Research Overview

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.

Research Background

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.

Publications

  1. Tissue-specific and tissue-agnostic effects of genome sequence variation modulating blood pressure. Cell Rep. 2023 11 28; 42(11):113351. View Abstract
  2. Multi-population genome-wide association study implicates immune and non-immune factors in pediatric steroid-sensitive nephrotic syndrome. Nat Commun. 2023 04 29; 14(1):2481. View Abstract
  3. Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs. Nat Commun. 2023 04 19; 14(1):2229. View Abstract
  4. Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model. Proc Natl Acad Sci U S A. 2022 12 20; 119(51):e2212810119. View Abstract
  5. Sequence-based correction of barcode bias in massively parallel reporter assays. Genome Res. 2021 09; 31(9):1638-1645. View Abstract
  6. Analysis of putative cis-regulatory elements regulating blood pressure variation. Hum Mol Genet. 2020 07 21; 29(11):1922-1932. View Abstract
  7. Common risk variants in NPHS1 and TNFSF15 are associated with childhood steroid-sensitive nephrotic syndrome. Kidney Int. 2020 11; 98(5):1308-1322. View Abstract
  8. 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
  9. Human cardiac cis-regulatory elements, their cognate transcription factors, and regulatory DNA sequence variants. Genome Res. 2018 10; 28(10):1577-1588. View Abstract
  10. Design of a synthetic yeast genome. Science. 2017 03 10; 355(6329):1040-1044. View Abstract
  11. Enhancer Variants Synergistically Drive Dysfunction of a Gene Regulatory Network In Hirschsprung Disease. Cell. 2016 Oct 06; 167(2):355-368.e10. View Abstract
  12. LS-GKM: a new gkm-SVM for large-scale datasets. Bioinformatics. 2016 07 15; 32(14):2196-8. View Abstract
  13. A method to predict the impact of regulatory variants from DNA sequence. Nat Genet. 2015 Aug; 47(8):955-61. View Abstract
  14. A comparative encyclopedia of DNA elements in the mouse genome. Nature. 2014 Nov 20; 515(7527):355-64. View Abstract
  15. Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol. 2014 Jul; 10(7):e1003711. View Abstract
  16. Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011 Dec; 21(12):2167-80. View Abstract

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