Thanks to my diverse academic and industry experiences, I have a broad background in epigenomics, single-cell multi-omics, computational biology, gene regulation, population genetics and liquid biopsy. These skills and interests drive me to understand the interactions between genetic and epigenetic variations — and bridge the gaps between genotype and phenotype.
My PhD training included developing computational and experimental methods for the high throughput epigenomic assay, specifically in DNA methylation and DNA accessibility/nucleosome positioning. Following my postdoctoral training, where I focused on population epigenomics, I worked as a principal computational biologist at a liquid biopsy company.
Together with experimental biologists, I developed NOMe-seq, Methyl-HiC and single-cell Methyl-HiC to profile the multi-omics within the same assay and even in the same single cell. Our NOMe-seq technology was selected as one of the Top 10 Innovations in 2013 by Scientist Magazine. Our single-cell Methyl-HiC technology was highlighted by Nature Method as Method of the Year 2019 (single-cell multimodal omics).
Since joining Cincinnati Children’s in 2019, my lab has focused on developing and applying machine learning and high-throughput experimental methods to understand gene regulation and non-coding genetic variants.
I’ve received numerous honors and awards, including:
Postdoc Training: Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology, Broad Institute of MIT and Harvard, MA, 2014-2017.
PhD: USC Epigenome Center, University of Southern California, CA, 2009-2014.
BS: School of Life Sciences, Nanjing University, China, 2004-2008.
Epigenomic and gene regulation mechanism in cancer and other common complex diseases
Epigenomics; liquid biopsy; computational biology/bioinformatics; gene-regulation; cell-free DNA; exosomal-DNA; single-cell -omics
Human Genetics, Biomedical Informatics
Human fetal cerebellar cell atlas informs medulloblastoma origin and oncogenesis. Nature. 2022; 612:787-794.
CRAG: de novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing. Genome Medicine. 2022; 14.
NOMe-HiC: joint profiling of genetic variants, DNA methylation, chromatin accessibility, and 3D genome in the same DNA molecule. 2022.
At the dawn: cell-free DNA fragmentomics and gene regulation. British Journal of Cancer. 2022; 126:379-390.
FinaleDB: a browser and database of cell-free DNA fragmentation patterns. Computer Applications in the Biosciences. 2021; 37:2502-2503.
Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature. 2020; 583:699-710.
Perspectives on ENCODE. Nature. 2020; 583:693-698.
CRAG: De novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing. 2020.
Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA. BMC Cancer. 2019; 19.
Joint profiling of DNA methylation and chromatin architecture in single cells. Nature Methods. 2019; 16:991-993.