在单核酸分辨率下进行植物基因组跨物种建模,使用预训练的DNA语言模型
Jingjing Zhai1, Aaron Gokaslan2, Yair Schiff2
1Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853.
概括
一种新的植物DNA语言模型PlantCaduceus准确地预测了各种物种的基因功能. 该工具通过改进变异效应预测和识别关键遗传元素来增强植物基因组学和作物育种.
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:
- 在多样化的植物基因组中解释功能和健康效应是具有挑战性的.
- 在生物序列上预训练的语言模型 (LMs) 可以捕捉进化保护.
- 监督模型在有限的标记数据中扎,以进行跨物种预测.
研究的目的:
- 介绍PlantCaduceus,一种植物DNA语言模型.
- 开发一种能够学习植物基因组进化保护模式的模型.
- 证明模型在各种基因组任务上的可转移性和性能.
主要方法:
- 训练了PlantCaduceus在16个血管精子基因组上,同时模拟两个DNA链.
- 在有限的标记为*Arabidopsis*的数据上微调模型,用于预测翻译启动/终止和拼接位置.
- 评估了玉米的性能,玉米是一种1亿6000万年前分离的物种.
- 评估变体效应预测能力,并与现有方法进行比较.
主要成果:
- 植物Caduceus证明了与玉米的显著转移性.
- 在玉米拼接捐赠者 (1.45倍) 和翻译启动地点 (7.23倍) 预测中明显优于现有的DNALM.
- 在变异效应预测中实现了与蛋白质LMS相比的性能.
- 由PlantCaduceus预测的有害突变具有较低的小等位基因频率.
- 成功地确定了Arabidopsis和玉米中已知的因果变异.
结论:
- 植物Caduceus是一个多功能DNA语言模型用于植物基因组学.
- 该模型模拟两个DNA链的能力增强了进化保护学习.
- 植物Caduceus显示了加速植物基因组学和作物育种应用的巨大潜力.
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