Li, Kuan and Xu, Chaoqun and Huang, Jian and Liu, Wei and Zhang, Lina and Wan, Weifeng and Tao, Huan and Li, Li and Lin, Shukai and Harrison, Andrew and He, Huaqin (2017) Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativaL.). Briefings in Bioinformatics, 18 (2). pp. 270-278. DOI https://doi.org/10.1093/bib/bbw021
Li, Kuan and Xu, Chaoqun and Huang, Jian and Liu, Wei and Zhang, Lina and Wan, Weifeng and Tao, Huan and Li, Li and Lin, Shukai and Harrison, Andrew and He, Huaqin (2017) Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativaL.). Briefings in Bioinformatics, 18 (2). pp. 270-278. DOI https://doi.org/10.1093/bib/bbw021
Li, Kuan and Xu, Chaoqun and Huang, Jian and Liu, Wei and Zhang, Lina and Wan, Weifeng and Tao, Huan and Li, Li and Lin, Shukai and Harrison, Andrew and He, Huaqin (2017) Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativaL.). Briefings in Bioinformatics, 18 (2). pp. 270-278. DOI https://doi.org/10.1093/bib/bbw021
Abstract
Heterotrimeric G protein signaling cascades are one of the primary metazoan sensing mechanisms linking a cell to environment. However, the number of experimentally identified effectors of G protein in plant is limited. We have therefore studied which tools are best suited for predicting G protein effectors in rice. Here, we compared the predicting performance of four classifiers with eight different encoding schemes on the effectors of G proteins by using 10-fold cross-validation. Four methods were evaluated: random forest, naive Bayes, K-nearest neighbors and support vector machine. We applied these methods to experimentally identified effectors of G proteins and randomly selected non-effector proteins, and tested their sensitivity and specificity. The result showed that random forest classifier with composition of K-spaced amino acid pairs and composition of motif or domain (CKSAAP_PROSITE_200) combination method yielded the best performance, with accuracy and the Mathew's correlation coefficient reaching 74.62% and 0.49, respectively.We have developed G-Effector, an online predictor, which outperforms BLAST, PSI-BLAST and HMMER on predicting the effectors of G proteins. This provided valuable guidance for the researchers to select classifiers combined with different feature selection encoding schemes.We used G-Effector to screen the effectors of G protein in rice, and confirmed the candidate effectors by gene co-expression data. Interestingly, one of the top 15 candidates, which did not appear in the training data set, was validated in a previous research work. Therefore, the candidate effectors list in this article provides both a clue for researchers as to their function and a framework of validation for future experimental work. It is accessible at http://bioinformatics.fafu.edu.cn/geffector.
Item Type: | Article |
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Uncontrolled Keywords: | rice (Oryza sativa L.); heterotrimeric G proteins; effectors; predicting |
Subjects: | Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 23 May 2017 12:58 |
Last Modified: | 07 Aug 2024 19:56 |
URI: | http://repository.essex.ac.uk/id/eprint/19706 |