An example of E -value calibration with database size dependence. Average cumulative number of false positives versus E-values for Mascot. Theoretically speaking, if the number of trials is large enough, the average number of false positives with E-value less than or equal to a cutoff E
should be E
(indicated by the solid black line). Panel (a) displays the results from various random databases of different sizes; panel (b) displays the same results after rescaling the effective variable according to the size of the random databases. For Mascot, the rescaled curves aggregate well and the aggregate shows a slope quite parallel to the theoretical curve on the log-log plot, indicating a simple rescaling of E-value will suffice to bring the aggregate to the theoretical line. For other methods, the transformation function may be more complicated.