CADD v1.7:使用蛋白质语言模型,调控CNN和其他核酸水平得分来改善全基因组变异预测
Max Schubach1, Thorben Maass2, Lusiné Nazaretyan1
1Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Nucleic acids research
|January 6, 2024
概括
最新版本的Combined Annotation-Dependent Depletion (CADD) v1.7通过集成先进的蛋白质语言模型和监管预测来增强遗传变异优先级. 此更新改进了对临床发现和研究的遗传变异的评估.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 机器学习有助于对临床发现和研究的遗传变异评估.
- 综合注释依赖枯竭 (Combined Annotation-Dependent Depletion,简称CADD) 是全基因组变异优先级的领先方法之一.
- 持续开发CADD确保了更好的性能和更广泛的适用性.
研究的目的:
- 介绍最新发布的CADD v1.7.7版本.
- 整合新的注释功能,以增强变体评分和分类.
- 使用各种基准数据集评估CADD v1.7的性能.
主要方法:
- 综合最先进的蛋白质语言模型得分 (Meta ESM-1v).
- 从卷积神经网络中集成的调节变量效应预测.
- 包括来自Zoonomia的序列保护得分.
- 在ClinVar,ExAC/gnomAD,1000个基因组,深度突变扫描 (DMS) 数据集和促进器/增强器测试上评估了性能.
主要成果:
- 加入新的功能显著改善了CADD的整体性能.
- 在预测编码和监管变异效应方面,CADD v1.7表现出更高的准确性.
- 新的特征有助于更强大的全基因组变异优先级.
结论:
- 在基于机器学习的变种优先级上,CADD v1.7 代表了显著的进步.
- 更新版本为遗传研究和临床变异评估提供了改进的工具.
- 科学界可以获得包括分数,脚本和web服务器在内的资源.
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