NGS has opened opportunities and challenges of “big data science” to biologists and clinicians for genome-wide evaluation of genetic variations, expression of distinct RNA species, and epigenetic changes associated with development, aging, and disease. The rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Current computational methods are not able to harness the full potential of large genomic and epigenomic datasets being generated by innovations in NGS technology. Thus, a greater focus is needed on developing novel tools for computational and systems level analysis.RESEARCH INTERESTS
Currently, I have been focused on:
(i) Analysis, mining and modeling of NGS data to extract its biological relevance and place it into the context of our understanding,
(ii) Developing bioinformatics analysis pipelines to integrate diverse biological data sets at different resolutions, including genome, transcriptome and epigenome levels to address biological questions,
(iii) Building online bioinformatics databases and analysis tools for scientific community.