SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques

Baixue Qiao^1,2,3Wentao Gao⁴Xudong Zhang^1,3Min Du^1,3Xuanrui Liu^1,3Shaozi Pang^1,3Chunxue Yang⁵Jiang Wang^1*Yuming Zhao^4*Linan Xie^2,6*

• ¹Key Laboratory of Saline-Alkali Vegetation Ecology Restoration, Ministry of Education, Northeast Forestry University, Harbin, China
• ²School of Ecology, Northeast Forestry University, Harbin, China
• ³State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
• ⁴College of Computer and Control Engineering, Northeast Forestry University, Harbin, China
• ⁵College of Landscape Architecture, Northeast Forestry University, Harbin, China
• ⁶Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Ecology, Northeast Forestry University, Harbin, China

Mining novel genes underlying agronomical traits is a crucial subject in plant biology, essential for enhancing crop quality, ensuring food security, and preserving biodiversity. Wet experiments are the main methods to uncover genes with target functions but are expensive and time-consuming. Machine learning, in contrast, can accelerate the gene discovery process by learning from accumulated data, making it more efficient and cost-effective. However, despite their potential, existing machinelearning tools to mine stress-resistant genes in plants are scarce. In this study, we developed the first known machine learning model, SaGP (Saline-alkali Genes Prediction), to identify plant saline-alkali tolerance genes based on sequencing data. It outperformed traditional computational tools, i.e., BLAST, and correctly identified the latest published genes. Moreover, we utilized SaGP to evaluate three recently published genes: GhAG2, MdBPR6, and TaCCD1. SaGP correctly identified all their functions.Overall, these results suggest that SaGP can be used for the large-scale identification of saline-alkali tolerance genes and served as a framework for the development of additional automated tools, thus promoting crop breeding and plant conservation. To efficiently identify salt-alkali resistant genes in large-scale data, we developed a userfriendly, freely accessible web service platform based on SaGP (https://www.sagprediction.com/).