DEVELOPMENT OF COMPUTATIONAL TOOLS TO STUDY THE PATTERNING OF DNA AND RNA METHYLATION IN HEALTHY AND DISEASE STATES

Epigenetics can be defined as the set of sequence independent processes that produces heritable changes in cellular information. These chromatin-based events such as covalent modification of DNA and histone tails are laid down by the co-ordinated action of chromatin modifying enzymes, thus altering the organisation of chromatin and its accessibility to the transcriptional machinery. Our understanding of epigenetic intricacies has considerably increased over the last decade owing to rapid development of genomic and proteomic technologies. This has resulted in huge surge in the generation of epigenomics data. Integrative analysis of these epigenomics datasets provides holistic view on the interplay of various epigenetic components and possible aberration in patterns in specific biological or disease states. Although, there are numerous computational tools available catering individually to each epigenomic datatype, a comprehensive computational framework for integrated exploratory analysis of these datasets was missing. We developed a suite of R packages methylPipe and compEpiTools that can efficiently handle whole genome base-resolution DNA methylation datasets and effortlessly integrate them with other epigenomics data. We applied these methods to the study of epigenomics landscape in B-cell lymphoma identifying a putative set of tumor suppressor genes. Moreover, we also applied these methods to explore possible associations between m6A RNA methylation, epigenetic marks and regulatory proteins.