[The Svedberg seminar] Extracting meaning from high-throughput functional genomics data with Bayesian statistics
May 12, 2025 @ 15:15 – 16:15 CEST
High-throughput functional genomics technologies have transformed infection biology into an increasingly data-driven science, yet extracting meaningful insights from complex experiments remains challenging. Bayesian hierarchical modeling addresses these challenges by providing a framework to reason about the underlying data generating process. Here, I illustrate the power of this approach through two case studies. First, Bayesian modeling of RNA decay dynamics in Salmonella enterica identified confounding factors significantly biasing previous global RNA half-life estimates and revealed novel functions of bacterial RNA-binding proteins via transcriptome-wide differential stability analysis. Second, we designed and analyzed a genome-wide transposon insertion screen for Shigella flexneri in a realistic human organoid infection model, accounting for population bottlenecks and uncovering an unexpected role for bacterial tRNA modification enzymes in regulating virulence. I will end with an outlook on scaling our approach to very large datasets in the context of bacterial single-cell RNA-seq.
Lars Barquist, Assistant Professor University of Toronto, Canada
Host: Maria Letizia Di Martino ml.dimartino@imbim.uu.se and Mikael Sellin mikael.sellin@imbim.uu.se
Bio
Lars received his PhD from Cambridge University for work on high-throughput and computational methods for studying pathogen evolution at the Wellcome Sanger Institute. After an Alexander von Humboldt postdoctoral fellowship, he went on to start his own research group in 2018 at the Helmholtz Institute for RNA-based Infection Research in Würzburg, Germany before recently moving to the University of Toronto. His work spans a broad range of topics at the interface of infection and computational biology, ranging from large-scale comparative pathogen genomics and genotype-to-phenotype inference to detailed molecular studies of regulatory mechanisms and translational applications in antibiotic development.
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