Currently, I am involved in carrying out analysis of large-scale data sets to understand the genetics of neurodegenerative diseases. I will be analyzing data from clinical samples and mouse models of Alzheimer's disease to determine how genetic risk factors lead to dementia. Additionally, characterize the effects on the retina of genetic mutations that increase risk for eye disease. This work will substantially broaden our knowledge of the molecular mechanisms behind common neurodegenerative diseases.
Previously, I have been working on problems like understanding the evolution of genomes by identification of evolutionary strata in sex chromosomes of mammals, birds and plants using Markov model of segmentation and clustering, which can further help in resolving many epigentics related problems like X chromosome inactivation, Identification of horizontally transferred genes, which can have evolutionary, ecological and potential biotechnological significance in recipient species and more robust taxonomic profiling of metagenomic data. Beside this, I have been also involved in many projects, which were focused on differential gene expression, functional and pathway analysis of NGS/RNA-seq data.
Mammalian sex chromosomes arose from a pair of homologous autosomes that differentiated into the X and Y chromosomes following a series of recombination suppression events between the X and Y. The stepwise recombination suppressions from the distal long arm to the distal short arm of the chromosomes are reflected as regions with distinct X-Y divergence, referred to as evolutionary strata on the X. All current methods for stratum detection depend on X-Y comparisons but are severely limited by the paucity of X-Y gametologs. We have developed an integrative method that combines a top-down, recursive segmentation algorithm with a bottom-up, agglomerative clustering algorithm to decipher compositionally distinct regions on the X, which reflect regions of unique X-Y divergence. In application to human X chromosome, our method correctly classified a concatenated set of 35 previously assayed X-linked gene sequences by evolutionary strata. We then extended our analysis, applying this method to the entire sequence of the human X chromosome, in an effort to define stratum boundaries. The boundaries of more recently formed strata on X-added region, namely the fourth and fifth strata, have been defined by previous studies and are recapitulated with our method. The older strata, from the first up to the third stratum, have remained poorly resolved due to paucity of X-Y gametologs. By analyzing the entire X sequence, our method identified seven evolutionary strata in these ancient regions, where only three could previously be assayed, thus demonstrating the robustness of our method in detecting the evolutionary strata.