RicePPINet - A computational Interactome for Rice
RicePPINet covers 16,895 non-transposable-element (non-TE) related proteins with 708,819 high-confidence interactions (download riceppinet full dataset) at a 71% true positive rate. The power of the network for discovering novel protein interactions is demonstrated by a comparison with other publicly available PPI prediction methods as well as by experimentally determined PPI datasets. Furthermore, global analysis of domain-mediated interactions reveals RicePPINet well reflects PPIs at the domain level. Our studies have demonstrated the efficiency of RicePPINet-based methods in prioritizing candidate genes involved in complex agronomic traits, is approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of predicted interactome, which will certainly facilitate genetic dissection of agronomically important traits in rice.
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Features used in machine learning to predict rice protein-protein interactions
Coverage(1): the number of protein pairs with an available given feature was divided by the total number of possible pairs.
||Predicted protein interaction
|Preserved interface size
|Fraction of preserved interface
|Biological process ontology
|Molecular function ontology
|Cellular component ontology
Coverage(2): the number of prediced protein-protein interactions with an available given feature was divided by the total number of predicted interactions.
Please cite: Shiwei Liu, Yihui Liu, Jiawei Zhao, Shitao Cai, Hongmei Qian, Kaijing Zuo, Lingxia Zhao, and Lida Zhang. A computational interactome for prioritizing genes associated with complex agronomic traits in rice. Plant Journal. 2017. DOI:10.1111/tpj.13475. Article