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Lookup NU author(s): Dr Sam BenedictORCiD, Dr James Haycocks, Professor Paula Moynihan, Manuel BanzhafORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2026. Published by Oxford University Press.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).
Author(s): Ahmad H, Doherty HM, Benedict ST, Haycocks JRJ, Zhou G, Moynihan PJ, Moradigaravand D, Banzhaf M
Publication type: Article
Publication status: Published
Journal: Bioinformatics
Year: 2026
Volume: 42
Issue: 2
Print publication date: 01/02/2026
Online publication date: 13/01/2026
Acceptance date: 07/01/2026
Date deposited: 16/02/2026
ISSN (print): 1367-4803
ISSN (electronic): 1367-4811
Publisher: Oxford University Press
URL: https://doi.org/10.1093/bioinformatics/btag021
DOI: 10.1093/bioinformatics/btag021
Data Access Statement: The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.16753661. The datasets were derived from sources in the public domain: Nichols et al. (2011), Price et al. (2018), Shiver et al. (2017), and Vieitez et al. (2022).
PubMed id: 41530107
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