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

Feature Protein pair Coverage(1) Predicted protein interaction Coverage(2)
Structural similarity 121,410,081 14.72% 486,238 68.60%
Structural distance 121,410,081 14.72% 486,238 68.60%
Preserved interface size 97,755,074 11.85% 441,450 62.28%
Fraction of preserved interface 97,755,074 11.85% 441,450 62.28%
Biological process ontology 129,468,231 15.70% 543,838 76.72%
Molecular function ontology 188,363,222 22.84% 533,153 75.22%
Cellular component ontology 71,682,505 8.69% 388,407 54.80%
Gene coexpression 797,861,431 96.75% 703,595 99.26%
Phylogenetic profile 824,646,966 100.00% 708,819 100.00%
Interolog 881,560 0.11% 443,542 62.57%
Rosetta stone 1,503,600 0.18% 3,627 0.51%
Coverage(1): the number of protein pairs with an available given feature was divided by the total number of possible pairs.
Coverage(2): the number of predicted 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