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Ji-Ping Wang

Department Chair; Professor of Statistics and Data Science, Adjunct Professor of Molecular BioSciences, Faculty member of NSF-Simons Center for Quantitative Biology

Ph.D., 2003, Pennsylvania State University

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 specifically designed for the analysis of large-scale, high-dimensional genomic and genetic data. Some of recent working topics 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, prediction of DNA bendability and its relationship to chromosome functions and etc. My lab has developed a few software tools that have been frequently used by researchers, including SPECIES (for species number estimation,  CRAN), NuPoP (for nucleosome positioning prediction, bioconductor), DegNorm (for degradation normalization for RNA-seq, bioconductor), RiboDiPA (for differential pattern analysis for Ribo-seq data, GitHub, bioconductor), DNAcycP (for DNA cyclizability/bendability prediction, GitHub, Web server) and BoostMEC (for CRISPR-cas9 cleavage efficiency prediction, GitHub).

Recent Publications

Estimating the species richness by a Poisson-compound Gamma model, Biometrika, 2010, 97(3): 727-740

A base pair resolution map of nucleosome positions in yeast (with Brogaard, Xi and Widom), Nature, 2012, 486: 496–501

Insights into Nucleosome Organization in Mouse Embryonic Stem Cells through Chemical Mapping (with Voong et al), Cell, 2016, 167(6),1555-1570.e15, 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).