微调序列以使深度学习模型在大规模蛋白质组数据上运行,提高了变异效应预测的准确性
Eduarda Vaz1, Lena Wang2, Jake Galvin3
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
bioRxiv : the preprint server for biology
|October 3, 2025
概括
使用大样本尺寸的微调蛋白质模型显著改善了变异效应预测,特别是在罕见的遗传变异中. 这种方法提高了未见的基因和个体的准确性,推进了遗传研究.
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:
- 序列到函数模型对变异效应预测有希望.
- 在模型准确性和对新基因和个体的概括方面仍然存在挑战.
研究的目的:
- 用英国生物银行等离子体蛋白质组项目的一大数据集来微调Borzoi模型.
- 评估样本大小和变异频率对预测准确性的影响.
主要方法:
- 在54,219个个体和2,923个血蛋白中微调Borzoi.
- 将微调模型与150个单基因模型的弹性网基线进行比较.
- 分析罕见 (MAF < 0.01) 和常见 (MAF > 0.05) 变异对模型性能的影响.
主要成果:
- 与基线相比,微调的Borzoi模型改善了86%的基因的变异效应预测.
- 增加样本规模和包括罕见变体是提高性能的主要驱动因素.
- 微调的Borzoi优先考虑罕见变体,而基线优先考虑监管地区的常见变体.
结论:
- 较大的样本大小和罕见变异的纳入对于增强序列到功能模型在变异效应预测中至关重要.
- 精心调整的Borzoi证明了高度准确的变异效应预测能力,优于预先训练的模型和联合训练的单基因模型.
- 这项研究强调了将多样化的遗传数据纳入强大的预测建模的可行性和重要性.
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