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学术报告: The power of being quantitative for understanding the dynamics of epigenetic regulations

发表时间:2013-03-12  |  阅读次数:46次  |  字体大小 [ ]

题目:The power of being quantitative for understanding the dynamics of epigenetic regulations

主讲人:邵振博士

        Department of Pediatric Oncology, Dana Farber Cancer Institute;Division of Hematology/Oncology, Children’s Hospital Boston;Harvard Medical School

时间:2013314日上午900

地点:复旦大学枫林校区明道楼二楼多功能厅

主办方:复旦大学上海医学院生物医学研究院

讲座内容简介

ChIP-Seq now is widely used to characterize the genome-wide patterns of epigenetic modifications and transcription factors’ bindings. Although comparison of ChIP-Seq data sets is critical for understanding cell type-dependent and cell state-specific binding, and thus the study of cell-specific gene regulation, few quantitative approaches have been developed.

Here first I will present a simple and effective method, MAnorm, for quantitative comparison of ChIP-Seq data sets. By applying MAnorm to ChIP-Seq data between different cell lines, we find the quantitative differences of H3K27ac, a histone mark of active promoters and enhancers, show strong correlation with both the changes in expression of target genes and the binding of cell type-specific regulators. Then, through three case studies, I’ll show this new model is really a power tool for

1.studying the stage-specific epigenetic regulations in human erythroid cells;

2. quantifying the association between genotypes and variations of epigenetic marks across different human individuals;

3. understanding the role of two critical histone demethylases in mouse embryonic stem cells.  

Besides, I’ll also present some evidences to support the differential roles of two ETS family genes in prostate cancer and the existence of a non-canonical form of PRC2 in primary tissues, as well as a sequence-based model for predicting PRC2-associated lincRNAs. Finally, as extension of current works, I’ll introduce some statistical tools and a web-based analysis platform, which we are developing right now, for quantitative comparison and integration of ChIP-seq data with other types of data.
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