EpigenomeViewer: A Shiny Web Application for Visualizing Methylation Patterns Across Biological Samples
DNA methylation plays a crucial role in gene regulation and is involved in the development of numerous diseases including cancer. However, researchers often face challenges in visualising and analysing methylation data because of limited accessibility to user-friendly software that integrates intera...
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| Format: | Article |
| Language: | English |
| Published: |
Academica Press Solutions
2025-02-01
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| Series: | Malaysia Journal of Invention and Innovation |
| Online Access: | https://digit360.com.my/mjii/index.php/home/article/view/123 |
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| Summary: | DNA methylation plays a crucial role in gene regulation and is involved in the development of numerous diseases including cancer. However, researchers often face challenges in visualising and analysing methylation data because of limited accessibility to user-friendly software that integrates interactive visualisation with bioinformatics analysis. EpigenomeViewer is a Shiny web application developed to address this gap by providing a comprehensive platform for visualising DNA methylation patterns across tumour and normal biological samples. Users can upload their differentially methylated probes and explore methylation patterns using publication-ready figures, such as violin plots, heatmaps, and correlation matrices. Additionally, the app’s real-time filtering and correlation matrix analysis allowed researchers to identify co-methylation patterns, facilitating insights into gene regulation and potential biomarkers for disease. EpigenomeViewer is particularly useful in the study of drug resistance and early stage cancer biomarkers, where understanding methylation differences between tumour and normal samples is vital. With its export features, researchers can download high-quality visualisations for publication and presentation. This application is an invaluable tool for both experienced researchers and educators in bioinformatics and epigenetics, supporting a wide range of studies from academic research to clinical applications. Future developments include expanded support for multi-omics data and additional gene ontology features.
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| ISSN: | 2976-2170 |