I am a DPhil student in Interdisciplinary Bioscience doctoral training program under the supervision of Gerton Lunter. I am interested in applying computational approaches to find out the hidden information related to gene regulation from sequencing data. Specifically, my research focuses on improving the downstream statistical analysis strategies for single-cell ATAC-seq (scATAC-seq) data and using machine learning to understand the cell-specific effects of non-coding genomic variants.  

Single-cell sequencing has improved the resolution of various kind of sequencing to individual cell level. While the more widely-used scRNA-seq is limited to information within coding regions, scATAC-seq reveals cell-to-cell heterogeneity of DNA accessibility across the entire genome, which provides valuable data with which to study gene regulatory mechanisms at individual cell level. However, the current downstream analysis of scATAC-seq is far from satisfaction. We aim to generate an optimized and comprehensive pipeline for scATAC-seq analysis, extract cell-specific information from transcription profile, and develop machine learning models to predict cell-specific patterns of genome-wide DNA accessibility.

Before coming to Oxford, I completed my undergraduate in Biological Sciences from Nankai University, China in 2018. Through the iGEM project and my final-year project (Investigation on candidate regulatory proteins of the Hippo Signaling Pathway), I gradually formed interest in genomics with the application of mathematical modelling and statistics, which brought me to postgraduate studies in bioinformatics.