报告题目：A novel computational framework for genome-scale alternative transcription units prediction
报告时间：12月22日上午10:30开始
报告地点：3003必赢官网计算机楼313会议室
报告人：刘丙强 教授
报告摘要：
Alternative transcription units (ATUs) are dynamically encoded under different conditions or environmental stimuli in bacterial genomes, and genome-scale identification of ATUs is essential for understanding the transcriptomic architecture and functional genomics. However, it is unrealistic to identify all ATUs using experimental techniques, due to the complexity and dynamic nature of ATUs. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on RNA-Sequencing data. The framework utilizes a convex quadratic programming model (CQP) to seek an optimum expression combination of all of the to-be-identified ATUs by minimizing their squared error compared with the actual expression levels in genetic and intergenic regions. The predicted ATUs in E. coli reached a precision of 0.66/0.60 and a recall of 0.68/0.67 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from SMRT-Cappable-seq. Furthermore, the 5’-end genes of the predicted ATUs validated by experimental TSSs or TF binding sites were over 1.5 times greater than those of the other genes. In terms of GO and KEGG enrichment analyses, the gene pairs frequently encoded in the same ATUs were more functionally related than those that belong to two distinct ATUs. It is noteworthy that the information about the degradation rate of the mRNA transcripts was organically integrated to provide linear constraints of CQP and it have significantly improved the prediction performance in the above evaluations. We believe that the ATUs identified by SeqATU can provide fundamental knowledge to guide the reconstruction of transcriptional regulatory networks in bacterial genomes.
报告人简介：
刘丙强，山东大学数学学院教授、博士生导师，系统与运筹学研究所所长，院长助理。所在学科为运筹学与控制论，研究方向为组合最优化与生物信息学。2003年毕业于山东大学数学学院基础数学专业，获学士学位。2010年毕业于山东大学数学学院运筹学与控制论专业，获博士学位。其间于2007年1月至2010年1月赴美国乔治亚大学联合培养，研究方向为生物信息学。2010年留校任教，2013年任山东大学数学学院副教授，2017年任教授。主要研究方向为利用图与组合优化的模型与理论针对生物信息学问题进行算法设计与数据分析，研究课题包括转录因子结合位点计算预测、表达数据分析、转录单元预测、调控网络构建与分析等。
