Plant Long Non-Coding RNA Prediction by Random fOrests

Citation

Please cite this article for any publication:

Singh, U., Khemka, N., Rajkumar, M. S., Garg, R., & Jain, M. (2017). 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. Nucleic Acids Research, 45(22),e183. [Article]

Contact

  • Urminder Singh

    School of Computational and Integrative Sciences
    Jawaharlal Nehru University
    New Delhi, India
    Email id: urmind13_sit@jnu.ac.in

  • Dr. Mukesh Jain

    School of Computational and Integrative Sciences
    Jawaharlal Nehru University
    New Delhi, India
    Email id: mjain@jnu.ac.in

Release notes

PLncPRO v1.1 (Date: 18th January 2017):

  • All scripts written in Python
  • Implemented biopython to improve file handling
  • Implemented multithreading capability
  • Added option to store outputs in specified directory

PLncPRO v1.0 (Date: 11th June 2016):

Mountain View School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India

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