Dongwon Lee’s research focuses on discovering the genomic basis of pediatric kidney diseases from the perspective of transcriptional regulation in a cell-type-resolved manner. Dongwon has developed several computational methods based on machine-learning techniques and epigenomics data to predict cis-regulatory elements (CREs) and CRE variants from their primary DNA sequences. He demonstrated that the CRE variants predicted by these methods significantly contribute to the heritability of human traits and diseases in a tissue-specific way. He will extend these methodologies and develop new methods to build a unified framework that can illuminate the transcriptional regulatory network.


Dongwon Lee received his PhD from the Department of Biomedical Engineering at the Johns Hopkins University in 2013 and completed postdoctoral research at the Center for Human Genetics and Genomics at NYU School of Medicine.


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  1. Sequence-based correction of barcode bias in massively parallel reporter assays. Genome Res. 2021 Sep; 31(9):1638-1645. View abstract
  2. Multiple SCN5A variant enhancers modulate its cardiac gene expression and the QT interval. Proc Natl Acad Sci U S A. 2019 05 28; 116(22):10636-10645. View abstract
  3. Human cardiac cis-regulatory elements, their cognate transcription factors, and regulatory DNA sequence variants. Genome Res. 2018 10; 28(10):1577-1588. View abstract
  4. Cardiomyocytes have mosaic patterns of protein expression. Cardiovasc Pathol. 2018 May - Jun; 34:50-57. View abstract
  5. Rare coding variants associated with blood pressure variation in 15?914 individuals of African ancestry. J Hypertens. 2017 07; 35(7):1381-1389. View abstract
  6. Testing the Ret and Sema3d genetic interaction in mouse enteric nervous system development. Hum Mol Genet. 2017 05 15; 26(10):1811-1820. View abstract
  7. Design of a synthetic yeast genome. Science. 2017 03 10; 355(6329):1040-1044. View abstract
  8. Enhancer Variants Synergistically Drive Dysfunction of a Gene Regulatory Network In Hirschsprung Disease. Cell. 2016 Oct 06; 167(2):355-368.e10. View abstract
  9. Rare coding TTN variants are associated with electrocardiographic QT interval in the general population. Sci Rep. 2016 06 20; 6:28356. View abstract
  10. gkmSVM: an R package for gapped-kmer SVM. Bioinformatics. 2016 07 15; 32(14):2205-7. View abstract
  11. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics. 2016 07 15; 32(14):2196-8. View abstract
  12. A method to predict the impact of regulatory variants from DNA sequence. Nat Genet. 2015 Aug; 47(8):955-61. View abstract
  13. A comparative encyclopedia of DNA elements in the mouse genome. Nature. 2014 Nov 20; 515(7527):355-64. View abstract
  14. Divergent functions of hematopoietic transcription factors in lineage priming and differentiation during erythro-megakaryopoiesis. Genome Res. 2014 Dec; 24(12):1932-44. View abstract
  15. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol. 2014 Jul; 10(7):e1003711. View abstract
  16. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets. Nucleic Acids Res. 2013 Jul; 41(Web Server issue):W544-56. View abstract
  17. Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes. Genome Res. 2012 Nov; 22(11):2290-301. View abstract
  18. Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011 Dec; 21(12):2167-80. View abstract