IRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice. Issue 1 (2nd December 2021)

• Record Type: Journal Article
• Authors: Lv, Hao; Zhang, Yang; Wang, Jia-Shu; Yuan, Shi-Shi; Sun, Zi-Jie; Dao, Fu-Ying; Guan, Zheng-Xing; Lin, Hao; Deng, Ke-Jun
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 1(2022)
• Publication Date: 2021-12-02
• Subjects: PTM -- XGBoost -- feature integration -- rice -- web-server; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbab486
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS .