Anyone who would like to talk with the speaker, please feel free to let me know!
Speaker: Yi Xing, PhD
Professor, Microbiology, Immunology, & Molecular Genetics,UCLA
Professor (by courtesy), Statistics
Director, Bioinformatics Interdepartmental Ph.D. Program
Time : 10:00-11:30 am , Dec. 26（Tuesday）
Venue: Room 300, SIBS Main Building, Yueyang Road 320
Host： Prof. Li Yang
CAS-MPG Partner Institute for Computational Biology
Dr. Yi Xing is a Professor in the Department of Microbiology, Immunology, and Molecular Genetics at UCLA, and the director of UCLA’s Bioinformatics Interdepartmental Ph.D. Program. He received his B.S. in Molecular and Cellular Biology and B.E. in Computer Science and Technology from the University of Science and Technology of China (2001). He completed his Ph.D. training in Bioinformatics with Dr. Christopher Lee at UCLA (2001-2006), and his postdoctoral training with Drs. Wing Hung Wong and Matthew Scott at the Stanford University (2006-2007). Dr. Xing has an extensive publication record in bioinformatics, genomics, and RNA biology. His research has provided fundamental insights into the function, regulation, and evolution of post-transcriptional RNA processing in mammals. Today his group combines genomic, bioinformatic, molecular, and systems approaches to elucidate the variation and dynamics of RNA regulatory networks in development and disease. Dr. Xing has received prestigious young investigator research awards from the Sloan Foundation and the March of Dimes Foundation, and his research is supported by the NIH and other nonprofit organizations.
Elucidate transcriptome isoform complexity using massive RNA-seq data
Mammalian cells produce a large number of distinct mRNA and protein isoforms from individual gene loci via alternative processing and modifications of RNA. 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 using massive RNA-seq datasets.
All are welcome！