Historically, the core strengths of our research laid in computational biology with special emphasis on the development of novel methods and software tools for predicting biological properties of the uncharacterized genes and gene products using models built from the known datasets. Over the past 18 years, we have developed a number of novel methods in the areas of 3-D protein structure alignment, prediction of subcellular localization of proteins, prediction of enzyme classes and domain-domain interactions in proteins, comparing cancer protein-protein interaction networks, and taxonomic profiling of metagenomic samples. We also developed a number of open-source bioinformatics databases, software packages and web applications for the research community. We believe that good science can be advanced through sharing, collaboration and team effort.
While we continue to develop novel methods in bioinformatics areas, lately our focus has shifted more toward analysis of genomic, proteomic and metagenomic data, and the application of next-generation sequencing (NGS) data analysis to cancer genomics and precision medicine. Most of the current projects in the laboratory involve development of new tools for analyzing ‘Big Data’ in Genomics, particularly the data related to Cancer Genomics.