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Related Experiment Video

Updated: Jun 25, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

EMImR: a Shiny application for identifying transcriptomic and epigenomic changes.

Hiba Ben Aribi¹, Careen Naitore², Farah Ayadi¹

¹Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia.

Gigabyte (Hong Kong, China)

|April 10, 2026

Summary

This summary is machine-generated.

This study introduces EMImR, a user-friendly application for identifying gene expression changes and epigenetic modifications. It helps researchers explore the links between transcriptomic and epigenomic data to understand disease mechanisms.

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Last Updated: Jun 25, 2026

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Area of Science:

Bioinformatics
Genomics
Molecular Biology

Background:

Gene expression profiling is key to understanding diseases but is influenced by epigenomic factors like DNA methylation and microRNA.
Identifying these regulatory mechanisms is essential for discovering biomarkers and therapeutic targets.

Purpose of the Study:

To develop a user-friendly application for identifying and correlating transcriptomic and epigenomic changes.
To provide a tool for analyzing differentially expressed genes, differentially methylated genes, and differentially expressed interfering microRNAs.

Main Methods:

Development of a novel Shiny application named EMImR.
Integration of Bioconductor and CRAN packages for data analysis.
Implementation of a graphical user interface for ease of use.

Main Results:

EMImR successfully identifies differentially expressed genes, methylated genes, and microRNAs.
The application correlates transcriptomic and epigenomic modifications.
Ontology analysis of genes of interest is performed.

Conclusions:

EMImR is a valuable tool for studying the regulatory effects of epigenetic factors on gene expression.
The application facilitates the identification of potential biomarkers and therapeutic targets in genetic pathologies.
The tool is publicly available on GitHub for broader research use.