DNA modifications contribute to diseases such as cancer and it has been found to be highly predictive of age, demonstrating the value of epigenomics data to understand the profile of each individual patient. We plan to use an anomaly detection method based on deep learning models that can identify DNA modification from nanopore sequencing data. Existing approaches are limited to specific type modification and work on a subset of modified and unmodified nucleotides or previously trained sequences. Successful detection of DNA modifications in an accurate manner would enable large scale profiling of patients in Singapore and opens new venues for large scale projects related to use of AI in genetics.