Department of Computer Science, Stanford University, Stanford, USA.
Institute of Medical Genetics, Cardiff University, Cardiff, UK.
Genome Med. 2022 Jul 29;14(1):81. doi: 10.1186/s13073-022-01078-y.
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 .
终止增益突变是第三大类单基因人类疾病突变，通常首先在患者外显子组中进行检测。然而，现有的计算终止增益致病性预测器在临床应用所需的高灵敏度方面表现不佳。在这里，我们引入了一种新的分类器，称为 X-CAP，它使用一种新颖的训练方法和独特的特征集，在大型变异数据库中提高 AUROC 18%，并将假阳性率降低 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性终止增益，进一步说明了其临床应用。X-CAP 可在 https://github.com/bejerano-lab/X-CAP 获得。
