nonpediatric cancers and noncancer genetic diseases. Although PeCanPIE's web-based interface was designed to be accessible to non-bioinformaticians, its back-end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

Child↗

PubMed2002

Apollo: a sequence annotation editor.

The well-established inaccuracy of purely computational methods for annotating genome sequences necessitates an interactive tool to allow biological experts to refine these approximations by viewing and independently evaluating the data supporting each annotation. Apollo was developed to meet this need, enabling curators to inspect genome annotations closely and edit them. FlyBase biologists successfully used Apollo to annotate the Drosophila melanogaster genome and it is increasingly being used as a starting point for the development of customized annotation editing tools for other genome projects.

Animals↗

PubMed2005

The Open Microscopy Environment (OME) Data Model and XML file: open tools for informatics and quantitative analysis in biological imaging.

The Open Microscopy Environment (OME) defines a data model and a software implementation to serve as an informatics framework for imaging in biological microscopy experiments, including representation of acquisition parameters, annotations and image analysis results. OME is designed to support high-content cell-based screening as well as traditional image analysis applications. The OME Data Model, expressed in Extensible Markup Language (XML) and realized in a traditional database, is both extensible and self-describing, allowing it to meet emerging imaging and analysis needs.

Computational Biology↗