My lab?s research goal is to develop open source integrative computational tools that perform secondary analysis of publicly available multi-omics biological, clinical and environmental exposure datasets to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response. With the recent advances in high-throughput technologies in biology, the cost of data generation has reduced tremendously, which enabled the generation of vast amounts of multi-omics datasets such as gene expression, microRNA expression, copy number alteration, and DNA methylation. Numerous international and national consortiums have been established to generate these multi-omics datasets to study regulatory elements in DNA, disease and healthy tissues, epigenetic signatures, and drug responses. Furthermore, ongoing large initiatives such as UK Biobank, Million Records Project, and the All of Us research program will bring vast amounts of multi-omics datasets from millions of individuals. Consequently, there is a tremendous need for scalable methods that can integrate different layers of multi-omics datasets across millions of individuals from different backgrounds. These methods would produce valuable insights into human diseases and pave the way towards precision medicine. My research program is devoted to utilizing these multi-omics datasets cost effectively by developing open-source innovative and integrative computational resources. My lab has been successful in developing open source integrative computational methods to integrate such datasets to infer gene regulatory interactions and modules and to predict disease drivers. In the next five years, we aim to extend our recent and ongoing work to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient- specific drug response. We will integrate various types of heterogenous multi-omics datasets to build integrative and scalable computational tools. The computational tools we develop through this research will enable us to elucidate the genetic and epigenetic architecture of regulatory interactions and drug response and discover novel disease associated genes. Our tools will be applicable for any disease type and will enable researchers to leverage publicly available multi- omics datasets to their full extent and pave the road towards precision medicine. Through this research program, I will create research opportunities for graduate and undergraduate students particularly those from under-represented groups.
As the high-throughput techniques have become more cost-effective and prevalent, an unprecedented amount of biological, clinical, and environmental data have been generated. Computational methods that perform secondary analysis of these publicly available datasets can pave the road towards precision medicine, which would then enable more effective treatments to patients and reduced health care costs. In this study, we aim to develop open source computational tools that integrate these datasets to infer gene regulatory interactions, and predict disease associated genes and patient-specific drug response.
