Abstract
In plants, GIGANTEA (GI) protein plays different biological functions including carbon and sucrose metabolism, cell wall deposition, transpiration and hypocotyl elongation. This suggests that GI is an important class of proteins. So far, the resource-intensive experimental methods have been mostly utilized for identification of GI proteins. Thus, we made an attempt in this study to develop a computational model for fast and accurate prediction of GI proteins. Ten different supervised learning algorithms i.e., SVM, RF, JRIP, J48, LMT, IBK, NB, PART, BAGG and LGB were employed for prediction, where the amino acid composition (AAC), FASGAI features and physico-chemical (PHYC) properties were used as numerical inputs for the learning algorithms. Higher accuracies i.e., 96.75% of AUC-ROC and 86.7% of AUC-PR were observed for SVM coupled with AAC + PHYC feature combination, while evaluated with five-fold cross validation. With leave-one-out cross validation, 97.29% of AUC-ROC and 87.89% of AUC-PR were respectively achieved. While the performance of the model was evaluated with an independent dataset of 18 GI sequences, 17 were observed as correctly predicted. We have also performed proteome-wide identification of GI proteins in wheat, followed by functional annotation using Gene Ontology terms. A prediction server “GIpred” is freely accessible at http://cabgrid.res.in:8080/gipred/ for proteome-wide recognition of GI proteins.
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Availability of data and material
All the datasets used in this study are available at http://cabgrid.res.in:8080/gipred/dataset.html.
Code availability
A prediction server “GIpred” has been developed for the prediction of GIGANTEA proteins. The server is freely accessible at http://cabgrid.res.in:8080/gipred/index.html.
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This study was supported by ICAR CABin Scheme Network project on Agricultural Bioinformatics and Computational Biology (F. No. Agril. Edn. 14/2/2017-A&P dated 02.08.2017), received from Indian Council of Agricultural Research (ICAR), New Delhi. The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
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Conceptualization: PKM; Data curation: SD, SS and TKS; Formal analysis: PKM; Investigation: PKM and SD; Methodology: PKM and SD; Software: TKS and PKM; Validation: SS, SD, TKS and SP; Visualization: PKM, SS and TKS; Roles/Writing—original draft: PKM, SD, SP and SS; Writing—review and editing: PKM and SP.
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Meher, P.K., Dash, S., Sahu, T.K. et al. GIpred: a computational tool for prediction of GIGANTEA proteins using machine learning algorithm. Physiol Mol Biol Plants 28, 1–16 (2022). https://doi.org/10.1007/s12298-022-01130-6
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DOI: https://doi.org/10.1007/s12298-022-01130-6