ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features
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...
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MDPI AG
2022-01-01
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author | Prabina Kumar Meher Shbana Begam Tanmaya Kumar Sahu Ajit Gupta Anuj Kumar Upendra Kumar Atmakuri Ramakrishna Rao Krishna Pal Singh Om Parkash Dhankher |
author_facet | Prabina Kumar Meher Shbana Begam Tanmaya Kumar Sahu Ajit Gupta Anuj Kumar Upendra Kumar Atmakuri Ramakrishna Rao Krishna Pal Singh Om Parkash Dhankher |
author_sort | Prabina Kumar Meher |
collection | DOAJ |
description | 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 <i>K</i>-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. |
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spelling | doaj.art-ad1699c583be4cc2a5c27b1543fdd5912023-11-23T16:43:25ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-01-01233161210.3390/ijms23031612ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional FeaturesPrabina Kumar Meher0Shbana Begam1Tanmaya Kumar Sahu2Ajit Gupta3Anuj Kumar4Upendra Kumar5Atmakuri Ramakrishna Rao6Krishna Pal Singh7Om Parkash Dhankher8ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi 110012, IndiaICAR-National Institute for Plant Biotechnology, Indian Council of Agricultural Research, New Delhi 110012, IndiaICAR-National Bureau of Plant Genetic Resources, Indian Council of Agricultural Research, New Delhi 110012, IndiaICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi 110012, IndiaICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research, New Delhi 110012, IndiaDepartment of Molecular Biology, Biotechnology and Bioinformatics, College of Basic Sciences and Humanities, CCS Haryana Agricultural University, Hisar 125004, IndiaIndian Council of Agricultural Research (ICAR), New Delhi 110012, IndiaBiophysics Unit, College of Basic Sciences and Humanities, GB Pant University of Agriculture & Technology, Pantnagar 263145, IndiaStockbridge School of Agriculture, University of Massachusetts Amherst, Amherst, MA 01003, USAMicroRNAs (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 <i>K</i>-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.https://www.mdpi.com/1422-0067/23/3/1612abiotic stressmiRNAsstress-responsive genesmachine learningcomputational biology |
spellingShingle | Prabina Kumar Meher Shbana Begam Tanmaya Kumar Sahu Ajit Gupta Anuj Kumar Upendra Kumar Atmakuri Ramakrishna Rao Krishna Pal Singh Om Parkash Dhankher ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features International Journal of Molecular Sciences abiotic stress miRNAs stress-responsive genes machine learning computational biology |
title | ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features |
title_full | ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features |
title_fullStr | ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features |
title_full_unstemmed | ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features |
title_short | ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo <i>K</i>-Tuple Nucleotide Compositional Features |
title_sort | asrmirna abiotic stress responsive mirna prediction in plants by using machine learning algorithms with pseudo i k i tuple nucleotide compositional features |
topic | abiotic stress miRNAs stress-responsive genes machine learning computational biology |
url | https://www.mdpi.com/1422-0067/23/3/1612 |
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