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Hongmei Zhang^1,2, Xuanrui Liu^1,2, Wuyong Liu³

¹Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education (Northeast Forestry University), Harbin, 150040, China.

The Plant journal : for cell and molecular biology

|November 6, 2025

概括

一个新的深度学习工具,PASRGA,准确地注释了植物基因对非生物应激抵抗,有助于作物改善. 这一进步支持了提高粮食安全的育种努力.

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.
植物科学植物科学

背景情况:

减少的DNA测序成本产生了大量的基因组数据,这对于植物非生物应激研究和作物增强至关重要.
准确的基因注释是利用基因组数据用于植物科学应用的重要瓶.

研究的目的:

开发和验证PASRGA,这是一个深度学习模型,用于注释植物基因,使其抵抗干旱,盐,寒冷和紫外线等非生物压力.
创建一个全面的数据库 (PlantASRG),集成人工策划和预测的基因数据,用于作物育种.

主要方法:

PASRGA使用转移学习和对比学习来进行基因注释.
使用F1分数,AUROC,AUPRC和MCC来评估性能,并与现有方法 (CLEAN,PF-NET,NetGO 4.0,传统ML) 进行比较.
在Eutrema salsugineum中使用盐应激治疗进行实验验证,随后对17种主要作物进行全基因组注释.

主要成果:

在注释抗压基因方面,PASRGA显著优于现有模型.
该工具显示了高精度的指标 (F1,AUROC,AUPRC,MCC).
PASRGA成功注释了17种主要作物的基因组,并被整合到PlantASRG数据库中.

结论:

帕斯拉 (PASRGA) 是一种高效的深度学习工具,用于注释植物非生物应激抵抗基因.
植物ASRG数据库为作物育种和粮食安全倡议提供了宝贵的资源.
这项工作促进了基因组数据的利用,以开发适应气候变化的作物.

关键词:

无生物压力是无生物压力.这是一种繁殖繁殖.功能性注释功能性注释大型语言模型

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