DEPARTMENT OF STATISTICS AND DATA SCIENCE

Research Interests

My research interest centers around statistical applications in bioinformatics and computational biology. I am actively engaged in developing advanced statistical and machine learning methods and tools for the analysis of large-scale, high-dimensional genomic and genetic data. My recent projects include species number estimation, nucleosome positioning mapping and prediction, next-generation sequencing analysis, RNA-seq normalization, Ribo-seq pattern differentiation, CRISPR-Cas9 cleavage efficiency prediction, DNA bendability prediction and its relationship to chromosome functions, and cell type deconvolution in spatial transcriptomics data. To translate this research into practice, my lab has developed several software tools frequently utilized by researchers worldwide. These include SPECIES (CRAN) for species number estimation; NuPoP (bioconductor) for nucleosome positioning; DegNorm (bioconductor) for RNA-seq degradation normalization; RiboDiPA (GitHub/bioconductor) for Ribo-seq differential pattern analysis; DNAcycP and DNAcycP2 (GitHub/Web server/Bioconductor) for DNA cyclizability prediction; and BoostMEC (GitHub) for CRISPR-Cas9 cleavage efficiency prediction.

Recent Publications

Insights into Nucleosome Organization in Mouse Embryonic Stem Cells through Chemical Mapping (with Voong et al), Cell, 2016, 167(6), highlighted in Nature Reviews Molecular Cell Biology

DegNorm: normalization of generalized transcript degradation improves accuracy in RNA-seq analysis (with Xiong et al), Genome Biology, 2019, 20:75

DNAcycP: A Deep Learning Tool for DNA Cyclizability Prediction (with Li et al). Nucleic Acids Research, 2022, 50(6).

DNAcycP2: improved estimation of intrinsic DNA cyclizability through data augmentation (with Kendall et al). Nucleic Acids Research, 2025, 53(5).

BNLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data (with Chen et al). Bioinformatics, 2025,41(1).