Abstract
microRNAs (miRNAs) are short, noncoding regulatory RNAs derived from hairpin precursors (pre-miRNAs). In synergy with experimental approaches, computational approaches have become an invaluable tool for identifying miRNAs at the genome scale. We have recently reported a method called miRLocator, which applies machine learning algorithms to accurately predict the localization of most likely miRNAs within their pre-miRNAs. One major strength of miRLocator is the fact that the machine learning-based miRNA prediction model can be automatically trained using a set of miRNAs of particular interest, with informative features extracted from miRNA-miRNA* duplexes and the optimized ratio between positive and negative samples. Here, we present a detailed protocol for miRLocator that performs the training and prediction processes using a python implementation and web interface. The source codes, web interface, and manual documents are freely available to academic users at https://github.com/cma2015/miRLocator.
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Acknowledgments
This work was supported by funding from the National Natural Science Foundation of China (31570371), the Youth 1000-Talent Program of China, the Hundred Talents Program of Shaanxi Province of China, the Projects of Youth Technology New Star of Shaanxi Province (2017KJXX-67), and the Fund of Northwest A & F University.
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Zhang, T., Ju, L., Zhai, J., Song, Y., Song, J., Ma, C. (2019). miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences. In: de Folter, S. (eds) Plant MicroRNAs. Methods in Molecular Biology, vol 1932. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9042-9_6
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DOI: https://doi.org/10.1007/978-1-4939-9042-9_6
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