My interests include computational methods to estimate the genetic, epigenetic and transcriptional profiles of cells involved in disease like cancer and diabetes. Currently we are able to profile the transcriptional and epigenetic profiles of the cells involved in these diseases at single cell resolution. These technologies generate huge amount of data and there is an urgent need to develop novel methods to analyze these data to generate biological insights.
I also coordinate the efforts to characterize the genome of patient-derived xenograft (PDX) models developed at JAX. PDX models are essentially human tumors engrafted in immunodeficient mice and are excellent models to study therapeutic response to cancer. We assess the genomic mutations and gene-expression profiles of these tumors using next-generation DNA sequencing technology.
A JAX team developed CUP-AI-Dx, a machine learning tool that uses RNA sequence data for analysis. The researchers show that CUP-AI-Dx provides an important clinical tool to help guide therapies for CUP patients.