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LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature

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  • Cong Pian
  • Guangle Zhang
  • Zhi Chen
  • Yuanyuan Chen
  • Jin Zhang
  • Tao Yang
  • Liangyun Zhang

Abstract

As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at http://mm20132014.wicp.net:57203/LncRNApred/home.jsp.

Suggested Citation

  • Cong Pian & Guangle Zhang & Zhi Chen & Yuanyuan Chen & Jin Zhang & Tao Yang & Liangyun Zhang, 2016. "LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-20, May.
    • Handle: RePEc:plo:pone00:0154567
    DOI: 10.1371/journal.pone.0154567
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