Probabilistic Models of Transcriptomic (Dys)regulation WHO: David Knowles, Postdoctoral Researcher, Stanford University WHERE: ECCR 265 WHEN: Thursday, Feb. 22 | 3:30-4:30 PM 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 (mRNA). Massively parallel sequencing of RNA (RNA-seq) has become a ubiquitous technology to assay the “transcriptome”: the collection of mRNA molecules expressed in a given tissue. However, significant computational and statistical challenges remain to translate the resulting noisy, confounded RNA-seq data into understanding of the biological system or disease state under consideration. In particular Knowles’ team, and others, have shown that these functional data can help assign molecular mechanism to the thousands of disease variants uncovered by Genome-Wide Association Studies (GWAS). Knowles will describe three vignettes where probabilistic models have helped them address such challenges: a generalized linear mixed model to detect gene-by-environment effects in a large observational RNA-seq cohort, a novel approach to quantifying splicing dysregulation in complex disease and a neural-network model that predicts splicing from DNA sequence, allowing improved interpretation of splicing disrupting mutations from genome sequencing studies. BIO: David Knowles is a postdoctoral researcher at Stanford University with Sylvia Plevritis (Center for Computational Systems Biology/Radiology) and Jonathan Pritchard (Genetics), having previously worked with Daphne Koller prior to her move to Coursera. He did his PhD with Zoubin Ghahramani in the Machine Learning group of the Cambridge University Engineering Department. Knowles was the Roger Needham Scholar at Wolfson College, funded by Microsoft Research. His undergraduate degree comprised two years of physics before switching to engineering to complete an MEng with Zoubin. He took the MSc Bioinformatics and Systems Biology at Imperial College in 2007/8.