Elucidating Transcriptome Complexity using Massive RNA-Seq Data
(大数据时代的转录组分析)
邢毅博士
美国加州大学洛杉矶分校微生物、免疫学与分子遗传学系教授（终身职位）
邢毅教授在生物大数据，转录组分析，RNA可变剪切和转录后调控网络的生物信息学和基因组学研究作出了重大和原创性的工作，发展了一系列被广泛使用的计算生物学算法和工具，发表学术论文71篇，共被引用约5300次，H-index 36。发展的一系列基于高通量转录组测序的计算生物学和统计学算法现在被国内外科学家广泛使用，成为基于RNA测序技术的生物信息学工具的算法基础。曾获著名的斯隆基金会(Sloan Foundation)和10美分活动基金会(March of Dimes Foundation)的青年科学家奖。长期为国际高水平期刊审稿(Cell, Science, Nature系列，PNAS, 等等)。2013年起被美国国家卫生研究院聘为基因组学，计算生物学和技术研究领域基金评审小组的长期成员（任期6年）。2015起被中国科学院聘为海外评审专家。
The recent advent of the high-throughput RNA sequencing (RNA-seq) technology has provided a powerful tool for transcriptome-wide measurements of mRNA isoform complexity at an unprecedented resolution. By generating massive amounts of sequence reads from a given RNA sample, researchers can reveal the identity and quantify the abundance of mRNA isoforms across the entire transcriptome. Large consortium projects are generating RNA-seq data on tens of thousands of samples along with a wide variety of other genomic and phenotypic measurements. However, the enormous potential of these large, complex datasets cannot be fully realized without the development of methods for discovering patterns and generating biological insights from big transcriptome and genome data. In this talk, I will discuss our recent efforts in developing computational and statistical methods for elucidating transcriptome isoform complexity and post-transcriptional RNA regulatory networks using massive RNA-seq datasets.
