通过集群和分类时间版本的生物本体学来生成罕见和未被诊断的疾病的假设
Michael S Bradshaw1, Connor Gibbs2, Skylar Martin1
1Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States of America.
PloS one
|December 26, 2024
概括
这项研究引入了一种新的计算工具,BOCC,以揭示遗传变异和罕见疾病症状之间的隐藏联系. 通过整合蛋白质相互作用和表型数据,BOCC有助于诊断当直接遗传联系是未知的患者.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 罕见疾病 罕见疾病
背景情况:
- 罕见疾病影响10人中的1人,但即使使用先进的遗传检测,诊断仍然难以捉摸,高达一半.
- 未被诊断的患者往往缺乏在已识别的遗传变异和现有文献中观察到的表型之间的明确联系.
研究的目的:
- 通过整合生物网络,开发一种计算方法来发现潜伏的基因与表型连接.
- 通过识别间接变异-表型关系,帮助诊断罕见疾病.
主要方法:
- 蛋白质与蛋白质相互作用数据 (STRING) 和表型关系 (人类表型本体学) 的整合.
- 网络分析和聚类以确定显著的基因与表型联系.
- 开发一个工具,BOCC (基于生物网络的Omics连接连接器),作为一个Web应用程序和命令行工具.
主要成果:
- 与科罗拉多州儿童医院合作,在38名患者中产生了潜在的基因与表型联系的有希望的假设.
- 从MyGene2数据库中为14名患者提供了潜在的诊断.
- 通过BOCC识别的显著集群显示,与不显著集群相比,已知的药物相互作用推断出的基因与表型边缘的2.35至8.72倍.
结论:
- 整合STRING和HPO等生物网络可以揭示间接的基因与表型的联系,这对于诊断罕见疾病至关重要.
- 该BOCC工具在发现这些隐藏的连接方面显示出显著的潜力,改善未被诊断的患者的诊断产量.
- 对STRING和HPO数据的网络时间序列分析可以评估发现的集群的意义.
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