Ye Zheng
Regular Member
Assistant Professor
The University of Texas MD Anderson Cancer Center
Department of Bioinformatics and Computational Biology
Inherently drawn to problems at the interface of statistical, biological, and cancer studies, my research group goal is to investigate the genomic basis for gene regulation, which sheds light on the intrinsic features that drive the variations of cell functions and cancer treatment outcomes. Leveraging computational and experimental technologies, we work on pioneering statistical models and computational pipelines that integrate bulk and single-cell transcriptomics, epigenomics, proteomics, and three-dimensional (3D) chromatin structure to infer gene regulation in investigations of biomedical science.
- From the epigenomic perspective, we focus on the histone and RNA PolII landscape profiling on the formalin-fixed paraffin-embedded (FFPE) samples. This novel experimental technology, along with the customized computational pipeline, can maximize the use of TMC’s extensive FFPE collections. Hence, we provide a cost-effective and robust approach to generate critical data for cancer research and motivate new association and prediction models with clinical outcomes. 2. 3D genomics data can tell us which distal regulatory elements physically interact with target genes in the 3D space. Students can join the team in developing statistical models and software to denoise and integrate bulk and single-cell 3D genomics (e.g., scHi-C data). 3. Cell surface protein measurement (proteomics) can provide deeper and standardized single-cell cell-type annotations and status descriptions. Our nonparametric normalization strategy for CITE-seq and Cytomery data makes the proteomics integration across the study and platform possible. The unsupervised and automated cell-type annotations from this work accelerate scientific discoveries, especially in immunology. 4. Collaborating with biologists and clinicians, we have combined the insights from bulk and single-cell multi-omics to decipher key genotypic and phenotypic features that drive efficacy versus toxicity in CAR-T cell immunotherapy. Motivated by the collaboration work, novel genomic-disease association models are proposed, suitable for students who want to work at the junction of genomics and quantitative science. In summary, students can gain the expertise to solve biological and clinically important, and methodologically challenging problems by innovating cutting-edge statistical and computational models.
Education & Training
PhD - University of Wisconsin - 2019