ROC curves. ROC curves of one-to-one orthologous relationship predictions using the Bi-directional Best Hit method with several best-hit criteria and substitution matrices. Either the score (bdb-score) or the e-value (bdb-evalue) was used to determine the best hit, and the alignments were obtained with SSEARCH using either the BLOSUM62 matrix or the MOLLI60 matrix. As inset, is a zoom-in on the most interesting part of the curve: where the trade-off between sensitivity and specifity is usually determined for orthology predictions. Note that sensitivity (or true positive rate) is plotted against the absolute number of false positives instead of the false positive rate as in a classical ROC curve, for lisibility reasons. FP rate can be obtained simply by dividing the amount of FP by the amount of non-orthologous relationships which is constant (884833).
Lemaitre et al. BMC Bioinformatics 2011 12:457 doi:10.1186/1471-2105-12-457