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Review

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type

1
School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
2
School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
3
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(18), 6879; https://doi.org/10.3390/ijms21186879
Received: 16 August 2020 / Revised: 15 September 2020 / Accepted: 17 September 2020 / Published: 19 September 2020
With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs. View Full-Text
Keywords: RNA-binding residues; protein-RNA interactions; ribosomal RNA; transfer RNA; small nuclear RNA; messenger RNA; signal recognition particle; protein-DNA interactions; benchmark; predictive performance RNA-binding residues; protein-RNA interactions; ribosomal RNA; transfer RNA; small nuclear RNA; messenger RNA; signal recognition particle; protein-DNA interactions; benchmark; predictive performance
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MDPI and ACS Style

Wang, K.; Hu, G.; Wu, Z.; Su, H.; Yang, J.; Kurgan, L. Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type. Int. J. Mol. Sci. 2020, 21, 6879. https://doi.org/10.3390/ijms21186879

AMA Style

Wang K, Hu G, Wu Z, Su H, Yang J, Kurgan L. Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type. International Journal of Molecular Sciences. 2020; 21(18):6879. https://doi.org/10.3390/ijms21186879

Chicago/Turabian Style

Wang, Kui, Gang Hu, Zhonghua Wu, Hong Su, Jianyi Yang, and Lukasz Kurgan. 2020. "Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type" International Journal of Molecular Sciences 21, no. 18: 6879. https://doi.org/10.3390/ijms21186879

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