miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences

• Published in: Methods in molecular biology (Clifton, N.J.) Vol. 1932; p. 89
• Main Authors: Zhang, Ting, Ju, Lie, Zhai, Jingjing, Song, Yujia, Song, Jie, Ma, Chuang
• Format: Journal Article
• Language: English
• Published: United States 2019
• Subjects: Plant; miRNAs; Pre-miRNA; Machine learning; Prediction; miRNA-miRNA duplex; Random forest; Secondary structure
• ISSN: 1940-6029
• DOI: 10.1007/978-1-4939-9042-9_6

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 .