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NLM Intramural Research Program |
| Research Group of Ivan Ovcharenko | |
| Research Projects | Publications | Collaborations | Resources | Group Members | Principal Investigator | Visiting us |
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Group Members | |||
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Di Huang Staff Scientist April 2015 - present (Previously: Research Fellow, June 2010 - April 2015) Mapping biological pathways in tissue-specific enhancers My project is aimed at decoding regulatory pathways underlying the development of specific tissues. An enhancer set driving the development of a specific tissue (e.g., heart) is heterogeneous, regulating different pathways and controlling the development of different sub-tissues. We are developing a method to map enhancers into different pathways, and predict the pathway-specific activity of enhancers. This method will provide novel analytical tools for an in detail characterization of enhancers identified using next-generation sequencing technologies. D. Huang, H. Petrykowska, B. Miller, L. Elnitski and I. Ovcharenko Identification of human silencers by correlating cross-tissue epigenetic profiles and gene expression. Genome Research, 29(4):657-667 (2019) PDF D. Huang and I. Ovcharenko Epigenetic and genetic alterations and their influence on gene regulation in chronic lymphocytic leukemia. BMC Genomics, 18(1):236-245 (2017) Huang D and Ovcharenko I Identifying causal regulatory SNPs in ChIP-seq enhancers Nucleic Acids Research, 43(1):225-36 (2015) PDF Busser BW, Haimovich J, Huang D, Ovcharenko I, Michelson AM Integrative analysis of the zinc finger ncer modeling uncovers transcriptional signatures of individual cardiac cell states... Nucleic Acids Research, 43(3):1726-39 (2015) Busser BW*, Huang D*, Rogacki KR*, ... Bulyk ML, Ovcharenko I, Michelson AM Integrative analysis of the zinc finger transcription factor Lame duck in the Drosophila myogenic gene regulatory network PNAS, 109(50):20768-73 (2013) (* - co-first authors) PDF Ahmad SM*, Busser BW*, Huang D*, ... Bulyk ML, Ovcharenko I, Michelson AM Machine learning classification of cell-specific cardiac enhancers uncovers developmental subnetworks regulating progenitor cell division and cell fate specification Development, 141(4):878-88 (2014) (* - co-first authors) PDF Huang D and Ovcharenko I Genome-Wide Analysis of Functional and Evolutional Features of Tele-Enhancers G3: Genes, Genomes, Genetics, 4(4):579-93 (2014) PDF |
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Shan Li Postdoctoral Fellow April 2013 - present Killer Mutations of Transcription Factor Binding Sites My current project focuses on studying the underlying mechanism by which genetic variants affect transcriptional regulation and phenotype divergence. We identified the "killer mutations" (KMs) in human regulatory elements that have disruptive effects on transcription factor binding, and are currently studying the interplay between disease traits and KMs. We are also investigating how "killer mutations" alter transcription factor binding and gene expression during the course of evolution and the mode in which they co-evolve with each other. S. Li, E. Kvon, A. Visel, L. Pennacchio and I. Ovcharenko Stable enhancers are active in development, and fragile enhancers are associated with evolutionary adaptation. Genome Biology, 20(1):140-149 (2019) PDF R. Vera Alvarez, S. Li, D. Landsman and I. Ovcharenko SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genom. Bioinformatics, 34(2):289-291 (2017) S. Li, R. Vera Alvarez, R. Sharan, D. Landsman and I. Ovcharenko Quantifying deleterious effects of regulatory variants. Nucleic Acids Research, 45(5):2307-2317 (2017) S. Li and I. Ovcharenko Human Enhancers Are Fragile and Prone to Deactivating Mutations. Molecular Biology and Evolution, 32(8):2161-2180 (2015) PDF |
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Wei Song Postdoctoral Fellow January 2017 - present Heterogeneity of tissue-specific enhancers. My research interests focus on the heterogeneity of tissue-specific enhancers in mammalian genome, including identification of the primary enhancer and hierarchical structure in a multi-element regulatory program of the target gene, based on either the sequence signatures or the 3D chromatin contacts of the enhancer-gene networks. My current project includes clustering enhancers with a deep learning model to capture the sequence features for each class of enhancers, and applying this model to predicting novel active enhancers across the whole human genome. W. Song, R.Sharan and I. Ovcharenko The first enhancer in an enhancer chain safeguards subsequent enhancer-promoter contacts from a distance. Genome Biology, 20(1):197-207 (2019) PDF W. Song and I. Ovcharenko Dichotomy in redundant enhancers points to presence of initiators of gene regulation. BMC Genomics, 19(1):947-956 (2018) |
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Sanjarbek Hudaiberdiev Postdoctoral Fellow March 2018 - present Development of Deep Learning methods for enhancer identification. My research interests fall into the broad categories of computational genomics and applied Deep Learning. My main project involves the application of Deep Learning methods to identification and analysis of enhancers in the human genome. To that end, we developed a network comprising of Convolutional Layers and Long-Short Term Memory (LSTM) layers to study and detect the regions within enhancers that demonstrate an increased level of vulnerability to mutations. In addition, we are interested in broadening the scope of the deep-learning-based analysis to study complex diseases, with an aim of developing a tool that will help to identify causal SNPs from the GWAS studies where the data may be too noisy owing to LD blocks. |
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