Bayesian inference of relative fitness on high-throughput pooled competition assays.

ABSTRACT: The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known par

SUBMITTER: Razo-Mejia M 

PROVIDER: S-EPMC10971673 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature