Abstract：
Stopgain substitutions are the third-largest class of monogenic human disease mutations and often examined first in patient exomes. Existing computational stopgain pathogenicity predictors, however, exhibit poor performance at the high sensitivity required for clinical use. Here, we introduce a new classifier, termed X-CAP, which uses a novel training methodology and unique feature set to improve the AUROC by 18% and decrease the false-positive rate 4-fold on large variant databases. In patient exomes, X-CAP prioritizes causal stopgains better than existing methods do, further illustrating its clinical utility. X-CAP is available at https://github.com/bejerano-lab/X-CAP .
摘要：
Stopgain取代是人类疾病单基因突变的第三大类别，通常首先在患者外显子组中进行检查。现有的计算stopgain致病性预测因子，然而,在临床使用所需的高灵敏度下表现出较差的性能。这里,我们引入了一个新的分类器，被称为X-CAP，它使用新颖的训练方法和独特的特征集将AUROC提高了18％，并将大型变异数据库上的假阳性率降低了4倍。在患者外显子组中，X-CAP比现有方法更好地优先考虑因果关系，进一步说明其临床实用性。X-CAP可在https://github.com/bejerano-lab/X-CAP获得。
