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Deep Learning and Deep Sequencing for mRNA Design

Two decades past the human genome project, genomics is at an inflection point from an era of discovery to an era of engineering. My research journey has paralleled this transition from uncovering fundamental mechanisms of gene regulation to developing new therapeutic applications. I’ll describe how our lab combines machine learning with high-throughput experiments to understand and engineer key processes controlling genome output: mRNA translation and splicing. By building quantitative models of how sequence determines protein output, we’ve decoded the rules for designing therapeutic mRNAs. Our deep learning models of splicing regulation have enabled a novel CRISPR-based strategy to treat genetic diseases by engineering ‘poison exons.’ These advances showcase how mechanistic insights, coupled with modern computational and molecular tools, allow us to not just read but rewrite the genome’s regulatory code.

Speaker: Liana Lareau, UC Berkeley

Wednesday, 02/05/25

Contact:

Website: Click to Visit

Cost:

Free

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Stanley Hall

UC Berkeley
Room 106
Berkeley, CA 94720