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  Ivan Ovcharenko Research Group




Research Interests

  • Sequence composition of tissue-specific gene regulatory elements
  • Noncoding sequence evolution and selection acting on regulatory elements
  • Identification of sequence signatures of enhancers and repressors using machine learning
  • Heart and brain gene regulatory mechanisms
  • Disease associated noncoding mutations




Gene Regulation: From Sequence to Function, to Disease.

The research of the Ovcharenko research group focuses on deciphering semantics and studying evolution of the gene regulatory code in eukaryotes.

With less than 2% of the human genome sequence being coding, the search for noncoding functional DNA is a guileless treasure hunt. We currently lack fundamental understanding of the genomic language that governs the temporal and spatial dynamics of gene expression regulation, native to every cell of a living creature. In an effort to breach the gap between the modern success in genome sequencing and sequencing data interpretation, we are developing pattern recognition methods to functionally characterize noncoding DNA. Our ultimate goal is to be able to use these methods in translating the noncoding genome sequence into function.

Understanding the gene regulatory landscape of the human genome will open doors for studies of population variation in noncoding functional elements, promoting identification of disease-causing mutations residing outside of genes. As mutations in gene regulatory regions might be mainly linked to an increased susceptibility to disease, not necessarily resulting in a disease phenotype, our research has a potential for mapping key regulatory elements in the vicinity of disease-linked genes. Availability of computationally defined datasets of human regulatory elements tailored to specific common diseases (including heart disease, obesity, diabetes, and cancer, for example) will permit designing novel disease susceptibility measurement methods, expressly targeting selected elements.

We utilize comparative genomics, Bayesian statistics, multiple sequence alignments, libraries of transcription factor binding sites, microarray gene expression data, sequence pattern recognition techniques, dynamic programming, population genetics, and transgenic animal experimentation (the latter through collaborations) -- all to infer the noncoding genome function through the analysis of sequence data and evolutionary trends. Our research relies on collaborative studies with several research and clinical groups within the NIH and from other research Universities and Institutions.



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Last modified: April 22, 2009