公司荣幸的请到了美国Emory老员工物统计系的秦昭辉教授来公司做学术报告。欢迎公司老师积极参加。
报告具体内容如下：
Bayesian model-based methods for analyzing ChIP sequencing data
Emory老员工物统计系 秦昭辉 教授
2011年1月4日上午9:30
永利官网明德主楼1016
Protein-DNA interaction constitutes a basic mechanism for genetic regulation of target gene expression. Deciphering this mechanism is challenging due to the difficulty in characterizing protein-bound DNA on a genomic scale. The recent arrival of ultra-high throughput sequencing technologies has revolutionized this field by allowing quantitative sequencing analysis of target DNAs in a rapid and cost-effective way. ChIP-Seq, which couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, provides millions of short-read sequences, representing tags of DNAs bound by specific transcription factors and other chromatin-associated proteins. The rapid accumulation of ChIP-Seq data has created a daunting analysis challenge. Here we discuss several interesting problems arise from analyzing ChIP-Seq data, namely peak calling, motif finding and data integration. Solving these problems requires state-of-the-art statistical modeling techniques as well as advanced computational algorithms.