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Prediction and identification of the effectors of heterotrimeric G proteins in rice (oryza sativa L.)

Li, K and Xu, C and Huang, J and Liu, W and Zhang, L and Wan, W and Tao, H and Li, L and Lin, S and Harrison, A and He, H (2017) 'Prediction and identification of the effectors of heterotrimeric G proteins in rice (oryza sativa L.).' Briefings in Bioinformatics, 18 (2). 270 - 278. ISSN 1467-5463

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Abstract

© 2016. Published by Oxford University Press. 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
Subjects: Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 23 May 2017 12:58
Last Modified: 26 Dec 2017 19:15
URI: http://repository.essex.ac.uk/id/eprint/19706

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