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Computational Prediction of DNA Motifs and Regulons in Proka
时间:2016-11-22 点击:
报告时间:20161125日(周五)下午2:30-5:30
报告地点:北校区主楼四区107
报告人:山东大学 刘炳强 博士、副教授


Title: Computational Prediction of DNA Motifs and Regulons in Prokaryotic genomes
 
Abstract: Regulons are basic units of the response system in a bacterial cell, each consisting of transcriptionally co-regulated operons. The computational prediction of novel regulons usually based on de novo motif finding. Here we designed a phylogenetic footprinting motif finding framework based on a novel promoter pruning method, through motif voting by complementary tools. The new method can eliminate the effect of random noise, thus reduce the false positives. Further, we developed a new computational framework for regulons elucidation based on a graphic model, which can make the regulon prediction problem becomes substantially more solvable. We integrated these methods into our motif identification and analysis server DMINDA, through which the users can efficiently identify and analysis cis-regulatory motifs and do ab initio regulon prediction for prokaryotic genomes.
 
个人简介:
1999-2003 山东大学数学学院基础数学本科
2003-2006 山东大学数学学院运筹学与控制论 硕士
2006-2010 山东大学数学学院生物信息学 博士
2007.1-2010.1 美国佐治亚大学 联合培养博士
2010年留校工作至今。
研究方向:基因转录调控相关的计算生物学算法设计等。