Pattern mining from HTPS data, non-coding RNA biology and DNA methylation.
Recent advances in DNA sequencing technology led to the generation of vast amount of sequencing data and provided an unprecedented opportunity to understand the complexity of genome. However, this massive flood of sequencing data also created challenges in mining patterns of interest from this data. Moreover, diversity of datasets [RNA-SEQ, ChIP-Seq, Exome etc] generated from these technologies also demand more integrated evaluation across various sequencing platforms and data types. My dissertation was focused on deriving meaningful patterns from these datasets generated from both model and non-model species and also studying the variations in non-coding RNA (ncRNA) secondary structure and developing a novel method for ncRNA detection in the genome using patterns of chromatin-modifications. My post-dissertation work primarily concentrated on developing systems/algorithms to effectively analyze next generation sequencing data involving mammalian genome.