Cancer Institute

  • Bioinformatics Core Research Interests

    We are interested in the broad area of bioinformatics and computational genomics. We develop and apply bioinformatics and statistical genetics methods in genomics. We are particularly focused on functional genomics problems involving high-dimensional data sets, such as that obtained from large-scale genotyping, gene, protein, metabolomic, lipidomic expression profiling and other "omics" data, next generation sequencing data and integration of these data with other biological and clinical data in cancer and other common human disease. We collaborate with other investigators in the Cancer institute, university-wide, in the region, nationally and internationally.

    The overarching goal of our research is to utilize multiple sources of high-throughput genetic, sequence and genomics data to understand biological regulatory networks and the biological machinery underlying genomic variation, gene function and regulation in cancer and other common human diseases. Our current research topics include:

    Cancer biomarker discovery and target validation

    Decomposing gene expression variation in cancer and other common diseases. Identification of molecular portraits of breast cancer, identification of drug targets, identification of miRNAs, functional characterization of identified genes and miRNAs, Elucidating the effects of SNPs, mutations on cis and trans regulatory elements.

    Pathway prediction and modeling gene regulatory networks

    Inferring causal regulatory networks from studies involving high-throughput gene expression and large-scale genotype data. This includes gene-gene, protein-protein interactions, biological pathways, pathway crosstalk, molecular network analysis, and regulatory networks.

    Population genomics and epigenomics of cancer health disparities

    Analysis of patterns of genomic and epigenomic variation, genotype, haplotype, within and between populations to understand the genetic and epigenetic susceptibility landscape of cancer and other common human diseases. We are specifically working on understanding the triangular relationship between breast cancer, obesity and diabetes.

    Data mining and integration in cancer and other common human diseases

    Mining and integration of large-scale "omics" data on cancer and other common human diseases. This includes genome-wide association, sequence, genomics, phenotypic and other biological data.

    Neurodegenerative diseases

    Our key focus is identification and functional characterization of genetic markers and epigenetic factors associated with neurodegenerative diseases such as aging, Alzheimer's, Parkinson, autism, schizophrenia and bipolar. We use integrative bioinformatics approaches to analyze genomic, epigenomic and imaging data to identify molecular signatures and biological pathways involved in neurodegenerative diseases, and to elucidate the effects of SNPs and CNVs on gene and protein function.