Abstract: “Although next generation sequencing of diseased traits has unraveled thousands of DNA alterations, the functional relevance of most of these mutations and how they relate to other epigenetic mechanisms are still poorly understood. Here, we present SnvDMiR as a freely–available R pipeline that conducts combinatorial proximity analysis between disease– associated SNVs, deregulated miRNAs, and differentially methylated regions (DMRs) to identify genomically adjacent SNV-miRNA pairs as well as SNV-DMR pairs. These variants could be further investigated as putative candidates for driving pathogenic processes in diseases. We demonstrated the usefulness of the SnvDMiR pipeline by applying it on a published set of breast cancer-related mutations, deregulated miRNAs, and DMRs. Our pipeline characterized potential driver mutations that are predicted to have damaging effects on related protein functions. Availability: http://gepard.bioinformatik.unisaarland. de/software”

Keywords: Single, Nucleotide, Variant, (SNV), somatic, mutation, deregulated, miRNA, differentially, methylated, region, (DMR), genomic, proximity, epigenetics, data, integration, breast, cancer.