where N is the total number of amino acids in the ground truth dataset, rather than the total number of predicted amino acids. Thus, if the de novo prediction method occasionally predicts peptides that are too long or too short, then the total number of predicted amino acids (C + I + U ) may not equal N . In this setting, the precision-recall curve will not terminate exactly on the line x = y, and the precision at the end of the list will not equal the recall. Given a precision-recall or precision-coverage curve, three types of performance measures can be calculated. Some studies report the overall precision of the full list, either at the peptide or amino acid level. At the peptide level, this value is alternatively referred to as "peptide recall" or "peptide-level accuracy." At the amino acid level, "positional accuracy" or "amino acid-level accuracy" are sometimes used. In addition, some studies report the area under either the precision-recall curve or the precision-coverage curve . This approach has the advantage of giving a higher score to methods that successfully rank correct predictions above incorrect ones. Finally, at least one study measures the number of spectra that are correctly predicted subject to a specified precision threshold (of 95% or 99%) . This approach captures the intuition that, in practice, many end users are interested in obtaining a large set of predictions with a high estimated precision.