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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 starting in May 2025 in the research group of Dr. Ivan Ovcharenko at the National Institutes of Health (NIH). Our current research focuses on AI/ML approaches to study epigenetic and sequence-based mechanisms of gene regulation. We are developing AI, ML, and statistical methods to decipher the regulatory landscape of the human genome. By integrating AI-based enhancer modeling with GWAS data and extensive experimental enhancer characterization, our approaches aim to uncover transcriptional mechanisms underlying cell-type-specific gene regulatory signals and to accurately identify disease-causal noncoding mutations in the human genome.Candidates with a PhD in Computational Biology, AI/ML, Computer Science, Population Genetics, Bioinformatics, or a related field, and fewer than 5 years of prior postdoctoral experience, are encouraged to apply. Advanced programming skills and experience with genome data analysis are desirable. Some of our sample publications: S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 120 (35) e2206612120 (2023), [PDF] D. Huang et al., The contribution of silencer variants to human diseases, Genome Biology, Jul 8;25(1):184 (2024), [PDF] S. Li et al., De novo human brain enhancers created by single-nucleotide mutations, Science Advances, 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 [email protected]. |
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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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