Computational Epigenomics

We focus on computational challenges in processing and interpreting cancer epigenomic data. 

 

Mingxiang Teng, PhD

 

Assistant Member 
Department of Biostatistics and Bioinformatics 
Moffitt Cancer Center
12902 Magnolia Drive
Tampa, FL 33612
mingxiang dot teng at moffitt dot org

 


Selected Publications

  • Liu X, Zhao B, Shaw TI, Fridley BL, Duckett DR, Tan AC, Teng M*. Summarizing internal dynamics boosts differential analysis and functional interpretation of super enhancersNucleic Acids Research. 2022;gkac141.

  • Teng M*, Du D, Chen D, Irizarry RA. Characterizing batch effects and binding site-specific variability in ChIP-seq dataNAR Genomics and Bioinformatics. 2021;3(4):lqab098.

  • Wang C, Zhang L, Ke L, …, Kaye KM, Teng M*, Zhao B*. Primary effusion lymphoma enhancer connectome links super-enhancers to dependency factors. Nature Communications. 2020;11(1):6318.

  • Guo R†, Jiang C†, Zhang Y, …, Doench JG, Teng M*, Gewurz BE*. MYC controls the Epstein-Barr Virus lytic switch. Molecular Cell. 2020;78(4):653-669.e8.

  • Teng M, Irizarry RA. Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data. Genome Research. 2017;27(11):1930-1938.

  • Teng M, Love MI, Davis CA, …, Wei X, Zhan L, Irizarry RA. A benchmark for RNA-seq quantification pipelines. Genome Biology. 2016;17:74.