对致病性和可能致病性UTR变体进行精心策划的普查,并对深度学习模型进行评估,以预测变体效应
Emma Bohn1, Tammy T Y Lau1, Omar Wagih1
1Deep Genomics Inc., Toronto, ON, Canada.
Frontiers in molecular biosciences
|September 25, 2023
概括
我们在5'和3'未翻译区域 (UTR) 中创建了一组可靠的致病变体,以评估用于罕见疾病预测的深度学习 (DL) 模型. DL模型准确地区分了致病变体,支持它们在罕见疾病研究中的使用.
科学领域:
- 遗传学 是一个遗传学.
- 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:
- 在5'和3'未翻译区域 (UTR) 的变异与罕见疾病有关.
- 变种致病性的预测算法是有价值的,但需要仔细验证.
- 对UTR变体进行深度学习 (DL) 模型实用性的评估至关重要.
研究的目的:
- 开发一组具有高可靠性的致病性 (P) 和可能致病性 (LP) 5'和3'UTR变体.
- 评估基于序列的DL模型在分类这些变体中的性能.
- 为未来的罕见病DL算法开发提供一个验证的数据集.
主要方法:
- 根据致病性指南,从ClinVar对5'和3'UTR进行精选的P/LP变异.
- 对模型匹配的P/LP,模型不匹配的P/LP和良性变异的DL模型预测进行了比较.
- 利用PhyloP评分来评估进化保护差异.
主要成果:
- 建立了一组精选的26个3'和68个5'UTR P/LP变体.
- DL模型显示了P/LP和良性变体之间的统计学上显著的预测差异.
- P/LP变种的PhyloP保护得分明显高于良性变种.
结论:
- 创建了致病性5'和3'UTR变异的高可靠性数据集.
- 深度学习模型有效地预测UTR变体的病原性.
- 这一数据集将有助于开发和验证用于罕见疾病变体解释的DL工具.
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