X-CAP improves pathogenicity prediction of stopgain variants.

X-CAP improves pathogenicity prediction of stopgain variants.
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DOI:
10.1186/s13073-022-01078-y
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发表时间:
2022-07-29
期刊:
影响因子:
12.3
通讯作者:
--
中科院分区:
生物学1区
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--
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Stopgain置换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查。然而,现有的计算stopgain致病性预测,表现出临床使用所需的高灵敏度的性能差。在这里,我们介绍了一种新的分类器,称为X-CAP,它使用一种新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停止,进一步说明了其临床实用性。X-CAP可在https://github.com/bejerano-lab/X-CAP上获得。在线版本包含补充材料,可在(10.1186/s13073-022-01078-y)获得。
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. The online version contains supplementary material available at (10.1186/s13073-022-01078-y).
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