Fig. 5: Potential determinants of the effective mRNA transport rate.
From: Kinetics of mRNA nuclear export regulate innate immune response gene expression
A Effective transport rates (k1’k2’/k2) show a significant negative correlation with gene length (Pearson’s correlation of −0.47 and −0.42 with associated P value of 3e-12 and 9e-10 for replicate 1 and 2, respectively). B Effective transport rates show a significant negative correlation with the number of introns (Pearson’s correlation of −0.54 and −0.53 with associated P value of 3e-16 and 8e-16 for replicate 1 and 2 respectively). C Effective transport rates show a significant positive correlation with splicing probability, averaged over all introns (Pearson’s correlation of 0.63 and 0.55 with associated P value <2.2e-16 and of 1e-13 for replicate 1 and 2, respectively). This correlation coefficient is higher than when only the most retained intron is considered (Supplementary Fig. S5A). D Sum of ChIP-seq signals of indicated histone mark associated with the gene do not show a correlation with the effective transport rate (alternatively, windows different sizes along the gene, described in Methods, were tried but yielded no better correlation). E Machine-learning models reveal little predictive power in histone modification ChIP-seq signals. Top, plot of R2 values that indicates the predictive power of machine-learning models that consider indicated features. Error bars indicate the mean +/− standard deviation of R2 value of the cross-validation sets. A two-sided t test was used with */**/*** indicating a P value of <0.05, <0.01, <0.001. Bottom, heatmap of the features’ importance, defined by the gain in accuracy brought by each feature normalized by the total gain. Numbers in the heatmap correspond to the number of ChIP-seq bins selected by the model.
