The field of cancer genomics is currently being enhanced by the power of Epigenome-wide association studies (EWAS). Over the last couple of years comprehensive sequence data sets have been generated, allowing analysis of genome-wide activity in cohorts of different individuals to be increasingly available. Finding associations between epigenetic variation and phenotype is one of the biggest challenges in biomedical research. Laboratories lacking dedicated resources and programming experience require bioinformatics expertise which can be prohibitively costly and time-consuming. To address this, we have developed a collection of freely available Galaxy tools (Poterlowicz, 2018a), combining analytical methods into a range of convenient analysis pipelines with graphical user-friendly interface.The tool suite includes methods for data preprocessing, quality assessment and differentially methylated region and position discovery. The aim of this project was to make EWAS analysis flexible and accessible to everyone and compatible with routine clinical and biological use. This is exemplified by my work undertaken by integrating DNA methylation profiles of melanoma patients (at baseline and mitogen-activated protein kinase inhibitor MAPKi treatment) to identify novel epigenetic switches responsible for tumour resistance to therapy (Hugo et al., 2015). Configuration files are publicly published on our GitHub repository (Poterlowicz, 2018b) with scripts and dependency settings also available to download and install via Galaxy test toolshed (Poterlowicz, 2018a). Results and experiences using this framework demonstrate the potential for Galaxy to be a bioinformatics solution for multi-omics cancer biomarker discovery tool.