Research Topics
Dr. Bin Zhu integrates statistics and genomics to understand the etiology of mutational signatures and reveal tumor heterogeneity. He develops statistical methods/tools and leads the scientific investigations to:
- Extract mutational signatures and characterize their etiologies across different study designs and platforms, and
- Uncover inter- and intra-tumor heterogeneity with translational and clinical implications.
Software
- REBET for a subregion-based burden test in rare-variant association studies.
- SubHMM to identify tumor subclones in next-generation sequencing studies.
- SKIT for a semiparametric kernel independence test when there are excess zeros.
- SUITOR to select the number of mutational signatures through cross-validation.
Biography
Dr. Zhu received a Ph.D. in biostatistics from the University of Michigan in 2010 and then spent two years as a postdoctoral associate at the Department of Statistical Science and the Center for Human Genetics at Duke University, North Carolina. He joined DCEG as a tenure-track investigator in the Biostatistics Branch in 2012, and was awarded NIH scientific tenure and promoted to senior investigator in 2023.
Selected Publications
- Zhu B, Xiao Y, Yeager M, Clifford G, Wentzensen N, Cullen M, Boland JF, Bass S, Steinberg MK, Raine-Bennett T, Lee D, Burk RD, Pinheiro M, Song L, Dean M, Nelson CW, Burdett L, Yu K, Roberson D, Lorey T, Franceschi S, Castle PE, Walker J, Zuna R, Schiffman M, Mirabello L. Mutations in the HPV16 genome induced by APOBEC3 are associated with viral clearance. Nat Commun. 2020;11(1):886.
- Zhu B, Poeta ML, Costantini M, Zhang T, Shi J, Sentinelli S, Zhao W, Pompeo V, Cardelli M, Alexandrov BS, Otlu B, Hua X, Jones K, Brodie S, Dabrowska ME, Toro JR, Yeager M, Wang M, Hicks B, Alexandrov LB, Brown KM, Wedge DC, Chanock S, Fazio VM, Gallucci M, Landi MT. The genomic and epigenomic evolutionary history of papillary renal cell carcinomas. Nat Commun. 2020;11(1):3096.
- Lee D, Zhu B. A semiparametric kernel independence test with application to mutational signatures. J Am Stat Assoc. 2021;116(536):1648-1661.
- Choo-Wosoba H, Albert PS, Zhu B. A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies. Biostatistics. 2022;23(1):69-82.
- Lee D, Wang D, Yang XR, Shi J, Landi MT, Zhu B. SUITOR: Selecting the number of mutational signatures through cross-validation. PLoS Comput Biol. 2022;18(4):e1009309.
Related Scientific Focus Areas
This page was last updated on Thursday, April 4, 2024