PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea

@inproceedings{Singh2017PLncPROFP,
  title={PLncPRO for prediction of long non-coding RNAs (lncRNAs) in plants and its application for discovery of abiotic stress-responsive lncRNAs in rice and chickpea},
  author={Urminder Singh and Niraj Khemka and Mohan Singh Rajkumar and Rohini Garg and Mukesh Jain},
  booktitle={Nucleic acids research},
  year={2017}
}
Long non-coding RNAs (lncRNAs) make up a significant portion of non-coding RNAs and are involved in a variety of biological processes. Accurate identification/annotation of lncRNAs is the primary step for gaining deeper insights into their functions. In this study, we report a novel tool, PLncPRO, for prediction of lncRNAs in plants using transcriptome data. PLncPRO is based on machine learning and uses random forest algorithm to classify coding and long non-coding transcripts. PLncPRO has… CONTINUE READING
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