P-TarPmiR accurately predicts plant-specific miRNA targets

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca. The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

Automated summary

MicroRNAs (miRNAs) regulate gene expression through targeting messenger RNA (mRNAs) and most target predictors focus on animals but perform poorly in plants. In this study, the TarPmiR miRNA target predictor is retrained using plant-specific data from the TarDB database, leading to improved accuracy in predicting miRNA targets in plants. Surprisingly, excluding animal training data results in the most accurate plant-specific miRNA target predictor, suggesting that animal-based data may hinder miRNA target prediction in plants.