Probabilistic Models for RNA Splicing Analysis

Probabilistic Models for RNA Splicing Analysis

Explore a colloquium talk by David Knowles from Stanford University on the challenges and advancements in RNA splicing analysis. Delve into the complexities of translating RNA-seq data into meaningful biological insights, focusing on probabilistic models for quantifying alternative splicing and predicting splicing from DNA sequences. Learn about Knowles' innovative approaches to improving the interpretation of rare variants in exome and whole-genome sequencing studies. Gain insights into the application of statistical machine learning in functional genomics and its potential impact on understanding disease states and human development.