TISCalling: leveraging machine learning to identify translational initiation sites in plants and viruses.

ABSTRACT: The recognition of translational initiation sites (TISs) offers complementary insights into identifying genes encoding novel proteins or small peptides. Conventional computational methods primarily identify Ribo-seq-supported TISs and lack the capacity of systematic and global identification of TIS, especially for non-AUG sites in plants. Additionally, these methods are often unsuitable for evaluating the importance of mRNA sequence features for TIS determination. In this study, we present TISCalling, a robust framework that combines machine learning (ML) models and statistical analysis to identify and rank novel TISs across eukaryotes. TISCalling generalized and ranks important features common to multiple plant and mammalian species while identifying kingdom-specific features such as mRNA

SUBMITTER: Yen MR 

PROVIDER: S-EPMC12316744 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature