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

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 discov...

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Bibliographic Details
Published inPloS one Vol. 11; no. 5; p. e0154567
Main Authors Pian, Cong, Zhang, Guangle, Chen, Zhi, Chen, Yuanyuan, Zhang, Jin, Yang, Tao, Zhang, Liangyun
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 26.05.2016
Public Library of Science (PLoS)
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Summary: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.
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Competing Interests: The authors declare that no competing interests exist.
Conceived and designed the experiments: CP GLZ. Performed the experiments: ZC YYC JZ TY LYZ. Analyzed the data: CP. Contributed reagents/materials/analysis tools: CP. Wrote the paper: CP GLZ.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0154567