MicroRNAs are a predominant type of small non-coding RNAs approximately 21 nucleotides in length that play an essential role at the post-transcriptional level by either RNA degradation, translational repression or both through an RNA-induced silencing complex. Identification of these molecules can aid the dissecting regulatory functions. The secondary structures of plant pre-miRNAs are much more complex than these of animal pre-miRNAs. In contrast to prediction tools for animal pre-miRNAs, more less efforts have been contributed to plant pre-miRNAs. In this study, a set of novel knowledge-based energy features, which has very high discriminatory power, is proposed and incorporated with the existing features for specifically distinguishing the hairpins of real/pseudo plant pre-miRNAs. A promising performance of area under receiver operating characteristic curve of 0.9444 indicates that 5 knowledge-based energy features have very high discriminatory power. The 10-fold cross-validation result demonstrates that plantMirP with full features has a promising sensitivity of 92.61% and a specificity of 98.88%. Based on various different datasets, it is found that plantMirP has higher prediction performance through comparison with miPlantPreMat, PlantMiRNAPred, triplet-SVM, and microPred. Meanwhile, plantMirP can greatly balance sensitivity and specificity for real/pseudo plant pre-miRNAs. Taken together, we developed a promising SVM-based program, plantMirP, for predicting plant pre-miRNAs by incorporating knowledge-based energy features. This study makes it a valuable tool for miRNA-related studies.  

Download plantMirP standalone program: plantMirP.1.0.0.zip

  Reference:

  Yuangen Yao, Chengzhang Ma, Haiyou Deng, Quan Liu, Jiying Zhang and Ming Yi*. plantMirP: an efficient computational program for the prediction of plant pre-miRNA by incorporating knowledge-based energy features. Molecular BioSystems, 2016, 12, 3124-3131