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

  • Ting Zhang
  • Lie Ju
  • Jingjing Zhai
  • Yujia Song
  • Jie Song
  • Chuang MaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1932)

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.

Key words

Machine learning miRNAs miRNA-miRNA* duplex Plant Pre-miRNA Prediction Random forest Secondary structure 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Ting Zhang
    • 1
    • 2
  • Lie Ju
    • 3
  • Jingjing Zhai
    • 1
    • 2
  • Yujia Song
    • 3
  • Jie Song
    • 1
    • 4
  • Chuang Ma
    • 1
    • 2
    • 4
    Email author
  1. 1.State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life SciencesNorthwest A&F UniversityYanglingChina
  2. 2.Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of AgricultureNorthwest A&F UniversityYanglingChina
  3. 3.College of Information EngineeringNorthwest A&F UniversityYanglingChina
  4. 4.Biomass Energy Center for Arid and Semi-arid LandsNorthwest A&F UniversityYanglingChina

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