There are three key components to this research. First and foremost, we are targeting the DNA sequence encryption of gene regulatory elements and are working on developing computational algorithms to decipher it. Using the regulation of heart genes as a testbed, we are starting with addressing the human gene regulatory code in a manageable set of cell types while gearing towards deciphering the component of cardiovascular disorders and disease susceptibility that is not associated with mutations in genes, but is associated with mutations in elements regulating the expression of these genes. If successful, this project component can advance and complement the ongoing diagnostic, therapeutic, and drug development efforts to confront the heart disease the leading cause of death in the United States. Collaboration with the experimental lab of Prof. Nobrega from the University of Chicago comes as a critical part of this research aim, allowing rapid and effective in vivo testing of computationally predicted heart regulatory elements, using zebrafish as an animal model for transgene expression. We are pursuing a whole-genome characterization of gene regulatory pathways and enhancer elements partaking in heart development, attempting to generate a complete map of heart regulatory elements in the human genome for follow up diagnostic and drug discovery studies. The second project component addresses the evolutionary history and population variation in gene regulatory elements. Mutations in gene regulatory elements played a notable, if not major, role in the hominoid evolution. Computational characterization of evolutionary changes in gene regulatory elements promises to elucidate the gene regulatory component of the evolution of the human lineage, identifying elements under positive selection that might have had the most profound impact on the phenotypic speciation. It will also aid in determining sequence variation within human populations with phenotypic and disease-susceptibility functions. Finally, we are delineating the evolutionary interplay between the divergence of transcription factors regulating the level of gene expression and the divergence of their cognitive bindings sites. We are characterizing the genomic rearrangement of gene regulatory elements in different vertebrate lineages and developing novel computational methods to align noncoding DNA sequences using transcription factor binding site specificities instead of primal DNA sequences. In addition to the previously described collaboration with the group of Dr. Nobrega, we are also working closely with several other intramural and extramural research groups. These collaborations provide not only necessary, but also critically important means for the experimental validation of computational predictions in vivo. They facilitate addressing gene regulation and development pathways specific to different organs and tissue types, studying both the enhancement and repression of gene expression, using different model organisms (frog, mouse, and zebrafish), and establishing partnerships with other computational biology groups actively involved into the genome sequence analysis.

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Elnitski, Laura; Ovcharenko, Ivan (2018) The hypothesis of ultraconserved enhancer dispensability overturned. Genome Biol 19:57
Li, Shan; Alvarez, Roberto Vera; Sharan, Roded et al. (2017) Quantifying deleterious effects of regulatory variants. Nucleic Acids Res 45:2307-2317
Vera Alvarez, Roberto; Li, Shan; Landsman, David et al. (2017) SNPDelScore: combining multiple methods to score deleterious effects of noncoding mutations in the human genome. Bioinformatics :
Huang, Di; Ovcharenko, Ivan (2017) Epigenetic and genetic alterations and their influence on gene regulation in chronic lymphocytic leukemia. BMC Genomics 18:236
Taher, Leila; Narlikar, Leelavati; Ovcharenko, Ivan (2015) Identification and computational analysis of gene regulatory elements. Cold Spring Harb Protoc 2015:pdb.top083642
Li, Shan; Ovcharenko, Ivan (2015) Human Enhancers Are Fragile and Prone to Deactivating Mutations. Mol Biol Evol 32:2161-80
Busser, Brian W; Haimovich, Julian; Huang, Di et al. (2015) Enhancer modeling uncovers transcriptional signatures of individual cardiac cell states in Drosophila. Nucleic Acids Res 43:1726-39
Huang, Di; Ovcharenko, Ivan (2015) Identifying causal regulatory SNPs in ChIP-seq enhancers. Nucleic Acids Res 43:225-36
Huang, Di; Ovcharenko, Ivan (2014) Genome-wide analysis of functional and evolutionary features of tele-enhancers. G3 (Bethesda) 4:579-93
Ahmad, Shaad M; Busser, Brian W; Huang, Di et al. (2014) Machine learning classification of cell-specific cardiac enhancers uncovers developmental subnetworks regulating progenitor cell division and cell fate specification. Development 141:878-88

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