学 术 讲 座

主题：Integrated and systematic views of regulatory

          DNA motif identification and analyses

主讲人：Qin Ma （美国南达科塔州立大学助理教授）

时间：2017年05月22日 （周一）下午 3:30-5:00

地点：公司津南校区计算机与控制工程学院（信息东楼）328室

Abstract：Transcription factors are proteins that bind to specific DNA sequences and play important roles in controlling the expression levels of their target genes. Hence, prediction of transcription factor binding sites (TFBSs or motif for short) provides a solid foundation for inferring gene regulatory mechanisms and building regulatory networks for a genome. Motif identification and analyses are important and have been long-standing computational problems in bioinformatics. Substantial efforts have been made in this field during the past several decades. The bottleneck, however, is the lack of robust mathematical models, as well as efficient computational methods for motif prediction to make effective use of massive data sets in the public domain (e.g., ChIP-seq).

In this talk, I will present an integrated platform, DMINDA 2.0, which contains: (i) five motif prediction and analyses algorithms, including a de-novo genome-scale prediction algorithm a phylogenetic footprinting framework; (ii) 2,125 species with complete genomes to support the above five functions, covering animals, plants, and bacteria; and (iii) regulatory systems prediction and visualization.

Biography：马勤, 博士毕业于山东大学数学学院, 于美国佐治亚大学徐鹰老师实验室进行博士后研究。目前为美国南达科塔州立大学终身制助理教授, 组建了南达科塔州内首个生物信息学实验室,主要从事大规模生物组学数据的分析和数学模型，以及相应的生物信息软件开发。在国内外的重要期刊上发表论文45 篇（25篇第一作者或者通讯作者论文），包括Bioinformatics, Nucleic Acids Research, 和Briefings in Bioinformatics等国际著名学术期刊；应邀在国际生物信息学相关的会议上和国内外知名大学做学术报告30余次,研究成果被引用了近 450 次。为国际知名生物信息学期刊 《BMC Genomics》与《Mathematical Biosciences》编委，，并于2016年受邀加入美国 NSF 重点项目基金的评审委员会。