The focus of our team is computational genomics and systems biology. Our research goals can be broadly classified into the following areas:

1. Regulatory genomics and epigenomics. Our primary interest is to develop novel methods for the systematic discovery of regulatory motifs (DNA, RNA, epigenomic). To this end, we apply different statistical/computational approaches including Hidden Markov Models (HMMs) and other machine learning methods (such as SVM, ANN). We are currently interested in investigating large-scale publicly available "histone marks" datasets for annotation of genomic regions, networks, biological correlate of chromatin states, etc…
2. Reconstructing small and large scale transcriptional networks, with applications to modeling perturbations in disease networks (e.g. cancer). We are also developing alternative protocols for fast and memory efficient detection of network motifs.
3. Novel algorithms for management and analysis of large, genomic datasets including deep sequencing data, genome-wide "histone marks", etc… This project also involves developing value-added databases of interest to the bio-medical community.
4. Investigating the role of microRNAs in cancer. Unlike most of the other studies in our lab, this project involves both computation and "wet laboratory" experiments. We are studying the relevance of deregulated miRNA expression in primary tumor samples from head and neck cancer patients (HNSCC).

Research Grants

1. 2009-2012, Department of Science and Technology (DST, India). Grant support under Young Scientist FAST-Track Scheme for studying the role of microRNAs in etiologically diverse oral carcinomas. 2)Co-Investigator in a Department of Biotechnology (DBT) funded project (2008-2014), to develop vaccines against Classical Swine Fever (CSF) and delivery through conjugated nanoparticles.