Exploring salt tolerance mechanisms using machine learning for transcriptomic insights: case study in Spartina alterniflora.

ABSTRACT: Salt stress poses a significant threat to global cereal crop production, emphasizing the need for a comprehensive understanding of salt tolerance mechanisms. Accurate functional annotations of differentially expressed genes are crucial for gaining insights into the salt tolerance mechanism. The challenge of predicting gene functions in under-studied species, especially when excluding infrequent GO terms, persists. Therefore, we proposed the use of NetGO 3.0, a machine learning-based annotation method that does not rely on homology information between species, to predict the functions of differentially expressed genes under salt stress. Spartina alterniflora, a halophyte with salt glands, exhibits remarkable salt tolerance, making it an excellent candidate for in-depth transcriptomic

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11101319 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature