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Learn to apply the Pyro probabilistic programming language to statistical genomics problems in this 30-minute conference talk. Explore how Pyro's flexible framework enables sophisticated Bayesian modeling approaches for genomic data analysis, with practical applications demonstrated through cutting-edge research examples. Discover methodologies for leveraging functional annotations to map rare variants associated with Alzheimer's disease using the gruyere framework, and examine innovative approaches to Cas13d-mediated isoform-specific RNA knockdown through unified computational and experimental toolboxes. Gain insights into how probabilistic programming can address complex statistical challenges in genomics, from variant mapping to RNA interference studies, while understanding the theoretical foundations and practical implementation strategies that make Pyro particularly well-suited for genomic applications.
