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加快发现线粒体疾病的遗传变异,使用变异prIOritization使用潜伏spAce加速发现

Justine Labory^1,2, Youssef Boulaimen³, Jasmine Singh³

¹Université Côte d'Azur, Inserm U1081, CNRS UMR 7284, Institute for Research on Cancer and Aging Nice (IRCAN), 28, Ave de Valombrose, 06107 Nice Cedex 02, France.

Briefings in bioinformatics

|November 20, 2025

概括

维奥拉是一个新的管道,优先考虑基因变异来诊断线粒体疾病 (MDs). 它大大减少了变体列表,并识别了因果变体,帮助复杂的案件.

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:

解释整个外因组测序变异是具有挑战性的,特别是在线粒体疾病 (MDs) 等异质性疾病中.
现有的方法经常与复杂的案例作斗争,需要改进的变体优先级策略.

研究的目的:

开发和验证VIOLA (Variant prIoritizatiOn using Latent spAce),一种用于优先考虑复杂遗传疾病变异的新型计算管道.
通过整合功能注释,表型数据和机器学习来提高线粒体疾病的诊断产量.

主要方法:

使用变量自编码器来减少功能注释的维度.
采用DBSCAN用于异常检测以识别潜在的致病变体.
综合的人类表型本体学 (HPO) 术语和转录数据用于表型分析.
开发了VIOLA分数 (Vscore),VIOLA等级,ARrank和VIOLA总分数 (VAscore) 来确定变种优先级.

主要成果:

在20名患者的队列中,VIOLA成功地将变体列表减少了99%以上.
在被诊断的病例中,因果变异被使用ARrank排在前5位.
与现有的变种优先级方法相比,管道表现出优越的性能.

结论:

维奥拉为患者提供一种特定的方法来确定变异优先级,显著提高对挑战线粒体疾病病例的诊断能力.
该管道有助于通过有效识别致病性遗传变异来发现新型疾病机制.

关键词:

外基因组测序是指外基因组的测序.机器学习是机器学习.线粒体疾病是线粒体疾病.多种主题的多种主题.变种优先级排名变种优先级排名

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