Elizabeth Chun

Elizabeth Chun, PhD

Research Associate in Biomedical Informatics

Elizabeth (Hye-Jung) Chun received her MSc and PhD in Bioinformatics (under the supervisions of Drs. Steven Jones and Marco Marra, respectively) from the University of British Columbia, Canada. As part of the NIH NCI’s Therapeutically Applicable Research that Generates Effective Therapy (TARGET) initiative, Chun’s doctoral research focused on molecular characterization of pediatric malignant rhabdoid tumors (MRTs) through integrative analyses of whole genome, transcriptome, DNA methylation and histone modification profiles. Her work led to identification of novel molecular subgroups and revealed potential application of immunotherapy in this cancer type with very few mutations. She continued her research as a postdoctoral research fellow in Dr. Marra’s lab, to identify genetic and epigenetic alterations in MRTs using long-read sequencing data. Prior to her graduate training, she worked as a computational biologist at Canada’s Michael Smith Genome Sciences Centre, performing data analyses and coordinating research activities for The Cancer Genome Atlas (TCGA) and the Development of Highly active Anti-Leukemia stem cell Therapy at California Institute of Regenerative Medicine (CIRM-HALT) projects. She joined the Park Lab in June 2022. In the Park Lab, she is continuing her research in cancer genomics and contributing her work in the Common Fund Data Ecosystem project.

Analysis of 4,664 high-quality sequence-finished poplar full-length cDNA clones and their utility for the discovery of genes responding to insect feeding.
Authors: Ralph SG, Chun HJ, Cooper D, Kirkpatrick R, Kolosova N, Gunter L, Tuskan GA, Douglas CJ, Holt RA, Jones SJ, Marra MA, Bohlmann J.
BMC Genomics
View full abstract on Pubmed
Genomic convergence toward diploidy in Saccharomyces cerevisiae.
Authors: Gerstein AC, Chun HJ, Grant A, Otto SP.
PLoS Genet
View full abstract on Pubmed
Cancer genomics: From Bench to Personalized Medicine
Authors: Second-generation sequencing for cancer genome analysis
2014; 13-30.