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NLM Intramural Research Program |
| Research Group of Ivan Ovcharenko | |
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Research Interests
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Open Postdoctoral Research Position A postdoctoral position is available in the research group of Dr. Ivan Ovcharenko at the National Institutes of Health (NIH) starting June, 2024. Our current research projects include computational studies of epigenetic and sequence-based mechanisms of gene regulation. We are developing computational approaches to predict disease-causal regulatory mutations and mechanisms of action. By combining large-scale AI models with a substantial body of experimental enhancer characterization, our machine learning approaches target transcriptional mechanisms of tissue-specific regulatory signals and accurately quantify the impact of mutations on the activity of regulatory elements.Candidates with a PhD in Computational Biology, Machine Learning, Population Genetics, or a related field and less than 5 years of prior postdoctoral experience are encouraged to apply. Desirable qualifications include advanced programming skills expertise developing AI models. Some of our sample publications: S. Li et al., De novo human brain enhancers created by single-nucleotide mutations, Science Advances, 2023 [PDF] S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 2023 [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. |
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Gene Regulation: From Sequence to Function, to Disease. 
The research of the Ovcharenko research group focuses on deciphering semantics and studying the 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 a 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 bridge the gap between modern success in genome sequencing and sequencing data interpretation, we are developing pattern recognition AI methods to functionally characterize noncoding DNA. Our ultimate goal is to use these methods to translate 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-causative 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 the 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 AI (including Deep Learning), comparative genomics, Bayesian statistics, multiple sequence alignments, libraries of transcription factor binding sites, gene expression data, 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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