Epigenetics is the study of heritable phenotype changes that allow the cell to control gene activity without involving alterations in the DNA sequence. Therefore, epigenetic changes are modifications to DNA that regulate whether genes are turned on or off. Methylation occurring at cytosines in DNA represents the most stable type of epigenetic modification. Several studies show the role of DNA methylation in many different important biological processes (Yong et al., 2016) and in different diseases such as severe Covid-19, cardiovascular disease, cancer and diabetes or tuberculosis (Das et al., 2019, 2021). Therefore, deciphering the DNA methylation signature of the cell is the key in the study and understanding of these diseases. Nowadays DNA methylation analysis using arrays is a widely used method in research and clinical studies to study Epigenetics. Although several packages have been published to incur the results, most of them require a deep computational knowledge to perform the analysis. To resolve this limitation and provide an easily accessible solution for scientists, we plan to develop a user-friendly graphical tool that will assist the user along the full analysis starting from the raw data to the graphical representation of the results. We plan to use standard and established open-source published packages and pack them in an intuitive Shiny (Chang et al., 2021). We plan to make a modular tool so that besides the methylation algorithm that performs the principal analysis on raw data, the other modules can be used with different kind of datasets, as for example the graphical representation of differentially expressed genes (DEGs) from RNA-seq data. The tool will take in input only array of numbers (idat or csv file formats) so no human personal data from sequencing are involved in the development nor in the testing and use of this tool.