• Structural variations in cancer genomes
• Evolution of genome-architecture;
• Prognostic gene expression markers for ovarian cancer.

Recent large-scale sequencing efforts of cancer genomes indicate that genome rearrangements in cancer are more pervasive than earlier thought. A term, called “chromothripsis”, has been coined to describe the massive genome reorganization that happens in a subset of cancer genomes. Such reorganizations frequently create gene fusions that are oncogenic. We are investigating the genome rearrangements in cancer using probabilistic models to identify significant “driver” structural rearrangements. Various modeling techniques including Bayesian, maximum likelihood and support vector machine are being evaluated to gain insight into the structural rearrangements in cancer genomes. The goals are two folds: (a) Investigate and evaluate various statistical modeling techniques to identify significant structural rearrangements in cancer. (b) Investigate evolution of cancer genomes by following progression of genomic rearrangements in various grades of cancer.

We use computational linguistic technique to understand genome as a language. We are currently finding the properties of these languages and their roles in evolution.

We published recently a gene expression signature that can identify a subset of patients with worst outcome. In this project, we are sequencing ovarian cancer patients to create a prognostic and predictive marker to create a better therapy for ovarian cancer.