Won Group – University of Copenhagen

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Won Group

We aim to further our understanding of the genome by integrating large-scale genomic datasets. We also develop computational methods to exploit multi-dimensional genomic/epigenomic landscapes to understand cell-type specific or spatio-temporal gene regulation. Using computational integrative approaches, we also study transcriptional mechanisms controlling cell differentiation and cell identify. Our area of research include studying gene regulations in adipocyte, liver, cancer cells and stem cell differentiation.

1. Develop computational tools to understand gene regulation. We aim to develop computational tools that can contribute to the study of biology. We previously developed Chromia & AWNFR to identify gene regulatory regions and annotate genomic regions using epigenomic data, Defiant to call differentially methylated regions (DMRs), CellBIC to cluster single cell RNAseq (scRNAseq) data. We also develop tools to understand gene regulatory networks using scRNAseq data and other omics data. (fig1, fig2)

2. Studying metabolic biology using functional genomic analysis. We study critical aspects of metabolic controls over gene expression by applying integrative analyses. Using computational and systematic approaches, We perform functional and comparative genomic study for brown and white adipose tissue. We developed a pipeline to study enhancer RNAs (eRNAs) using global run-on sequencing (GRO-seq) data and Hi-C data.

3. HIV transmission study. Partnering with Philadelphia Department of Public Health (PDPH) and Penn Center for AIDS research, won lab studies HIV transmission dynamics through network analysis. We use sequence (for phylogenetic tree), partner networks (obtained from PDPH) and social networks (from Twitter or other social networks) for comprehensive network reconstruction.

Selected Publications (**Corresponding author)

1. Kim YH, Marhon SA, Zhang Y, Steger DJ, Won KJ**, Lazar MA** (2018) Rev-erb Dynamically Modulates Chromatin Looping to Control Circadian Gene Transcription, Science, 16;359(6381):1274-1277. PMID:29439026

2. Condon ED, Tran PV, Lien YC, Schug J, Georgieff M, Simmons RA, Won KJ (2018) Defiant: (DMRs: Easy, Fast, Identification and ANnoTation) Identifies Differentially Methylated Regions from Iron-Deficient Rat Hippocampus, BMC Bioinformatics 19(1):31, PMID:29402210

3. Stanescu DE, Yu R, Won KJ**, Stoffers DA** (2017). Single cell transcriptomic profiling of mouse pancreatic progenitors. Physiol Genomics. 2017 Feb 1;49(2):105-114. PMID:28011883

4. Shin HJ, Kim H, Oh S, Lee JG, Kee M, Ko HJ, Kweon MN, Won KJ, Baek SH. (2016) AMPK-SKP2-CARM1 signaling cascade in transcriptional regulation of autophagy. Nature. 2016 Jun 15;534(7608):553-7

5. Lu F, Chen HS, Kossenkov AV, DeWispeleare K, Won KJ**, Lieberman PM**. (2016) EBNA2 Drives Formation of New Chromosome Binding Sites and Target Genes for B-Cell Master Regulatory Transcription Factors RBP-jκ and EBF1. PLoS Pathogen 12(1):e1005339. PMID: 26752713

6. Won KJ, Choi I, LeRoy G, Zee BM, Sidoli S, Gonzales-Cope M, Garcia BA (2015) Proteogenomics analysis reveals specific genomic orientations of distal regulatory regions composed by non-canonical histone variants. Epigenetics & Chromatin, 10;8:13. PMID: 25878728. PMCID: PMC4397702.

7. Harms MJ*, Lim HW*, Ho Y, Ishibashi J, Rajakumari S, Steger DJ, Lazar MA, Won KJ**, Seale P** (2015), Prdm16 controls chromatin architecture to determine a brown fat transcriptional program, Gene and Development, 29(3), 298-307. PMID: 24703692; PubMed Central PMCID: PMC4012340. (Co-corresponding).

8. Lim HW*, Uhlenhaut NH*, Rauch A, Weiner J, Hübner S, Hübner N, Won KJ, Lazar MA, Tuckermann J, Steger DJ. (2015) Genomic redistribution of GR monomers and dimers mediates transcriptional response to exogenous glucocorticoid in vivo. Genome Research, 25(6):836-44. PMID: 25957148. PMCID: PMC4448680.

9. Choi I , Kim R, Lim HW, Kaestner KH, Won KJ (2014), 5-hydroxymethylcytosine represses the activity of enhancers in embryonic stem cells: a new epigenetic signature for gene regulation, BMC Genomics, 15:670. PMID: 25106691. PMCID: PMC4133056.

10. Nguyen N, Vo A, Won KJ (2014). A wavelet-based method to exploit epigenomic language in the regulatory regions, Bioinformatics, 30:7, 179-190. PMID: 24096080. PMC3983404

11. Step SE*, Lim HW*, Marinis JM, Prokesch A, Steger DJ, You SH, Won KJ, Lazar MA (2014), Antidiabetic rosiglitazone remodels the adipocyte transcriptome by redistributing transcription to PPARγ-driven enhancers (2014) Genes & Development, 28:9, 1018-1028, PMID: 24788520.

12. Won KJ, Zhang X, Wang T, Raha D, Snyder M, Ren B, Wang W. (2013). Comparative annotation of functional regions in the human genome using epigenomic data, Nucleic Acid Research, 41(8), 4423-4432. PMID: 23482391. PMCID: PMC3632130