Current high-throughput genomic technologies, especially next-generation sequencing, have revolutionized biological research and provided unique opportunities to study broad and novel questions about the regulation of gene expression. With these technologies, there has been an exponential increase in the types and the amount of high-throughput experimental datasets pertaining to the dynamics of gene expression, including time-series gene expression data and genome-wide maps of nucleosome occupancy, epigenetic marks and transcription factor binding sites in cells and organisms under various experimental conditions. In my lab, we develop computational models to take advantage of existing datasets to study the dynamics and mechanisms of transcriptional gene regulation and propose testable hypotheses.
Functional epigenetic patterns and their dynamics
Epigenomic information vary between cell types, between individuals and between organisms. It also spans several timescales, ranging from short term processes, such as cell differentiation to long-term processes such as aging. Due to this diversity of biological responses involving epigenomic variation, comparative analysis of varying epigenome maps provide a wide range of applications and opportunities for knowledge discovery. We developed computational models to reveal epigenetic patterns and their impact on the gene expression and DNA functionality [Bioinformatics 2010; NAR 2011]. Future interests of the lab includes exploring organism and cell-type specificity of epigenetic marks through comparative analyses of multiple epigenome maps. In addition, our lab is interested in studying the timing and changes in epigenetic patterns during development, aging, and diseases.
Regulatory networks in light of epigenetic data
Transcription factors, gene promoters, epigenetic marks, chromatin-modifying proteins, and nucleosomes together form extremely complex and dynamic regulatory systems. Although recent studies identified basic principles behind transcriptional regulation along with the individual role of many regulatory elements in regulation, we still lack a global understanding of genetic and epigenetic regulatory networks. Large collections of diverse regulatory relationships can be inferred from different high-throughput measurements and can be summarized in the form of networks. In the past, we developed network biology algorithms, particularly for the construction and analysis of protein-protein interaction networks [PKDD 2009; Bioinformatics 2008], regulatory networks [Bioinformatics 2009], and gene co-expression networks [Bioinformatics 2009]. More recently, we are interested in incorporating epigenetic data into regulatory networks to answer exciting questions regarding the combinatorial histone mark patterns, control mechanisms in transcriptional regulation that involve epigenetic signaling, and causal relationships between epigenetic marks and other regulatory elements.
Mining healthcare datasets to improve healthcare
Advances in data generation techniques combined with the decreasing genotyping and computing costs has led to an explosion of clinical genomic data in recent years. In addition, hospitals and clinics are increasingly utilizing electronic health record (EHR) systems for record keeping that form a vast repository of diseases and treatments that could be mined to drive discovery in disease genomics. A recent interest of my lab is to incorporate information in EHRs with next generation sequencing datasets to develop computational clinical decision support systems.
Principal Investigator: Duygu Ucar, Ph.D.
Webb AE, Pollina EA, Vierbuchen T, Urbn N, Ucar D, Leeman DS, Martynoga B, Sewak M, Rando TA, Guillemot F, Wernig M, Brunet A (2013) FOXO3 shares common targets with ASCL1 genome-wide and inhibits ASCL1-dependent neurogenesis. Cell Reports, Volume 4, Issue 3, 477491.
Rafalski VA, Ho PP, Brett JO, Ucar D, Dugas JC, Pollina EA, Chow LM, Ibrahim A, Baker SJ, Barres BA, Steinman L, Brunet A (2013) Expansion of oligodendrocyte progenitor cells following SIRT1 inactivation in the adult brain. Nature Cell Biology, 15, 614624.
Salih DAM, Rashid AJ, Colas D, Torre-Ubieta L, Zhu RP, Morgan AA, Santo EE, Ucar D, Devarajan K, Cole CJ, Madison DV, Shamloo M, Butte AJ, Bonni A, Josselyn SA, Brunet A (2012) FoxO6 regulates memory consolidation and synaptic function. Genes Development, 26(24): 27802801.
Greer EL, Maures TJ, Ucar D, Hauswirth AG, Mancini E, Lim JP, Benayoun B, Shi Y, Brunet A (2011) Transgenerational Epigenetic Inheritance of Longevity in C. elegans. Nature, 479: 365371.
Ucar D, Hu Q, Tan K (2011) Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering. Nucleic Acids Research, 39(10):4063-4075.
Firpi H, Ucar D, Tan K (2010) Discovering Regulatory DNA Elements Using Chromatin Signatures and Artificial Neural Network. Bioinformatics, 26(13):1579-1586.
Satuluri V, Parthasarathy S, Ucar D (2010) Markov Clustering of Protein Interaction Networks with Improved Balance and Scalability. ACM Intl Conference on Bioinformatics and Computational Biology, ACM-BCB.
Ucar D, Altiparmak F, Ferhatosmanoglu H, Parthasarathy S (2009) Mutual Information Based Extrinsic Similarity for Microarray Analysis. International Conference on Bioinformatics and Computational Biology, BiCoB.
Ucar D, Beyer A, Parthasarathy S, Workman CT (2009) Predicting functionality of protein-DNA interactions by integrating diverse evidence. Bioinformatics, 25(12): 137-144, ISMB 2009 Conference Proceedings.
Asur S, Parthasarathy S, Ucar D (2007) An Event-based Framework for Characterizing the Evolutionary Behavior of Interaction Graphs. The 13th Int’l Conference on Knowledge Discovery and Data Mining, SIGKDD, Best Applications Paper Award.
Ucar D, Neuhaus I, Ross-MacDonald P, Tilford C, Parthasarathy S, Siemers N, Ji R-R (2007) Construction of a Reference Gene Association Network from Multiple Profiling Data: Application to HIV Data Analysis. Bioinformatics, 23(20): 2716-2724.
Ucar D, Asur S, Catalyurek U, Parthasarathy S (2006) Functional Modularity in Protein-Protein Interactions Graphs Using Hub-induced Subgraphs. The 17th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD.