NLM Intramural Research Program | |
Research Group of Ivan Ovcharenko |
Research Projects | Publications | Collaborations | Resources | Group Members | Principal Investigator | Visiting us |
Group Members | |||
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Di Huang Staff Scientist, April 2015 - present Postdoctoral 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 and I. Ovcharenko The contribution of silencer variants to human diseases. Genome Biology, Jul 8;25(1):184 (2024) PDF D. Huang and I. Ovcharenko Enhancer-silencer transitions in the human genome. Genome Research, Mar;32(3):437-448 (2022) PDF 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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Sanjarbek Hudaiberdiev Staff Scientist, November 2024 - present Staff Bioinformatics Scientist at GRAIL from January 2024 to October 2024. Staff Scientist from February 2023 to December 2023. Postdoctoral Fellow from March 2018 to February 2023. S. Hudaiberdiev and I. Ovcharenko Functional characteristics and computational model of abundant hyperactive loci in the human genome. eLife, 13:RP95170 (2024) PDF S. Hudaiberdiev, D.L. Taylor, W. Song, N. Narisu, ... Ovcharenko I, Collins FS. Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits. PNAS, 120 (35) e2206612120 (2023) PDF C. Hill, S. Hudaiberdiev and I. Ovcharenko ChromDL: A Next-Generation Regulatory DNA Classifier. Bioinformatics, 39:i377-i385 (2023) PDF E.Z. Kvon, ... S. Hudaiberdiev, ... L.A. Pennacchio Comprehensive In Vivo Interrogation Reveals Phenotypic Impact of Human Enhancer Variants. Cell 180(6):1262-1271 (2020) PDF |
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Xiaoqin Huang Research Fellow, October 2024 - present Regulatory Genomics of Glaucoma and Drug Discovery. My research focuses on two main areas: first, identifying and prioritizing causal genes for retinal diseases, particularly glaucoma, by leveraging artificial intelligence to integrate multi-omics data. The ultimate goal is to enable early detection and develop gene therapies for glaucoma. Second, I apply deep learning techniques to drug discovery, integrating transcriptional responses to chemical perturbations with gene regulatory elements. This approach aims to identify novel therapeutics by screening gene-based treatments for various diseases. |
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Jaya Srivastava Postdoctoral Fellow, June 2022 - present Understanding the genetic control of chromatin states to infer genotype-molecular phenotype correlations. More than 90% of the phenotypic differences observed across mammals can be attributed to changes in regulatory regions of their genomes. My interests lie in identifying these differences within the human genome in comparison to closely related mammals and analyze the biological mechanisms that account for human-specific phenotypic features. To study this, I am repurposing deep learning models developed in-house that can capture nucleotide changes and predict altered phenotypic outcomes. The predictive power of these models can also be employed to identify changes that differentiate wild type from diseased state regulatory landscape. To this end, I am exploiting the models to identify causative mutations that lead to emergence of Polycystic Ovary Syndrome. J. Srivastava and I. Ovcharenko Regulatory plasticity of the human genome. bioRxiv, https://doi.org/10.1101/2024.11.13.623439 (2024) PDF |
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Gaetano Manzo Postdoctoral Fellow, February 2024 - present Comparative analysis of AI enhancers models. My current research focuses on developing and comparing deep learning models for predicting the effects of genetic variants on enhancer activity. I am exploring how emerging transformer architectures, known for their ability to capture long-range dependencies and parallel processing, perform in this domain compared to more established convolutional neural networks (CNNs), which have demonstrated success in genomic sequence analysis. By leveraging these advanced architectures, the goal is to improve the accuracy and interpretability of predicting variant impacts on enhancer regions, which play a critical role in gene expression and complex trait heritability. |
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