School of Biological Engineering, Sichuan University of Science & Engineering, Yibin 644000, China.
National Agriculture Science Data Center, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Genes (Basel). 2024 May 9;15(5):603. doi: 10.3390/genes15050603.
As a fundamental global staple crop, rice plays a pivotal role in human nutrition and agricultural production systems. However, its complex genetic architecture and extensive trait variability pose challenges for breeders and researchers in optimizing yield and quality. Particularly to expedite breeding methods like genomic selection, isolating core SNPs related to target traits from genome-wide data reduces irrelevant mutation noise, enhancing computational precision and efficiency. Thus, exploring efficient computational approaches to mine core SNPs is of great importance. This study introduces PlantMine, an innovative computational framework that integrates feature selection and machine learning techniques to effectively identify core SNPs critical for the improvement of rice traits. Utilizing the dataset from the 3000 Rice Genomes Project, we applied different algorithms for analysis. The findings underscore the effectiveness of combining feature selection with machine learning in accurately identifying core SNPs, offering a promising avenue to expedite rice breeding efforts and improve crop productivity and resilience to stress.
作为全球主要的粮食作物之一，水稻在人类营养和农业生产系统中起着至关重要的作用。然而，其复杂的遗传结构和广泛的性状变异性给育种家和研究人员在优化产量和品质方面带来了挑战。特别是为了加速基因组选择等育种方法，从全基因组数据中分离与目标性状相关的核心 SNPs 可以减少不相关的突变噪声，提高计算精度和效率。因此，探索有效的计算方法来挖掘核心 SNPs 非常重要。本研究介绍了 PlantMine，这是一种创新的计算框架，它集成了特征选择和机器学习技术，可有效地识别对水稻性状改良至关重要的核心 SNPs。我们利用 3000 份水稻基因组计划的数据集应用了不同的算法进行分析。研究结果强调了在准确识别核心 SNPs 方面结合特征选择和机器学习的有效性，为加速水稻育种工作提供了有前景的途径，提高了作物的生产力和对胁迫的适应能力。
