Key Features
Phenotype Visualisation
Visualise gene and condition phenotypes with interactive plots and tables, filtered by FDR thresholds.
Correlation Visualisation
Visualise gene and condition correlations with interactive plots and tables, filtered by FDR thresholds.
Enrichment Analysis
Perform GO and KEGG enrichment analysis to identify functional pathways and gene sets, with interactive plots and downloadable results.
Interactive Heatmaps
Visualise gene-condition fitness data with interactive heatmaps and dendrograms, offering clustering options and downloadable visualisations.
Upload Your Dataset
Upload your scored dataset to run FDR analysis and visualise phenotypes, correlations, and heatmaps.
Omics Integration
Integrate chemical genomic data with omics to compare hits, quantify overlap, and visualise shared patterns.
Objectives
Chemical genomic screens are a valuable resource for uncovering gene functions and mapping biological pathways. However, their large-scale nature makes these datasets complex and challenging to interpret. ChemGenXplore addresses this challenge by simplifying access to these resources and providing researchers with an intuitive platform to explore and understand genotype-phenotype relationships across various species.
Data Sources
ChemGenXplore includes pre-integrated, publicly available chemical genomic datasets from Escherichia coli and Saccharomyces cerevisiae . These datasets provide fitness scores across a wide range of conditions and serve as the foundation for the analyses performed in ChemGenXplore:
- Nichols et al., 2011: https://doi.org/10.1016/j.cell.2010.11.052
- Shiver et al., 2017: https://doi.org/10.1371/journal.pgen.1006124
- Price et al., 2018: https://doi.org/10.1038/s41586-018-0124-0
- Viéitez et al., 2022: https://doi.org/10.1038/s41587-021-01051-x
Users also have the flexibility to upload their own datasets for analysis.
Acknowledgements
ChemGenXplore was developed with support from the Banzhaf Lab and the Moradigaravand Lab.
H.A. was funded by the Darwin Trust of Edinburgh.
Affiliated institutions include:
- Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
- Laboratory of Infectious Disease Epidemiology, KAUST Center of Excellence for Smart Health and Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Contact
For questions or suggestions for additional features, please contact:
- Huda Ahmad: hxa105@student.bham.ac.uk
- Manuel Banzhaf: manuel.banzhaf@newcastle.ac.uk
- Danesh Moradigaravand: danesh.moradigaravand@kaust.edu.sa
User Guide
ChemGenXplore interface includes the following sections:
- Home: Overview of ChemGenXplore and its key features.
- About: Objectives, data sources, contributors, and contact information
- User Guide: Detailed instructions on using each component of the app.
- E. coli: Analyse preloaded Escherichia coli datasets.
- Upload Your Dataset: Upload a custom dataset for analysis.
This section provides an overview of the analyses available for the pre-loaded datasets. All analysis types apply to both E. coli and S. cerevisiae and allow users to adjust False Discovery Rate (FDR) thresholds to refine results. Each analysis provides interactive plots and tables with the option to download outputs for further use.
This section focuses on visualising the number of phenotypes associated with selected genes or conditions across preloaded datasets. Users can adjust False Discovery Rate (FDR) thresholds to identify statistically significant phenotypes. Results are displayed as interactive bar plots and tables, with the option to download both for further analysis. An additional table is available, listing all phenotypes without applying the FDR threshold for a comprehensive overview.
Gene-Level
- Go to E. coli or S. cerevisiae ▸ Phenotypes ▸ Gene-Level.
- Select one or more genes from the dropdown.
- Adjust the FDR slider to filter significant phenotypes.
- Review interactive bar plots and data tables.
- Download plots and tables using the buttons provided.
Condition-Level
- Go to E. coli or S. cerevisiae ▸ Phenotypes ▸ Condition-Level.
- Select one or more conditions from the dropdown.
- Adjust the FDR slider to filter significant phenotypes.
- Review interactive bar plots and data tables.
- Download plots and tables using the buttons provided.
This section focuses on visualising correlations between selected genes or conditions across pre-loaded E. coli and S. cerevisiae datasets. Users can adjust False Discovery Rate (FDR) thresholds to identify statistically significant correlations. Results are displayed as interactive bar plots and tables, with the option to download both for further analysis. An additional table is available, listing all correlations without applying the FDR threshold for a comprehensive overview.
Gene-Level
- Go to E. coli or S. cerevisiae ▸ Correlation Analysis ▸ Gene-Level.
- Select one or more genes from the dropdown.
- Adjust the FDR slider to filter significant correlations.
- Review interactive bar plots and data tables.
- Download plots and tables using the buttons provided.
Condition-Level
- Go to E. coli or S. cerevisiae ▸ Correlation Analysis ▸ Condition-Level.
- Select one or more conditions from the dropdown.
- Adjust the FDR slider to filter significant correlations.
- Review interactive bar plots and data tables.
- Download plots and tables using the buttons provided.
This section focuses on identifying enriched biological processes and pathways associated with selected genes across pre-loaded E. coli and S. cerevisiae datasets. Users can adjust False Discovery Rate (FDR) thresholds to identify statistically significant terms. Results are displayed as interactive bar plots and tables, with the option to download both for further analysis. An additional table is available, listing all enrichment results without applying the FDR threshold for a comprehensive overview.
GO Enrichment
- Go to E. coli or S. cerevisiae ▸ Enrichment Analysis ▸ GO Enrichment.
- Select one or more genes from the dropdown.
- Adjust the FDR slider to filter significant terms.
- Review interactive bar plots and data tables.
- Download plots and tables using the buttons provided.
KEGG Enrichment
- Go to E. coli or S. cerevisiae ▸ Enrichment Analysis ▸ KEGG Enrichment.
- Repeat the same steps as for GO Enrichment.
- Adjust the FDR slider to filter significant pathways.
- Review interactive bar plots and data tables.
- Download plots and tables using the buttons provided.
This section focuses on visualising fitness profiles across genes and conditions for pre-loaded E. coli and S. cerevisiae datasets using interactive heatmaps. Users can customise clustering options and distance metrics to refine the visualisation. Results are displayed as interactive heatmaps and tables, with the option to download both for further analysis. An additional table is available, listing the selected data used to generate the heatmap for a comprehensive overview.
- Go to E. coli or S. cerevisiae ▸ Heatmaps.
- Select datasets, genes, and conditions to generate heatmaps.
- Optionally cluster rows and/or columns and choose a distance metric.
- Download images and data tables using the buttons provided.
This section allows users to upload custom datasets for analysis. Uploaded datasets must be formatted with genes/strains as rows and conditions as columns. Once uploaded, users can explore phenotypes, correlations, enrichment analysis, and heatmap generation using the same tools and workflows available for the pre-loaded datasets.
- Go to Upload Your Dataset.
- Upload a CSV file where the first column contains unique gene/strain identifiers and all remaining columns contain numeric values.
- Explore Phenotypes, Correlation Analysis, Enrichment Analysis, and Heatmaps using the same steps as described above.
This section focuses on integrating chemical genomic data with an omics dataset (e.g., transcriptomics or proteomics) to identify shared gene-level signals across datasets. Users can align by gene IDs, apply thresholds to define hits, and explore concordance through scatter plots, paired heatmaps, and overlap statistics. Results include interactive visualisations, summary tables, and downloadable outputs for further analysis.
- Go to Omics Integration ▸ Scatter plot.
- Upload one chemical-genomics CSV and one omics CSV (first column must contain Gene IDs).
- Select one column from each dataset for comparison.
- Adjust the |score| thresholds to classify significant hits.
- Review interactive scatter plots with quadrant classification and optional gene labels.
- Download the scatter (PDF) and hit list (CSV) using the buttons provided.
- Go to Omics Integration ▸ Heatmaps.
- Optionally select a subset of genes and columns from each dataset.
- View paired heatmaps aligned by gene with symmetric colour scales centred at zero.
- Download the heatmap data (CSV) using the button provided.
- Go to Omics Integration ▸ Overlap Summary.
- Set thresholds for each dataset to define significant hits.
- Review counts for Chemical Genomics only, Omics only, both significant, and neither.
- Inspect Fisher’s exact test statistics (Odds Ratio, 95% CI, p-value) to assess enrichment.
- Download the overlap summary (CSV) using the button provided.
This section provides guidance for effectively using ChemGenXplore and resolving common issues encountered during data analysis. Following these tips ensures a smoother experience and more reliable results.
- Ensure the uploaded dataset contains numeric values for analysis.
- Check column and row names for invalid characters or formatting issues.
- If no data appears, verify that your selections align with the available data in the app.
Upload Your Dataset
Upload a CSV file where rows contain unique identifiers (e.g., genes, samples, strains) and columns contain experimental measurements (e.g., fitness scores, gene expression levels, growth rates). Example Dataset Below:
Omics Integration
Upload a chemical genomics matrix and an omics matrix, align by gene IDs, and generate figures and tables.
Chemical Genomics Upload
Omics Upload
Alignment
© 2025 ChemGenXplore | Developed by Huda Ahmad