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 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 downstream analysis of scATAC-seq is still behind 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 on Biology from Nankai University, China in 2018. Through the iGEM competetion and my final project (Investigation on candidate regulatory proteins of the Hippo Signaling Pathway), I gradually formed interest on genomics with the application of mathematical modelling and statistics, which brought me to postgraduate studies in bioinformatics.