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Drug Dependence
Oscar M. Harari, Ph.D.
Xin Zhou, Ph.D.
Sean D. Kristjansson, Ph.D.
Ruth Huang Miller, Ph.D.
Wei (Will) Yang, Ph.D.
Bo Zhang, Ph.D.
Sumithra Sankararaman
Ni Huang, Ph.D.

Bo Zhang, Ph.D. 


   Bo Zhang, Ph.D.     

Year in Program:                  Third Year

Primary Mentor:

   Ting Wang, PhD, Assistant Professor
   Department of Genetics
   Washington University School of Medicine


  Symposium Presentation 
  Video Link 
(viewable via Windows Media Player  
    click "open" to play)

Project Description

 (1) “M&M”, a statistical framework that combines MeDIP-seq and MRE-seq to analyze whole-genome DNA methylation profile:

DNA methylation plays a vital role in regulation of cellular processes including host defense of endogenous parasitic sequences, embryonic development, transcription, X chromosome inactivation, and genomic imprinting. DNA methylation profiling methods utilizing massively parallel sequencing are comprehensive, and as accuracy and affordability improves, will increasingly supplant microarrays for genome-scale methylation analyses. Among currently available technologies, generation of genome-wide data derived from methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) has become a major tool for epigenetic studies in health and disease. The computational analysis of such data, however, still falls short on accuracy, sensitivity, and speed. To complement MeDIP-seq which only interrogates methylated regions of the genome (whereas unmethylated regions are being inferred based on lack of data), our lab recently developed methylation-sensitive restriction enzyme sequencing (MRE-seq) which directly interrogate unmethylated regions.

In order to take advantage of both technologies, we are developing a new statistical framework which we call “M&M” to integrate both MeDIP and MRE data. This framework is composed of two major components. First, we develop a Conditional Random Field based model to learn absolute DNA methylation level at single CpG level; second, we develop a modified T-statistic to identify differentially methylated regions (DMR) between two samples (or two cohorts). We are applying these new methods to data obtained from the Roadmap Epigenomics project ( to investigate DNA methylation signatures of different cell types and of different individuals.

(2) Integrative epigenomics of cancer:

Endometrial cancers are the most common gynecologic cancers in the United States. Recent studies strongly suggest that epigenetics, in particular DNA methylation, is key in endometrial carcinogenesis. New sequencing technologies have revolutionized our ability to survey and compare cancer and normal genomes for changes in DNA methylation.

In collaboration with Dr. Paul Goodfellow, we obtained normal endometrial tissue (pooled normal cells) and endometrial cancer samples (six samples in total, three type I and three type II). Using our MeDIP-seq and MRE-seq technology, we generated seven complete DNA methylomes. These represent the first complete genome-wide DNA methylation profiles for endometrial tissue and cancer, which is an invaluable resource for the community.

We are applying our newly developed statistical framework (i.e. M&M) to these valuable endometrial cancer datasets. Initial analysis has already revealed key differences in DNA methylation between normal and cancer, and between type I and type II endometrial cancers. These will contribute importantly to the understanding of, and in the long term effective diagnosis and therapy for endometrial cancers.


Program Presentations

Zhang B. Comparative DNA methylome analysis of endometrial cancer. Oral presentation. Departments of Genetics annual Seminar, St. Louis, MO Sept 2012.

Zhang B. Comparative DNA methylome analysis of endometrioid adenocarcinoma, uterine papillary serous carcinoma, and normal endometrium cells. Poster presentation. 2013 Biology of genome, Cold spring harbor laboratory, New York, May 2013.

Zhang B. Discovery of functional DNA methylation difference using M&M algorithm. Oral presentation. Meeting of NIH Roadmap Epigenome Mapping Center, UCSF, San Francisco, CA, Jun 2013.

Zhang, B. (Oral Presentation): Discovery functional epigenetic signature by novel statistical framework. R25 Fellowship Symposium: Statistical and Computational Innovation in Addiction Genetics, Washington University School of Medicine, Saint Louis, MO, December 2013.


Program Publications

Xie M, Hong C, Zhang B, Lowdon RF, Xing X, Li D, Zhou X, Lee HJ, Maire CL, Ligon KL, Gascard P, Sigaroudinia M, Tlsty TD, Kadlecek T, Weiss A, O'Geen H, Farnham PJ, Madden PA, Mungall AJ, Tam A, Kamoh B, Cho S, Moore R, Hirst M, Marra MA, Costello JF, Wang T. DNA hypomethylation within specific transposable element families associates with tissue-specific enhancer landscape. Nat Genet. 2013 May; 45(7):836-41. Epub 2013 May 26. PMID: 23708189. PMC3695047.

Zhang B, Zhou Y, Lin N, Lowdon RF, Hong C, Nagarajan RP, Cheng JB, Li D, Stevens M, Lee HJ, Xing X, Zhou J, Sundaram V, Elliott G, Gu J, Gascard P, Sigaroudinia M, Tlsty TD, Kadlecek T, Weiss A, O'Geen H, Farnham PJ, Maire CL, Ligon KL, Madden PA, Tam A, Moore R, Hirst M, Marra MA, Zhang B, Costello JF, Wang T. Functional DNA methylation differences between tissues, cell types, and across individuals discovered using the M&M algorithm. Genome Res. 2013 Jun 26. [Epub ahead of print] PMID: 23804400.