Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Interpreting R Charts01:22

Interpreting R Charts

118
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
118
Introduction to R01:11

Introduction to R

621
R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
621
Ribosome Profiling02:24

Ribosome Profiling

3.6K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Preterm Birth Increases Susceptibility to Hyperglycemia-Induced Kidney Injury With Sex-Specific Differences in Structural and Molecular Responses.

Endocrinology, diabetes & metabolism·2026
Same author

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

Genes to cells : devoted to molecular & cellular mechanisms·2026
Same author

Integrating multi-omic QTLs and predictive models reveals regulatory architectures at immune related GWAS loci in CD4+ T cells.

medRxiv : the preprint server for health sciences·2026
Same author

Single-cell map of the female brain across reproductive transitions.

bioRxiv : the preprint server for biology·2026
Same author

Egr1 is a sex-dependent regulator of neuronal chromatin, structural plasticity, and behaviour.

Nature communications·2025
Same author

Preterm birth increases susceptibility to hyperglycemia induced glomerular alterations in male mice.

Scientific reports·2025

Related Experiment Video

Updated: Sep 15, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K

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

Sujith Taridalu1, Ayyappa Kumar Sista Kameshwar1, Masako Suzuki1

  • 1Department of Nutrition, Texas A&M University, College Station, TX, USA.

Biorxiv : the Preprint Server for Biology
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

ERSAtool simplifies complex RNA sequencing (RNA-seq) data analysis with an R/Shiny interface, making transcriptomic studies accessible. This educational tool aids researchers and students in gene expression analysis and functional interpretation.

Keywords:
Differential gene expressionEducational toolFunctional enrichmentInteractive visualizationRNA-seq analysisShiny applicationTranscriptomics

More Related Videos

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K

Related Experiment Videos

Last Updated: Sep 15, 2025

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.7K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K
High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
14:58

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Published on: March 5, 2022

4.4K

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • RNA sequencing (RNA-seq) is crucial for gene expression analysis but requires significant bioinformatics expertise.
  • The complexity of RNA-seq data analysis presents a barrier for many students and researchers.
  • A need exists for user-friendly tools to facilitate advanced transcriptomic analyses.

Purpose of the Study:

  • To introduce ERSAtool, an R/Shiny interface designed to simplify RNA sequencing data analysis.
  • To provide an intuitive graphical visualization of the entire RNA-seq workflow.
  • To enhance accessibility of sophisticated transcriptomic analyses for educational and research purposes.

Main Methods:

  • Developed ERSAtool as a comprehensive R/Shiny application.
  • Integrated established Bioconductor packages for high-standard analyses.
  • Supported various input formats and direct integration with Gene Expression Omnibus (GEO) for metadata.
  • Guided users through normalization, visualization, differential expression, and functional enrichment analyses (GO, GSEA).

Main Results:

  • ERSAtool offers an intuitive graphical interface for RNA-seq analysis.
  • The tool simplifies complex steps like normalization, differential expression, and functional interpretation.
  • Generated comprehensive, downloadable reports for enhanced reproducibility and knowledge sharing.
  • Facilitated educational use in transcriptomics courses.

Conclusions:

  • ERSAtool significantly lowers the technical barrier for RNA-seq data analysis.
  • The tool improves accessibility to advanced transcriptomic capabilities for a wider audience.
  • ERSAtool has the potential to accelerate discoveries in various biological fields by democratizing RNA-seq analysis.