A linear model based on promoter sequence features predicts a large fraction of the mean-independent component of the noise. (A,B,D,E) Shown is a comparison of the predicted (x-axis) and measured (y-axis) noise for a model that predicts noise using only mean expression (A,D) and for a model that also incorporates promoter sequence features (B,E). Results are shown for promoters with a single Gcn4 site (A,B) or multiple sites (D,E). The noise of each promoter was predicted using a model that was trained on the four subsets that did not include the promoter, out of five equally sized subsets among which we split the data. Pearson’s R-squared value (R2) and Spearman’s rank correlation coefficient (ρ) are shown in each model plot (A,B,D,E). (C,F) The weights of the sequence features used in the linear models presented in B and E (the weight of the expression mean is not shown). The weights correspond to the absolute value of the relative contribution of each feature to the prediction of the noise component that is independent of the mean.
