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.
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 three pre-integrated, publicly available Escherichia coli datasets from chemical genomic screens. 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., 2016: https://doi.org/10.1371/journal.pgen.1006124
- Price et al., 2018: https://doi.org/10.1038/s41586-018-0124-0
Users also have the flexibility to upload their own datasets for analysis.
Acknowledgment
ChemGenXplore was developed with support from the Banzhaf Lab and the Moradigaravand Lab.
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, Biological and Environmental Science and Engineering (BESE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Financial support was provided by the Darwin Trust of Edinburgh.
Contact Information
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 preloaded E. coli datasets. All analysis types allow users to adjust False Discovery Rate (FDR) thresholds to refine results and access interactive plots and tables for download.
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
- Navigate to 'E. coli' > 'Phenotypes' > 'Gene-Level'.
- Select one or more genes of interest using the dropdown menu.
- Adjust the FDR threshold slider to filter significant phenotypes.
- View results as interactive bar plots and data tables.
- Download plots and tables using the provided buttons.
Condition-Level
- Navigate to 'E. coli' > 'Phenotypes' > 'Condition-Level'.
- Select one or more conditions of interest using the dropdown menu.
- Adjust the FDR threshold slider to filter significant phenotypes.
- View results as interactive bar plots and data tables.
- Download plots and tables using the provided buttons.
This section focuses on exploring correlations between genes or conditions based on their fitness profiles across preloaded 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
- Navigate to 'E. coli' > 'Correlation Analysis' > 'Gene-Level'.
- Select one or more genes using the dropdown menu.
- Adjust the FDR threshold slider to filter significant correlations.
- View correlation results as bar plots and data tables.
- Download plots and tables using the provided buttons.
Condition-Level
- Navigate to 'E. coli' > 'Correlation Analysis' > 'Condition-Level'.
- Select one or more conditions of interest using the dropdown menu.
- Adjust the FDR threshold slider to filter significant correlations.
- View correlation results as bar plots and data tables.
- Download plots and tables using the provided buttons.
This section focuses on identifying biological processes or pathways associated with a set of genes through enrichment analysis. Both Gene Ontology (GO) and KEGG pathway enrichment analysis are supported. Users can adjust False Discovery Rate (FDR) thresholds to refine results. 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
- Navigate to 'E. coli' > 'Enrichment Analysis' > 'GO Enrichment'.
- Select one or more genes using the dropdown menu.
- Adjust the FDR threshold slider to filter significant results.
- View results as bar plots and data tables.
- Download plots and tables using the provided buttons.
KEGG Enrichment
- Navigate to 'E. coli' > 'Enrichment Analysis' > 'KEGG Enrichment'.
- Repeat the steps outlined for GO Enrichment.
This section focuses on visualising fitness data across genes and conditions through interactive heatmaps. Users can customise clustering options and distance metrics to refine the visualisation. Results are displayed as interactive heatmaps, with the option to download both the heatmap images and the associated data tables for further analysis.
- Navigate to 'E. coli' > 'Heatmaps'.
- Select datasets, genes, and conditions to generate heatmaps.
- Options include clustering rows or columns, choosing distance metrics, and downloading heatmap images and data tables.
This section allows users to upload custom datasets for analysis. Uploaded datasets must be correctly formatted for compatibility, with rows representing genes or mutants and columns representing conditions. Once uploaded, users can explore phenotypes, correlations, enrichment analysis, and heatmap generation using the same tools and workflows available in the E. coli tab.
- Navigate to '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).
- Follow the same steps for phenotypes, correlation analysis, enrichment analysis, and heatmap generation as the E. coli tab.
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:
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