Research

(Keyword Cloud)

We are interested in the broad area of bioinformatics, systems medicine, personalized medicine, and translational science. We develop independent research projects to study important biological problems. We also actively collaborate with investigators university-wide, nationally and internationally. Our current research topics are:

  • Next generation sequencing (NGS) data analysis
    Since 2009, our lab quickly moved to develop pipelines for NGS data analysis. We are the first one being able to analyze the whole genome sequencing data in Vanderbilt. We currently analyzed data generated by Illumina (RNA-Seq, WGS, Exomes), SOLiD, and 454 platforms and for diseases such as melanoma, sever types of lung cancer including drug resistance, schizophrenia, bipolar disorder, Pulmonary Arterial Hypertension (PAH), Idiopathic Pulmonary Fibrosis (IPF), breast cancer, and colorectal cancer, among many others. Current projects also include analysis of the RNA-Seq data in yeast and ChIP-Seq. Our collaborators are both within the Vanderbilt University Medical Center, Vanderbilt-Ingram Cancer Center, and outside (Yale University, MGH, Harvard, Georgetown University, University of Southern California, Virginia Commonwealth University, Chinese Academy of Sciences, etc.). We are also actively developing our NGS analysis pipelines, The NGS Catalog database, and other computational tools. A list of recent NGS publications or manuscripts can be accessed here.
  • Disease gene network/pathway analysis
    Molecular mechanisms of complex diseases are much more complicated than previous thought. Rather than the traditional approaches to studying single genes or locus, we investigate gene networks/pathways in complex diseases especially in psychiatric disorders and cancer. This includes protein-protein interaction (PPI) networks, biological pathways, pathway crosstalk, and molecular network analysis by combing PPIs and pathways, and regulatory networks (e.g. microRNA and transcription factor mediated networks).
  • Translational Bioinformatics
    Integrative analyses of pharmacogenomics datasets and electronic medical records (EMRs), network pharmacology, application of systems biology approaches to identify biomarkers for drug response and side effects.
  • Cancer bioinformatics
    Noncoding RNA genes in cancer; gene fusion using Next-Gen sequencing technology; microarray gene expression profiling (GEP) in different cancer/tumors; GWAS analysis in cancer studies; methylation pattern in cancer cell lines or tumor tissues; and investigation of methylation status in tissue-specific genes.
  • Data integration and mining
    This involves data collection and curation, data management, integration, gene ranking, and gene feature analysis. We are specifically interested in integration of multi-dimensional data for specific disease. This includes (1) various types of genetic data such as linkage scan, gene expression, association studies, and genome-wide association studies (GWAS), (2) biological data such as Gene Ontology annotations, protein-protein interactions, and pathways, and (3) phenotypes/traits. We also develop computational tools and databases.
  • Bioinformatics in psychiatric genetics
    Study of gene structures of schizophrenia susceptibility genes (e.g. DTNBP1); design and establishment of a comprehensive bioinformatics system to efficiently manage various types of data; and development of computational tools for lab experimental support (e.g. selection of candidate genes or genetic markers).
  • Comparative and evolutionary genomics
    Investigation and comparison of patterns of single nucleotide polymorphisms (SNPs) among mammalian genomes and in their specific genomic regions; comparative genomic analysis of sequences especially CpG island sequences.
  • Human population genetics
    Patterns of genetic variation, genotype, and haplotype within and between subpopulations; and evolutionary history of human population.

∗ Students and post-doctoral fellows are always encouraged to develop their own projects in the broad area of bioinformatics.