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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
  • Disease causative noncoding mutations



Open Postdoctoral Research Position

A postdoctoral position is available in the research group of Dr. Ivan Ovcharenko at the National Institutes of Health (NIH). Our current research projects include computational studies of epigenetic and sequence-based mechanisms of gene regulation. We are developing computational approaches to decipher the regulatory landscape of the human genome. By combining next generation sequencing data with a large body of experimental enhancer characterization, our computational approaches target transcriptional mechanisms of tissue-specific regulatory signals and cell-type differences in gene regulation.

Candidates with a PhD in Computational Biology, Deep Learning, Population Genetics, Bioinformatics, or a related field and less than 5 years of prior postdoctoral experience are encouraged to apply. Advanced programming and genome data analysis skills are desirable.

Some of our sample publications:
S. Li et al., Stable enhancers are active in development, and fragile enhancers are associated with evolutionary adaptation, Genome Biology, 2019 [PDF]
D. Huang et al., Identification of human silencers by correlating cross-tissue epigenetic profiles and gene expression, Genome Research, 2019 [PDF]

This position is supported by the Intramural NIH Research Program and includes stable, multi-year funding, outstanding benefits and compensation. NIH is an Equal Opportunity Employer and encourages applications from women and minorities.

If interested, please email your CV and the names of 3 references to Ivan Ovcharenko at ovcharen@nih.gov.


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) 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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