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On the optimal design of metabolic RNA labeling experiments

Fig 2

The key characteristics of metabolic RNA labeling experiments.

A: The diagonal term of the Fisher information matrix (FIM) , as a function of the ratio of labeling time t to the characteristic time of degradation τ = 1/δ for the case of SLAMseq experiment. Read counts follow the Poisson distribution, the expression level is μ = 1 and the degradation rate is δ = 1. B: 95% confidence interval (CI) relative width of the degradation rates for different sets of time points included in the simulation of the SLAMseq experiment. We simulated counts for a range of rates δ and assumed for simplicity that normalization factors are perfectly known but not the rates and expression levels. Smoothed data from 10 simulation runs is shown. C: Relative standard deviation () of the MLE for δ as a function of measurement time at different values of the overdispersion parameter k. With increasing overdispersion, the profile of the dependency flattens. However, near the optimal time point, variance of the estimation is more sensitive to time of labeling, which complicates the optimal design choice for different δ ranges. Expression level is fixed to μ = 100 reads in this example, the degradation rate is assumed to be δ = 1. The FIM is calculated for n = 1. D: Relative standard deviation () for a model with overdispersion (k = 100, solid line) or with no overdispersion (k → ∞, dashed line). The degradation rate is δ = 1, the labeling time is t = 1. The FIM is calculated for n = 1.

doi: https://doi.org/10.1371/journal.pcbi.1007252.g002