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Article Abstract
Common stochastic models of gene expression predict the gene-specific distribution of total mRNA per cell but lack subcellular resolution. Here, for a broad class of transcription initiation models, we derive an exact steady-state solution for the joint distribution of nuclear and cytoplasmic mRNA, and demonstrate that fitting this solution to spatially resolved mRNA data enhances parameter identifiability. By accounting for extrinsic noise, we use the model to precisely quantify bursty expression across thousands of human genes and link it to their biological functions.
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