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Published on: August 21, 2019
MatPred: Computational Identification of Mature MicroRNAs within Novel Pre-MicroRNAs
Jin Li1, Ying Wang2, Lei Wang1
1Institute of Biomedical Engineering, College of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang 150001, China ; Bioinformatics Research Center, College of Automation, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, Heilongjiang 150001, China.
Background:
MicroRNAs (miRNAs) are short noncoding RNAs integral for regulating gene expression at the posttranscriptional level. However, experimental methods often fall short in finding miRNAs expressed at low levels or in specific tissues. While several computational methods have been developed for predicting the localization of mature miRNAs within the precursor transcript, the prediction accuracy requires significant improvement.
Methodology/Principal Findings:
Here, we present MatPred, which predicts mature miRNA candidates within novel pre-miRNA transcripts. In addition to the relative locus of the mature miRNA within the pre-miRNA hairpin loop and minimum free energy, we innovatively integrated features that describe the nucleotide-specific RNA secondary structure characteristics. In total, 94 features were extracted from the mature miRNA loci and flanking regions. The model was trained based on a radial basis function kernel/support vector machine (RBF/SVM). Our method can predict precise locations of mature miRNAs, as affirmed by experimentally verified human pre-miRNAs or pre-miRNAs candidates, thus achieving a significant advantage over existing methods.
Conclusions:
MatPred is a highly effective method for identifying mature miRNAs within novel pre-miRNA transcripts. Our model significantly outperformed three other widely used existing methods. Such processing prediction methods may provide important insight into miRNA biogenesis.
Insights
MatPred accurately predicts mature microRNAs (miRNAs) in precursor transcripts using novel RNA structure features. This computational method improves upon existing tools for identifying these crucial gene regulators.
Area of Science:
- Molecular Biology
- Bioinformatics
- Genetics
Background:
- MicroRNAs (miRNAs) are key posttranscriptional gene regulators.
- Experimental identification of low-abundance or tissue-specific miRNAs is challenging.
- Existing computational miRNA prediction tools require improved accuracy.
Purpose of the Study:
- To develop an accurate computational method for predicting mature miRNA locations within novel precursor miRNA transcripts.
- To enhance the prediction of miRNA candidates using integrated RNA structural features.
Main Methods:
- Developed MatPred, a novel prediction tool for mature miRNA candidates.
- Integrated features including miRNA locus, minimum free energy, and nucleotide-specific RNA secondary structure.
- Extracted 94 features from mature miRNA loci and flanking regions.
- Trained the model using a radial basis function kernel/support vector machine (RBF/SVM).
Main Results:
- MatPred accurately predicts mature miRNA locations in pre-miRNA transcripts.
- The method's predictions were validated using experimentally verified human pre-miRNAs.
- MatPred demonstrated a significant performance advantage over existing prediction methods.
Conclusions:
- MatPred is a highly effective tool for identifying mature miRNAs within novel pre-miRNA transcripts.
- The developed method offers significant improvements over current prediction approaches.
- This approach provides valuable insights into microRNA biogenesis.
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