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米SNP-ABST:一种深度学习方法,用于识别在米中的非生物压力相关的单核酸多态

Quan Lu¹, Jiajun Xu¹, Renyi Zhang¹

¹School of Information and Artificial Intelligence, Anhui Provincial Engineering Research Center for Beidou Precision Agriculture Information, Anhui Agricultural University, 130, Changjiang West Road, Hefei, Anhui Province 230036, China.

Briefings in bioinformatics

|January 6, 2025

概括

开发抗性大米品种需要识别与非生物性压力特征 (ABST-SNP) 相关的单核酸多态 (SNP). 新的RiceSNP-ABST模型使用深度学习准确地预测了米中的这些关键的ABST-SNP.

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.
植物科学植物科学

背景情况:

无生物压力对大米生产产生重大影响,需要开发抗压品种.
准确识别与非生物性压力特征 (ABST-SNP) 相关的单核酸多态 (SNP) 对米育种至关重要.
目前的研究面临挑战,原因是大米中缺乏高质量的非生物应激数据,以及深度学习在该领域的应用不足.

研究的目的:

开发一个高效的计算模型来预测大米中的ABST-SNP.
为了解决数据和计算工具的稀缺性,以识别与大米非生物应激耐受性相关的遗传变异.
为了促进改良品种的育种,提高对环境挑战的抵抗力.

主要方法:

采用一种新的负样本构建策略,生成六个培训数据集.
基于DNA序列片段的四种特征编码方法被提出,随后是特征选择.
具有残余连接的卷积神经网络被用于ABST-SNP预测,以及多颗粒度因果结构学习.

主要成果:

提出的RiceSNP-ABST模型与传统的机器学习和现有的最先进的方法相比,表现优越.
该模型在独立和跨物种数据集上表现出强大的概括能力.
多粒度因果结构学习有助于阐明DNA结构特征之间的关系,以有效识别关键遗传变异.

结论:

米SNP-ABST提供了一种有效的基于深度学习的方法,用于预测米中的ABST-SNP.
该模型的性能和通用性表明其在加速米改进和育种计划中的实用性.
开发的基于网络的工具 (http://rice-snp-abst.aielab.cc) 为大米遗传学和育种的研究人员提供了宝贵的资源.

关键词:

在GWAS中,GWAS就是GWAS.国家统一计划 (SNP) 是一个国家统一计划.无生物压力特征的特征深度学习是一种深度学习.米米饭米饭米饭米饭.

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