TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor–Target Interactions
Abstract
:1. Introduction
2. Materials and Methods
2.1. Transcription Factor Databases
2.2. Gene Expression Databases
- The GTEx (Genotype-Tissue Expression) project database provides extensive reference data for gene expression and its variability in different normal human tissues, enabling researchers to understand how gene expression is influenced by genetic background on a broader scale [19].
- The TCGA (The Cancer Genome Atlas) database collects multidimensional cancer genomic data, including gene expression, mutations, copy number variations, and epigenetic data, offering strong support for the discovery of cancer biomarkers and therapeutic targets [20].
- The CCLE (Cancer Cell Line Encyclopedia) database provides a wealth of gene expression, mutation, and epigenetic characteristic data for numerous cancer cell lines, serving as a crucial resource for studying cell line-specific responses and drug screening [21].
2.3. Software
2.4. Package
3. Results
3.1. Module 1: Procedures for the Prediction of the Target Genes of TF
- (1)
- Input a TF name: Users initiated the process by selecting a TF of interest from a dropdown list.
- (2)
- Select TF-Target datasets: Then, users could the TF-Target datasets to include in the analysis. Notably, if “KnockTF” was selected, an additional interface element appeared: a slider for setting the log2 fold-change (Log2FC) threshold. This specification is necessary as KnockTF predictions are based on differential gene expression data following TF knockdown or knockout.
- (3)
- Select correlation datasets (optional): Users can further enhance the accuracy of predictions based on the correlation analysis results of TF and target gene expression in different tissues based on the TCGA and GTEx databases. Also, the threshold for the correlation coefficient is set to 0.3.
- (4)
- Results: By clicking the “Go” button, users commenced the predictive analysis. After a short wait, the prediction outcomes were displayed on the “All results” tab, which encompassed individual tool results and the intersected findings.
- (5)
- Intersection selection: Given that some tools may yield sparse predictions or lack data, we provided an option box to select well-predicted datasets for intersection analysis, which is visualized through a Venn diagram.
- (6)
- Visualize intersections: The “Venn diagram” tab allowed for the visualization of overlapping predictions across multiple tools using Venn or petal diagrams.
- (7)
- Individual dataset review: The “Individual dataset” tab enabled viewing and downloading detailed information for each tool’s predictive results.
3.2. Module 2: Procedures for the Prediction of Upstream TFs of Target Genes
- (1)
- Input a target gene symbol: GAPDH is an example of this.
- (2)
- Select datasets: Participants then chose the predictive tools to include in the analysis. Notably, if “KnockTF” was selected, an additional interface element appeared: a slider for setting the log2 fold-change (Log2FC) threshold, along with a checkbox to include only downregulated genes. This specification is necessary as KnockTF predictions are based on differential gene expression data following TF knockdown or knockout.
- (3)
- Select correlation datasets (optional): Users can further enhance the accuracy of predictions based on the correlation analysis results of TF and target gene expression in different tissues based on the TCGA and GTEx databases. Also, the threshold for the correlation coefficient is set to 0.3.
- (4)
- Results: Similar to Module 1, the predicted results were displayed on the “All results” Table. Also, users can select datasets with robust predictions for intersection analysis.
3.3. Module 3: Pan-Tissue Correlation Analysis between the Expression of Predicted TF–Target Pair
- (1)
- TF and target gene input: The user begins by selecting a transcription factor and entering the symbol for the target gene.
- (2)
- Database selection: The database(s) for analysis is/are chosen from among TCGA, GTEx, and CCLE. Notably, upon selecting TCGA, a popup menu appears, offering the user the option to include tumor data exclusively.
- (3)
- Correlation analysis parameters: Parameters for correlation analysis are set, including the selection of the analysis method and the establishment of thresholds for the correlation coefficient and p-value.
- (4)
- Correlation results and scatter plot: Data retrieval and correlation analysis are initiated by clicking the “Go” button. Subsequently, the results of the correlation analysis are presented, along with a scatter plot illustrating the expression correlation.
- (5)
- Plotting parameter: Options are provided to adjust parameters relevant to the scatter plot visualization.
- (6)
- Detailed scatter plot: Clicking on a row within the results table prompts a popup window that displays a detailed scatter plot for the expression of the two genes within a single tissue type.
3.4. Module 4: TF–Target Regulation Network Analysis
- (1)
- Data upload: Users upload their gene expression differential analysis results. It is important to ensure that the column names in the uploaded file are consistent with those in the example data provided.
- (2)
- Differential gene selection criteria: Set the thresholds for selecting differentially expressed genes, specifically the log2 fold change (log2FC) and p-value.
- (3)
- TF different expression result: The ‘TF result’ page will exhibit the differential analysis results of TFs extracted from the user’s uploaded data.
- (4)
- Datasets selection: Choose the predictive tools to be included in the analysis for identifying TF–target gene relationships.
- (5)
- TF List Update: Upon input completion, the ‘TF to analysis’ input field automatically updates with a list of TFs. This list is generated based on the intersection of differentially expressed genes from the uploaded results and the TFs contained within the chosen predictive tools.
- (6)
- Network Visualization: Clicking the ‘Go’ button starts the predictive analysis process. After the analysis is complete, a network diagram is generated. Note that some TFs may not display target genes in the network diagram if no target genes are identified after intersecting the results from multiple tools. In such cases, it may be beneficial to reduce the number of tools included in the analysis to obtain more extensive information.
- (7)
- Plotting Data Interface: The ‘Plotting data’ interface will present the predicted results for TF–target genes.
4. Discussion
5. Conclusions
Supplementary Materials
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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R Package | Version | Function |
---|---|---|
shiny [23] | 1.8.0 | Building interactive web application UI |
bs4Dash [24] | 2.3.0 | Advanced UI design with Bootstrap 4 integration |
jsonlite [25] | 1.8.8 | Parsing JSON data |
UCSCXenaShiny [26] | 1.1.10 | Extracting gene expression data from XENA database |
ggplot2 [27] | 3.4.4 | Data visualizations |
igraph [28] | 1.6.0 | Network graph visualization |
VennDiagram [29] | 1.7.3 | Venn diagram visualization |
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Wang, J. TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor–Target Interactions. Biomolecules 2024, 14, 749. https://doi.org/10.3390/biom14070749
Wang J. TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor–Target Interactions. Biomolecules. 2024; 14(7):749. https://doi.org/10.3390/biom14070749
Chicago/Turabian StyleWang, Jin. 2024. "TFTF: An R-Based Integrative Tool for Decoding Human Transcription Factor–Target Interactions" Biomolecules 14, no. 7: 749. https://doi.org/10.3390/biom14070749