Dr. David Knowles, Departments of Genetics and Radiology, Stanford University: Probabilistic models of transcriptomic dysregulation in human disease

Seminar | February 5 | 4-5 p.m. | Soda Hall, HP Auditorium 306

 Center for Computational Biology, Electrical Engineering and Computer Sciences (EECS)

Abstract:
Transcription, the fundamental cellular process by which DNA is copied to RNA, is tightly regulated in healthy human development but frequently dysregulated in disease. During or shortly after transcription, regions known as “introns” are spliced out of the RNA to produce mature “messenger” RNA. Massively parallel sequencing of RNA (RNA-seq) has become a ubiquitous technology in biology to assay the “transcriptome”: the collection of messenger RNA molecules expressed from the genes of an organism. However, significant computational and statistical challenges remain to translate the resulting noisy, confounded RNA-seq data into meaningful understanding of the biological system or disease state under consideration. I will describe three vignettes where probabilistic models have helped us address these such challenges: a generalized linear mixed model to detect gene-by-environment effects on gene expression in a large observational cohort, a novel approach to quantifying alternative splicing across different tissues/diseases and a neural-network model that predicts splicing from DNA sequence, allowing interpretation of splicing disrupting mutations from exome or whole-genome sequencing studies.

Bio:
Dr. Knowles studied Natural Sciences and Information Engineering at the University of Cambridge before obtaining an MSc in Bioinformatics and Systems Biology at Imperial College London. During his PhD studies in the Cambridge University Engineering Department Machine Learning Group under Zoubin Ghahramani he worked on Bayesian nonparametric models for factor analysis, hierarchical clusterings and network analysis, as well as on (stochastic) variational inference. He is currently a post-doctoral researcher at Stanford University with Sylvia Plevritis (Center for Computational Systems Biology/Radiology) and Jonathan Pritchard (Genetics/Biology) having previously worked with Daphne Koller (Computer Science). His work involves the application of statistical machine learning in functional genomics, with the occasional foray into imaging of biological systems. As of 2017 he is an O-1 Alien of Extraordinary Ability and has a T-shirt to prove it.

 ccbadmin@berkeley.edu