From the annotated ranked lists, various performance measures can be calculated. Similar to the binary classification setting, we can apply a threshold to these ranked lists to separately consider predictions with confidence scores that fall above and below the threshold. In a binary classification setting, the precision and recall performance metrics are defined as follows: precision = TP TP + FP recall = TP TP + FN , where TP, FP, and FN correspond to the number of true positives, false positives, and false negatives, respectively, defined with respect to a particular score threshold. Thus, "precision" is the proportion of correct predictions among all predictions with scores greater than the specified threshold, and "recall" is the proportion of examples with positive labels that receive scores greater than the specified threshold. Many de novo sequencing studies borrow the terms "precision" and "recall" but employ somewhat idiosyncratic definitions. In particular, because de novo sequencing is not a binary classification task, the traditional categories of true positive, false positive, true negative, and false negative do not apply. Instead, there are only three categories: predictions above the threshold are marked "correct" or "incorrect," and predictions below the threshold are marked "unpredicted" (Figure ). Using these categories, we can define: