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Pradhan UK, Meher PK, Naha S, Rao AR, Kumar U, Pal S, Gupta A. ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants. Funct Integr Genomics 2023; 23:92. [PMID: 36939943 DOI: 10.1007/s10142-023-01014-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/18/2023] [Accepted: 03/06/2023] [Indexed: 03/21/2023]
Abstract
Abiotic stresses have become a major challenge in recent years due to their pervasive nature and shocking impacts on plant growth, development, and quality. MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of specific 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 model for prediction of miRNAs associated with four specific abiotic stresses such as cold, drought, heat and salt. The pseudo K-tuple nucleotide compositional features of Kmer size 1 to 5 were used to represent miRNAs in numeric form. Feature selection strategy was employed to select important features. With the selected feature sets, support vector machine (SVM) achieved the highest cross-validation accuracy in all four abiotic stress conditions. The highest cross-validated prediction accuracies in terms of area under precision-recall curve were found to be 90.15, 90.09, 87.71, and 89.25% for cold, drought, heat and salt respectively. Overall prediction accuracies for the independent dataset were respectively observed 84.57, 80.62, 80.38 and 82.78%, for the abiotic stresses. The SVM was also seen to outperform different deep learning models for prediction of abiotic stress-responsive miRNAs. To implement our method with ease, an online prediction server "ASmiR" has been established at https://iasri-sg.icar.gov.in/asmir/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for identification of specific abiotic stress-responsive miRNAs in plants.
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Affiliation(s)
- Upendra Kumar Pradhan
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, 110012, India
| | - Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, 110012, India.
| | - Sanchita Naha
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, 110012, India
| | | | - Upendra Kumar
- Department of Molecular Biology, Biotechnology and Bioinformatics, College of Basic Sciences and Humanities, CCS Haryana Agricultural University, Hisar, 125004, India
| | - Soumen Pal
- Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, 110012, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi, 110012, India
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ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pseudo K-Tuple Nucleotide Compositional Features. Int J Mol Sci 2022; 23:ijms23031612. [PMID: 35163534 PMCID: PMC8835813 DOI: 10.3390/ijms23031612] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/23/2022] [Accepted: 01/26/2022] [Indexed: 02/04/2023] Open
Abstract
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 K-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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Nicolet BP, Zandhuis ND, Lattanzio VM, Wolkers MC. Sequence determinants as key regulators in gene expression of T cells. Immunol Rev 2021; 304:10-29. [PMID: 34486113 PMCID: PMC9292449 DOI: 10.1111/imr.13021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022]
Abstract
T cell homeostasis, T cell differentiation, and T cell effector function rely on the constant fine-tuning of gene expression. To alter the T cell state, substantial remodeling of the proteome is required. This remodeling depends on the intricate interplay of regulatory mechanisms, including post-transcriptional gene regulation. In this review, we discuss how the sequence of a transcript influences these post-transcriptional events. In particular, we review how sequence determinants such as sequence conservation, GC content, and chemical modifications define the levels of the mRNA and the protein in a T cell. We describe the effect of different forms of alternative splicing on mRNA expression and protein production, and their effect on subcellular localization. In addition, we discuss the role of sequences and structures as binding hubs for miRNAs and RNA-binding proteins in T cells. The review thus highlights how the intimate interplay of post-transcriptional mechanisms dictate cellular fate decisions in T cells.
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Affiliation(s)
- Benoit P. Nicolet
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Nordin D. Zandhuis
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - V. Maria Lattanzio
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Monika C. Wolkers
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
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miRNA Targeting: Growing beyond the Seed. Trends Genet 2019; 35:215-222. [PMID: 30638669 DOI: 10.1016/j.tig.2018.12.005] [Citation(s) in RCA: 162] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 12/18/2018] [Accepted: 12/19/2018] [Indexed: 11/23/2022]
Abstract
miRNAs are small RNAs that guide Argonaute proteins to specific target mRNAs to repress their translation and stability. Canonically, miRNA targeting is reliant on base pairing of the seed region, nucleotides 2-7, of the miRNA to sites in mRNA 3' untranslated regions. Recently, the 3' half of the miRNA has gained attention for newly appreciated roles in regulating target specificity and regulation. In addition, the extent of pairing to the miRNA 3' end can influence the stability of the miRNA itself. These findings highlight the importance of sequences beyond the seed in controlling the function and existence of miRNAs.
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