在单核酸分辨率下,植物基因组的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 USA 14853.
bioRxiv : the preprint server for biology
|June 19, 2024
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. 这种多功能模型增强了植物基因组学,并通过识别关键遗传变异来加速作物育种.
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:
- 在植物物种中解释遗传功能和健康效应需要适应型号.
- 在广泛的生物序列上预先训练的语言模型 (LMs) 捕捉了进化保护,使得与监督方法相比,能够进行优越的跨物种预测.
- 植物基因组学中的有限的标记数据对传统的预测模型构成了挑战.
研究的目的:
- 为了介绍PlantCaduceus,一种新的植物DNA语言模型.
- 评估PlantCaduceus在跨不同植物物种的功能基因组元素预测方面的可转移性和性能.
- 评估PlantCaduceus在识别有害突变和因果变异方面的能力.
主要方法:
- 开发了PlantCaduceus,这是一个利用Caduceus和Mamba架构的DNA语言模型.
- 预先训练的PlantCaduceus在一个精选的数据集上,包括16个雄精子基因组.
- 精细调整的PlantCaduceus在有限的标签上的Arabidopsis thaliana数据,用于包括拼接位置和翻译启动/终止地点预测在内的任务.
主要成果:
- 证明了PlantCaduceus与玉米 (Zea mays) 的高可移植性,这是一种分离了1.6亿年的物种.
- 在预测任务中超过现有的DNA语言模型1.45到7.23倍.
- 与最先进的蛋白质LMS实现了有害突变识别的竞争性表现,超过PhyloP的三倍.
- 在阿拉比多普西斯和玉米中成功识别了已知的因果变异.
结论:
- 植物Caduceus是一个多功能DNA语言模型,具有显著的跨物种预测能力.
- 该模型显示了加速植物基因组学研究和作物育种应用的潜力.
- 植物Caduceus提供了一个功能性基因组学和植物变异解释的强大工具.
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