ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K -Tuple Nucleotide Compositional Features

• Published in: International journal of molecular sciences 2022-01, Vol.23 (3), p.1612
• Main Authors: Meher, Prabina Kumar, Begam, Shbana, Sahu, Tanmaya Kumar, Gupta, Ajit, Kumar, Anuj, Kumar, Upendra, Rao, Atmakuri Ramakrishna, Singh, Krishna Pal, Dhankher, Om Parkash
• Format: Article
• Language: English
• Subjects: Abiotic stress; Accuracy; Algorithms; Area Under Curve; Business metrics; Classification; Computational Biology - methods; Computer applications; Cultivars; Datasets; Feature selection; Gene Expression Regulation, Plant; Learning algorithms; Machine learning; MicroRNAs; MicroRNAs - genetics; miRNA; Nucleotides; Plant breeding; Plants - genetics; Predictions; Research methodology; RNA, Plant - genetics; Salinity; Stress, Physiological; Support Vector Machine; Support vector machines
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms23031612

MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational method for prediction of miRNAs associated with abiotic stresses. Three types of datasets were used for prediction, i.e., miRNA, Pre-miRNA, and Pre-miRNA + miRNA. The pseudo -tuple nucleotide compositional features were generated for each sequence to transform the sequence data into numeric feature vectors. Support vector machine (SVM) was employed for prediction. The area under receiver operating characteristics curve (auROC) of 70.21, 69.71, 77.94 and area under precision-recall curve (auPRC) of 69.96, 65.64, 77.32 percentages were obtained for miRNA, Pre-miRNA, and Pre-miRNA + miRNA datasets, respectively. Overall prediction accuracies for the independent test set were 62.33, 64.85, 69.21 percentages, respectively, for the three datasets. The SVM also achieved higher accuracy than other learning methods such as random forest, extreme gradient boosting, and adaptive boosting. To implement our method with ease, an online prediction server "ASRmiRNA" has been developed. The proposed approach is believed to supplement the existing effort for identification of abiotic stress-responsive miRNAs and Pre-miRNAs.