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pmiRScan: a LightGBM based method for prediction of animal pre-miRNAs
Amrit Venkatesan1, Jolly Basak2, Ranjit Prasad Bahadur3,4
1Computational Structural Biology Lab, Department of Bioscience and Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India.
Abstract:
MicroRNAs (miRNA) are categorized as short endogenous non-coding RNAs, which have a significant role in post-transcriptional gene regulation. Identifying new animal precursor miRNA (pre-miRNA) and miRNA is crucial to understand the role of miRNAs in various biological processes including the development of diseases. The present study focuses on the development of a Light Gradient Boost (LGB) based method for the classification of animal pre-miRNAs using various sequence and secondary structural features. In various pre-miRNA families, distinct k-mer repeat signatures with a length of three nucleotides have been identified. Out of nine different classifiers that have been trained and tested in the present study, LGB has an overall better performance with an AUROC of 0.959. In comparison with the existing methods, our method 'pmiRScan' has an overall better performance with accuracy of 0.93, sensitivity of 0.86, specificity of 0.95 and F-score of 0.82. Moreover, pmiRScan effectively classifies pre-miRNAs from four distinct taxonomic groups: mammals, nematodes, molluscs and arthropods. We have used our classifier to predict genome-wide pre-miRNAs in human. We find a total of 313 pre-miRNA candidates using pmiRScan. A total of 180 potential mature miRNAs belonging to 60 distinct miRNA families are extracted from predicted pre-miRNAs; of which 128 were novel and are note reported in miRBase. These discoveries may enhance our current understanding of miRNAs and their targets in human. pmiRScan is freely available at http://www.csb.iitkgp.ac.in/applications/pmiRScan/index.php .
Insights
A new Light Gradient Boost (LGB) method, pmiRScan, accurately identifies animal precursor microRNAs (pre-miRNAs) and novel human miRNAs. This tool aids in understanding miRNA roles in biological processes and disease development.
Area of Science:
- Bioinformatics
- Genomics
- Molecular Biology
Background:
- MicroRNAs (miRNAs) are key regulators of gene expression.
- Identifying novel animal precursor miRNAs (pre-miRNAs) is vital for understanding biological processes and disease mechanisms.
Purpose of the Study:
- To develop a robust computational method for classifying animal pre-miRNAs.
- To identify novel pre-miRNAs and mature miRNAs in the human genome.
Main Methods:
- Development of a Light Gradient Boost (LGB) based classifier named pmiRScan.
- Utilized sequence and secondary structural features, including k-mer signatures.
- Evaluated classifier performance using AUROC, accuracy, sensitivity, specificity, and F-score.
Main Results:
- The LGB-based pmiRScan achieved an AUROC of 0.959, outperforming existing methods.
- pmiRScan demonstrated high accuracy (0.93), sensitivity (0.86), specificity (0.95), and F-score (0.82).
- Successfully classified pre-miRNAs across mammals, nematodes, molluscs, and arthropods, and identified 313 human pre-miRNA candidates, including 128 novel mature miRNAs.
Conclusions:
- pmiRScan is an effective tool for classifying animal pre-miRNAs and discovering novel miRNAs.
- The identified novel human miRNAs may offer new insights into gene regulation and disease.
- The pmiRScan tool is publicly available for research use.
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