Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Aug 18;30(5):e70044. doi: 10.1111/gtc.70044

ERSAtool: A User‐Friendly R/Shiny Comprehensive Transcriptomic Analysis Interface Suitable for Education

Sujith Taridalu 1, Ayyappa Kumar Sista Kameshwar 1, Masako Suzuki 1,✉
PMCID: PMC12360868  PMID: 41677189

ABSTRACT

RNA sequencing (RNA‐seq) has become an essential technology for assessing gene expression profiles in biomedical research. However, the coding complexity of RNA‐seq data analysis remains a significant barrier for students and researchers without extensive bioinformatics expertise. We present the Educational RNA‐Seq Analysis tool (ERSAtool), a comprehensive R/Shiny interface that provides an intuitive graphical visualization of the complete RNA‐seq analysis workflow. The application is built on established Bioconductor packages and upholds high standards in analyses while significantly reducing the technical expertise required to conduct sophisticated transcriptomic analyses. ERSAtool supports various input formats, such as raw count matrices and STAR alignment outputs. It generates sample information metadata through direct integration with the international public repository, Gene Expression Omnibus (GEO). The application guides users through normalization, data visualization, differential expression analysis, and functional interpretation using Gene Ontology and Gene Set Enrichment Analyses. All results can be compiled into comprehensive, downloadable reports that enhance reproducibility and knowledge sharing. The design targets features that support educational use, making it especially helpful for teaching transcriptomics in undergraduate to graduate‐level bioinformatics courses and enhancing access to advanced transcriptomic analysis, potentially accelerating discoveries across various biological fields. ERSAtool is available for free at https://github.com/SuzukiLabTAMU/ERSAtool.

Keywords: educational tool, functional enrichment, interactive visualization, RNA‐seq analysis, shiny application, transcriptomics differential gene expression

The Educational RNA‐Seq Analysis tool (ERSAtool) provides a comprehensive R/Shiny interface with an intuitive graphical visualization of the entire RNA‐seq analysis pipeline, enabling users to perform complex transcriptomic analyses with less technical expertise.

graphic file with name GTC-30-0-g004.jpg

1. Introduction

In current biomedical research, RNA‐seq is the gold standard for transcriptome analysis, facilitating comprehensive profiling of gene expression across various biological systems (Conesa et al. 2016; Stark et al. 2019). As sequencing costs decline, RNA‐seq technology has become more widely accessible. Yet, the computational analysis of RNA‐seq data remains a considerable challenge for many biologists, clinicians, and students who lack specialized expertise in bioinformatics and statistics (Conesa et al. 2016). Although the Bioconductor project offers powerful open‐source R packages for RNA‐seq analysis, its command‐line interface poses a significant learning barrier for researchers without programming experience (Huber et al. 2015; Love et al. 2014).

Several RNA‐seq pipelines have attempted to address this gap. Still, limitations persist due to factors such as restricted analytical scope, limited customizable options, data privacy concerns related to external web servers, and complex installation requirements (Gu et al. 2016; Nelson et al. 2017; Russo et al. 2016). We developed a comprehensive and interactive Shiny application, the Educational RNA‐Seq Analysis tool (ERSAtool), that supports a complete RNA‐seq analysis workflow. It significantly reduces technical barriers and offers a user‐friendly interface maintaining the analytical rigor of established Bioconductor packages, including DESeq2 and ClusterProfiler (Love et al. 2014; Wu et al. 2021; Yu et al. 2012).

ERSAtool tackles the frequent challenges that beginners face in RNA‐seq analysis, including complex installation steps, creating sample metadata, evaluating and visualizing results, and generating a detailed report that includes code for reproducibility. Additionally, the application supports various input formats, provides direct NCBI GEO integration for metadata management, and guides users through the entire analytical process from normalization to functional interpretation. It enables users to analyze transcriptomic data independently and fosters critical thinking in biological disciplines. We illustrated a workflow for RNA‐seq analysis using the ERSAtool in Figure 1.

FIGURE 1.

FIGURE 1

Open in a new tab

RNA‐seq analysis workflow using ERSAtool.

2. Methods

2.1. Implementation and Architecture

ERSAtool is designed as a modular Shiny application in R (≥ 4.1) featuring a component‐based architecture that enhances maintainability and facilitates expansion. The application incorporates established Bioconductor packages into a cohesive analytical framework that can be executed through a user‐friendly interface.

The core structure is organized around a main entry point (App.R) and global configuration (global.R), with distinct directories for interface components (ui/), server logic (server/), and static assets (www/). User interface components are segregated into module‐specific files (e.g., metadata_ui.R, pca_heatmap_ui.R), promoting component reusability and independent development while maintaining a cohesive user experience through the dashboard framework (dashboard.R). Server logic is organized into dedicated four subdirectories: (1) code: implements essential analytical functions for normalization, differential expression, and enrichment analyses; (2) plots: manages visualization rendering for each analysis module; (3) downloads: oversees the export of results and images in formats suitable for publication; and (4) report: enables the creation of detailed analytical reports. This architecture enables independent development of application components and maintains integration across the analytical workflow.

2.2. A Step‐By‐Step Workflow Directing Users Through RNA‐Seq Analysis

  1. Loading data: ERSAtool requires two data inputs for analysis: sample metadata and raw count data. The sample metadata can be either a user‐provided local data file (.txt, .csv, .xlsx) or a Series Matrix File retrieved directly from GEO using the accession number. The first column should contain sample identifiers. Since the application uses DESeq2 (Love et al. 2014) for analysis, the expression data should be in raw count format. The user can upload a row count matrix (.gz, .csv, .txt, .xlsx) or STAR aligner outputs (.out. tab). Both formats should have the first column serving as a gene identifier. For row count matrices, the remaining columns represent sample counts. When the user chooses STAR aligner outputs as count data, it retrieves counts from designated columns (unstranded RNA‐seq counts, counts for the first read strand aligned with RNA, or counts for the second read strand aligned with RNA) and extracts sample names from the file names prior to merging them.

  2. Generating an analysis design formula: After users upload the sample metadata, the interface allows adjustments such as adding, removing, or renaming columns, inline editing of cells, and modifying rows in a paginated table format. Users can also export the modified sample metadata in an Excel format for future use. To generate a design formula, users select the column(s) they wish to include in the analysis.

  3. Initial assessment: In the initial phase of the analysis, the application offers a guided assessment utilizing interactive visualizations, including box‐whisker plots of raw and normalized counts, a pair plot of principal components (PC1 and PC2), and heatmaps from hierarchical clustering. The boxplot displays variations in sequencing depth across samples before and after normalization, while the PC pair plots and heatmaps reveal similarities in expression profiles among the samples.

  4. Differential expression analysis: The application utilizes DESeq2 (Love et al. 2014). Count data undergoes pre‐processing validation before being created into a DESeqDataSet. Low‐expressed genes are filtered using a minimum threshold of 1 count per sample. The DESeq2 core algorithm then estimates library size factors of each sample, calculates dispersion values, and fits negative binomial generalized linear models for each gene, storing results for visualization and analysis. Differentially expressed genes (DEGs) are identified using the statistical framework of DESeq2. The application includes features with adjustable threshold cutoffs for false discovery rate (FDR)‐adjusted p‐values and log2‐fold changes (logFC), and it accommodates a flexible experimental design for multiple‐factor study designs. These accommodations follow established best practices for contrast specification and DEG identification in DESeq2, enabling the analysis of various experimental designs without requiring users to manually create design formulas, contrast matrices or set thresholds for FDR‐adjusted p‐values and logFC.

  5. Interpretation of differentially expressed genes: The application supports two types of assays for interpreting gene expression profiles using the enrichGO and gseGO functions of clusterProfiler (Wu et al. 2021; Yu et al. 2012): Gene Ontology (GO) enrichment (GO‐enrichment) and GO gene set enrichment analysis (GO‐GSEA). GO‐enrichment analysis assesses whether the identified significant DEGs are enriched in specific pathways, while GO‐GSEA evaluates if a particular gene set is enriched or depleted in the dataset. These results can provide insights into the biological processes or pathways associated with a specific phenotype or condition. The GO‐enrichment results are reported separately for the biological process and molecular function categories.

  6. Report and result file generation: In the final step, the application can compile all analysis results into a comprehensive document that captures the entire analytical process. It also includes a function to export the result files (the list of DEGs and DO terms) in CSV format, as well as the plot images in PNG, JPEG, TIFF, and PDF formats.

To support these capabilities, ERSAtool integrates key R/Bioconductor packages into a unified framework, including core functionality packages (shiny, shinydashboard, rmarkdown) (Chang et al. 2024), data processing tools (data. table, dplyr, readr, readxl) (Barrett et al. 2024; Wickham and Bryan 2024; Wickham, François, et al. 2024; Wickham, Hester, and Bryan 2024), RNA‐seq analysis packages (DESeq2, GEOquery) (Davis and Meltzer 2007; Love et al. 2014), visualization libraries (ggplot2, plotly, pheatmap, PCAtools) (Kolde 2024; Marini and Binder 2019), and functional analysis tools (clusterProfiler, enrichplot, fgsea) (Sergushichev 2017; Wu et al. 2021; Yu 2018; Yu et al. 2012). ERSAtool requires R (version 4.1 or later), RStudio (recommended), and an internet connection for initial package installation and retrieval of GEO data. The application is launched by executing App R through the RStudio interface or via shiny::runApp (Chang et al. 2024). Upon initial execution, the application automatically detects missing package dependencies. It then downloads and manages all dependencies through runtime package verification, thereby reducing installation barriers for users with varying coding skills. The application is designed for local deployment, facilitating analysis of sensitive data without external transfer while maintaining accessibility for users with basic R installation knowledge. It is also suitable for educational settings with limited computational resources. A detailed step‐by‐step user guide is supplemented as Supporting Information 1.

2.3. Educational Functions and Tools

ERSAtool is designed to guide students and researchers through a structured RNA‐seq workflow, featuring dynamic visualizations that update as data is inputted. Dedicated interpretation text boxes beneath each visualization prompt users to document their insights during the analysis process. The entire workflow, including all user interpretations, can be exported as a static HTML report suitable for documentation or collaborative sharing, creating an efficient yet flexible approach that combines bioinformatics analysis with guided critical interpretation of biological significance. This structured framework enables users to concentrate on biological meaning while the tool manages computational complexity. In addition, the function to export the DEG or enriched pathway results allows the users to perform further analysis using web‐based analyses, such as EnrichR (Chen et al. 2013), STRING (Szklarczyk et al. 2023), GeneVenn (Pirooznia et al. 2007), and more. For advanced learners, the application displays all the code used at every stage, assisting them in writing their own code later.

2.4. Approximate Time Required to Process Each Step

Processing time is essential information for users, particularly beneficial for instructors designing coursework and for users understanding the expected analysis timeline. Therefore, we assessed the processing time for each step using two common operating systems, Mac and Windows, with a large publicly available RNA‐seq dataset (GSE72759). The specifications of the machines are as follows: Mac—MacBook Air (M2, eight cores (four performance and four efficiency), RAM: 16 GB) and Windows—HP EliteOne 870 G9 (12th Gen Intel(R) Core(TM) i7‐12,700, 2.10 GHz, RAM: 32 GB). The GSE72759 contains 376 RNA‐seq samples from the livers of 188 mice in two dietary intervention groups (98 fed a standard diet and 94 fed a high‐fat diet). We randomly selected 12 (n = 6 per group), 24 (n = 12 per group), 48 (n = 24 per group), and 96 (n = 48 per group) samples and analyzed them using ERSAtool. The processing time was measured by repeating the process three times on each operating system. Both operating systems showed comparable processing times (Figure 2). All steps, including report generation, were completed within 40 s for up to 24 samples and within 135 s for 48 samples. For 96 samples, the PCA/heatmap generation and report generation steps significantly increased the processing time, with 468.94 ± 3.78 s (Mac) and 313.13 ± 4.91 s (Windows) for PCA/heatmap generation and 739.13 ± 6.42 s (Mac) and 547.71 ± 12.75 s (Windows) for report generation, respectively. If users analyze more than 100 samples, we recommend using a traditional approach by writing the code themselves.

FIGURE 2.

FIGURE 2

Open in a new tab

Comparison of processing times across various sample sizes. We compared processing times (in seconds) for each step across different sample sizes on two widely used operating systems: Mac (top panels) and Windows (bottom panels). Each marker represents an independent analysis, while the bars show the average processing time for that step. Both operating systems demonstrated comparable processing times, with all steps completed within a reasonable timeframe (under 2.5 min) for up to 48 samples on both systems. Symbols indicate sample sizes: ● for 12, ■ for 24, ▲ for 48, and ◆ for 96.

3. Use Cases

3.1. Case Study: Identifying Changes in Tissue‐Specific Gene Expression of Adipose Precursor Cells Resulting From High‐Fat Diet Intake

We used a subset of a publicly available dataset (GSE273569 (Wing et al. 2025)) for this case study, which focuses on male mice. The original study tested the obesogenic effects of dietary oleic acid on adipocyte precursor cells (APCs). The authors performed transcriptional analysis on APCs from subcutaneous white adipose tissue and visceral white adipose tissue, which were sorted from mice fed a standard diet or a 60% kcal lard high‐fat diet for 3 days, a period when APC proliferation peaks. The authors found that high‐fat diet feeding increases visceral white adipose tissue APC proliferation in males (Wing et al. 2025). While the authors reported their findings with a sophisticated analysis, we performed a straightforward analysis focusing on male samples as a case study. We downloaded the unnormalized count and sample matrix files from the Gene Expression Omnibus (GEO). We provided those files as a training dataset (Supporting Information 3 and 4). The primary goal of this analysis was to evaluate the differentially expressed genes in APCs derived from two types of adipose tissue in response to a high‐fat diet. We created Figure 3 using the original exported images from the download plot functions. The report HTML file of this analysis has been provided as Supporting Information 5.

  1. Count distribution analysis: As a primary assessment of RNA‐seq, we examined whether the raw and normalized count distributions were comparable across all libraries (Figure 3A,B). Although we noted that some samples exhibited lower raw count distributions (Figure 3A), the normalized count distributions were similar among all samples, indicating comparable sequencing depth for the libraries (Figure 3B).

  2. Assessing gene expression profile similarity: We then visualized the gene expression profile similarity between the samples using two independent approaches: principal component analysis (PCA, Figure 3C) and hierarchical clustering (Figure 3D). Both results showed that the primary factor differentiating the gene expression profiles is the origin of the tissue from which the APCs are derived, with diet being a secondary consideration. The PCA analysis revealed that PC1, which accounts for 66.3% of the variation, dissociates visceral white adipose tissue (VWAT) from subcutaneous white adipose (SWAT), while PC2, accounting for 18.4% of the variation, differentiates standard diet (SD) from high‐fat diet (HFD). Although it was grouped within visceral white adipose tissue, one sample from the VWAT‐HFD group did not segregate or branch into other VWAT‐HFD samples, suggesting that individual variations might exist in response to HFD.

  3. Differentially expressed gene analysis: We identified differentially expressed genes between VWAT‐HFD and SWAT‐HFD that exhibit at least 1.5‐fold differences with an adjusted p‐value of 0.05. Based on this criterion, we have 1374 upregulated and 1291 downregulated genes in VWAT‐HFD compared to SWAT‐HFD (Figure 3E). The upregulated genes are enriched in cell cycle pathways, while the downregulated genes are enriched in immune‐response pathways (Figure 3F). While upregulation of cell cycle‐related pathways in VWAT‐HFD is concordant with what the original study reported, this comparison did not account for the gene expression variations between VWAT and SWAT. Therefore, we analyzed VWAT‐SD and SWAT‐SD and integrated the results to identify DEGs specific to VWAS in response to HFD. In the comparison between VWAT‐SD and SWAT‐SD, we identified 1281 VWAT upregulated and 1161 downregulated genes compared to SWAT (Figure 4A). The lists of tissue origin‐dependent DEGs in HFD and SD were exported, respectively. We used GeneVenn, a web‐based Venn diagram generator (Pirooznia et al. 2007), to intersect the DEG lists, identifying both overlapping and non‐overlapping tissue origin‐dependent DEGs. Since we are interested in whether the upregulations in cell cycle‐related genes are specific to VWAT, we focused on 408 VWAT‐specific upregulated genes for the downstream analysis (Figure 4B). We assessed these 408 VWAT‐specific upregulated genes using EnrichR, a web‐based gene set knowledge discovery tool (Chen et al. 2013), and found enrichments in cell cycle‐related pathways among VWAT‐specific upregulated genes (Figure 4C). This suggests that the upregulation of cell cycle‐related pathways in response to HFD is specific to VWAT, aligning with the findings of the original report (Wing et al. 2025). Additionally, our analysis suggested that VWAT‐specific upregulated genes are enriched downstream of E2F1 (Figure 4D) and enriched Palbociclib (CDK4/6 inhibitor) downregulated genes (Figure 4E). The L1000 CRISPR KO analysis results also support that those upregulated genes were downregulated in the DTL, XPO1, HSPA5, CDK6, or CDK4 knockouts (Figure 4F). While the original article does not discuss the enrichment of the CDK4/6‐E2F targets in VWAT‐specific HFD upregulated genes, CDK4/6‐E2F has been implicated in adipogenesis in activating Pparg signaling (Abella et al. 2005; Fajas et al. 2002). Our findings indicate that the HFD diet may also differentially affect E2F‐CDK4/6 cell cycle regulators based on tissue type, along with LXRα signaling.

FIGURE 3.

FIGURE 3

Open in a new tab

Example RNA‐seq analysis on a publicly available dataset using ERSAtool. As a case study, we analyzed a subset of a publicly available RNA‐seq dataset (GSE273569) using ERSAtool. In this figure, we show a step‐by‐step analysis workflow with plots generated at each step. Count distribution analysis of the raw count (A) and the normalized count (B): While some samples showed slightly lower distributions in raw count, all samples demonstrated comparable count distributions after normalization. Assessing gene expression profile similarity: The similarity of gene expression profiles was evaluated using PCA (C) and hierarchical clustering (D). Both results indicate that the origin of APC was the primary factor influencing gene expression variations, while diet modulated the gene expression profiles. Differentially expressed gene analysis: Among mice fed HFD, we identified 1374 VWAT upregulated and 1291 downregulated genes compared to SWAT (E). GO enrichment analysis indicated that these upregulated genes are enriched in cell cycle‐related pathways (F). The plots in this figure are slightly retouched from the original PDF files created using the export image function of ERSAtool.

FIGURE 4.

FIGURE 4

Open in a new tab

Exemplar secondary analysis using publicly available web‐based tools. We analyzed the results using GeneVenn and EnrichR, which are both publicly available web‐based analysis tools. This integrative analysis aims to identify DEGs specific to VWAS in response to HFD. The DEG analysis between the SWAT and VWAT in the SD group identified 1281 VWAT upregulated and 1161 downregulated genes (A). The VWAT upregulated and downregulated DEGs in HFD and SD were independently intersected to identify DEGs specific to VWAS in response to HFD using GeneVenn (B). Upregulated DEGs specific to VWAS in response to HFD were further analyzed with another web‐based gene set enrichment analysis tool, EnrichR (C–F). (C) Reactome Pathways, (D) TRANSFAC and JASPER Position Weight Matrix, (E) LINCS L1000 Chemical perturbation consensus signals, (F) LINCS L1000 CRISPR KO consensus signals.

4. Discussion

ERSAtool offers comprehensive learning objectives for analyzing transcriptomic data through a well‐crafted, standalone Shiny application. The application helps users understand normalization principles, evaluate expression profile similarity in complex datasets through dimensionality reduction methods, introduce statistical testing for differential expression analysis, and facilitate functional interpretation via GO enrichment and GSEA. A distinctive feature that enhances the learning experience is a text box in each step, prompting users to interpret their observations during analysis, along with a show/hide code function that helps advanced users understand the code more deeply. These interpretations, along with the conclusion, will be included in the final report, providing support for student assignments and sharing the results. The tool enhances transferable skills such as critical evaluation of results, effective scientific communication, and making well‐thought‐out analytical decisions. In addition, further analysis of exported DEG lists using publicly available tools allows users to perform advanced analyses and interpret the results more deeply. This is conveyed in the case study. By combining computational rigor with guided analytical steps and interpretation prompts, ERSAtool effectively bridges the gap between the complexity of bioinformatics and biological inquiry, empowering users to focus on deriving meaningful insights from gene expression data.

Author Contributions

Conceptualization: Masako Suzuki, Data Curation: Sujith Taridalu, Ayyappa Kumar Sista Kameshwar, and Masako Suzuki, Formal Analysis: Sujith Taridalu and Masako Suzuki, Investigation: Sujith Taridalu and Ayyappa Kumar Sista Kameshwar, Software: Sujith Taridalu, Visualization: Sujith Taridalu, Supervision: Masako Suzuki, Funding acquisition: Masako Suzuki, Writing – original draft: Ayyappa Kumar Sista Kameshwar and Masako Suzuki, Writing – review and editing: Sujith Taridalu, Ayyappa Kumar Sista Kameshwar, and Masako Suzuki.

Ethics Statement

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting Information 1. A step‐by‐step guide to using ERSAtool.

GTC-30-0-s003.docx (7.6MB, docx)

Supporting Information 2. ERSAtool app (ERSAtool‐main.zip).

GTC-30-0-s004.zip (1.4MB, zip)

Supporting Information 3. Example raw count matrix file (GSE273569_Males_Unnormalized_Count_Matrix.txt).

GTC-30-0-s005.txt (797.2KB, txt)

Supporting Information 4. Example sample matrix file (GSE273569_series_matrix.xlsx).

GTC-30-0-s001.xlsx (9.8KB, xlsx)

Supporting Information 5. Example HTML output (Tools_07_18_25.html).

GTC-30-0-s002.html (3.8MB, html)

Acknowledgments

The authors thank Dr. Yoshinori Seki, Ms. Brighton A. Garret, Ms. Kaitlyn Carter, and Mr. William R. Leach for their feedback on the software and manuscript.

Taridalu, S. , Kameshwar A. K. S., and Suzuki M.. 2025. “ ERSAtool: A User‐Friendly R/Shiny Comprehensive Transcriptomic Analysis Interface Suitable for Education.” Genes to Cells 30, no. 5: e70044. 10.1111/gtc.70044.

Funding: This work was supported by the Texas A and M University and the National Institutes of Health (R01HL145302, R01DK136989).

Sujith Taridalu and Ayyappa Kumar Sista Kameshwar contributed equally to this study.

Transmitting Editor: Tadashi Uemura

Data Availability Statement

The data that support the findings of this study are openly available in GitHub at https://github.com/SuzukiLabTAMU/ERSAtool.

References

  1. Abella, A. , Dubus P., Malumbres M., et al. 2005. “Cdk4 Promotes Adipogenesis Through PPARgamma Activation.” Cell Metabolism 2: 239–249. [DOI] [PubMed] [Google Scholar]
  2. Barrett, T. , Dowle M., Srinivasan A., et al. 2024. Data.Table: Extension of `Data.Frame`. R Foundation. 10.32614/cran.package.data.table. [DOI] [Google Scholar]
  3. Chang, W. , Cheng J., Allaire J. J., et al. 2024. Shiny: Web Application Framework for R. R Foundation. 10.32614/cran.package.shiny. [DOI] [Google Scholar]
  4. Chen, E. Y. , Tan C. M., Kou Y., et al. 2013. “Enrichr: Interactive and Collaborative HTML5 Gene List Enrichment Analysis Tool.” BMC Bioinformatics 14: 128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Conesa, A. , Madrigal P., Tarazona S., et al. 2016. “A Survey of Best Practices for RNA‐Seq Data Analysis.” Genome Biology 17: 13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Davis, S. , and Meltzer P. S.. 2007. “GEOquery: A Bridge Between the Gene Expression Omnibus (GEO) and BioConductor.” Bioinformatics 23: 1846–1847. [DOI] [PubMed] [Google Scholar]
  7. Fajas, L. , Landsberg R. L., Huss‐Garcia Y., Sardet C., Lees J. A., and Auwerx J.. 2002. “E2Fs Regulate Adipocyte Differentiation.” Developmental Cell 3: 39–49. [DOI] [PubMed] [Google Scholar]
  8. Gu, Z. , Eils R., and Schlesner M.. 2016. “Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data.” Bioinformatics 32: 2847–2849. [DOI] [PubMed] [Google Scholar]
  9. Huber, W. , Carey V. J., Gentleman R., et al. 2015. “Orchestrating High‐Throughput Genomic Analysis With Bioconductor.” Nature Methods 12: 115–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Kolde, R. 2024. pheatmap: Pretty Heatmaps. R Foundation. 10.32614/cran.package.pheatmap. [DOI] [Google Scholar]
  11. Love, M. I. , Huber W., and Anders S.. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA‐Seq Data With DESeq2.” Genome Biology 15: 550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Marini, F. , and Binder H.. 2019. “pcaExplorer: An R/Bioconductor Package for Interacting With RNA‐Seq Principal Components.” BMC Bioinformatics 20: 331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Nelson, J. W. , Sklenar J., Barnes A. P., and Minnier J.. 2017. “The START App: A Web‐Based RNAseq Analysis and Visualization Resource.” Bioinformatics 33: 447–449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Pirooznia, M. , Nagarajan V., and Deng Y.. 2007. “GeneVenn—A Web Application for Comparing Gene Lists Using Venn Diagrams.” Bioinformation 1: 420–422. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Russo, F. , Righelli D., and Angelini C.. 2016. “Advancements in RNASeqGUI Towards a Reproducible Analysis of RNA‐Seq Experiments.” BioMed Research International 2016: 7972351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Sergushichev, A. 2017. Fast Gene Set Enrichment Analysis. Bioconductor. 10.18129/b9.bioc.fgsea. [DOI] [Google Scholar]
  17. Stark, R. , Grzelak M., and Hadfield J.. 2019. “RNA Sequencing: The Teenage Years.” Nature Reviews. Genetics 20: 631–656. [DOI] [PubMed] [Google Scholar]
  18. Szklarczyk, D. , Kirsch R., Koutrouli M., et al. 2023. “The STRING Database in 2023: Protein‐Protein Association Networks and Functional Enrichment Analyses for Any Sequenced Genome of Interest.” Nucleic Acids Research 51: D638–D646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Wickham, H. , and Bryan J.. 2024. readxl: Read Excel Files. R Foundation. 10.32614/cran.package.readxl. [DOI] [Google Scholar]
  20. Wickham, H. , François R., Henry L., Müller K., and Vaughan D.. 2024. dplyr: A Grammar of Data Manipulation. R Foundation. 10.32614/cran.package.dplyr. [DOI] [Google Scholar]
  21. Wickham, H. , Hester J., and Bryan J.. 2024. readr: Read Rectangular Text Data. R Foundation. 10.32614/cran.package.readr. [DOI] [Google Scholar]
  22. Wing, A. , Jeffery E., Church C. D., et al. 2025. “Dietary Oleic Acid Drives Obesogenic Adipogenesis via Modulation of LXRα Signaling.” Cell Reports 44: 115527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Wu, T. , Hu E., Xu S., et al. 2021. “clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data.” Innovation 2: 100141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Yu, G. 2018. enrichplot: Visualization of Functional Enrichment Result. Bioconductor. 10.18129/b9.bioc.enrichplot. [DOI] [Google Scholar]
  25. Yu, G. , Wang L.‐G., Han Y., and He Q.‐Y.. 2012. “clusterProfiler: An R Package for Comparing Biological Themes Among Gene Clusters.” OMICS 16: 284–287. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supporting Information 1. A step‐by‐step guide to using ERSAtool.

GTC-30-0-s003.docx (7.6MB, docx)

Supporting Information 2. ERSAtool app (ERSAtool‐main.zip).

GTC-30-0-s004.zip (1.4MB, zip)

Supporting Information 3. Example raw count matrix file (GSE273569_Males_Unnormalized_Count_Matrix.txt).

GTC-30-0-s005.txt (797.2KB, txt)

Supporting Information 4. Example sample matrix file (GSE273569_series_matrix.xlsx).

GTC-30-0-s001.xlsx (9.8KB, xlsx)

Supporting Information 5. Example HTML output (Tools_07_18_25.html).

GTC-30-0-s002.html (3.8MB, html)

Data Availability Statement

The data that support the findings of this study are openly available in GitHub at https://github.com/SuzukiLabTAMU/ERSAtool.

Articles from Genes to Cells are provided here courtesy of Wiley

RESOURCES