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DeepLNC, a long non-coding RNA prediction tool using deep neural network

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Abstract

The significant role of long non-coding RNAs (lncRNAs) in various cellular functions, such as gene imprinting, immune response, embryonic pluripotency, tumorogenesis, and genetic regulations, has been widely studied and reported in recent years. Several experimental and computational methods involving genome-wide search and screenings of ncRNAs are being proposed utilizing sequence features-length, occurrence, and composition of bases with various limitations. The proposed classifier, Deep Neural Network (DNN) is fast and an accurate alternative for the identification of lncRNAs as compared to other existing classifiers. The information content stored in k-mer pattern has been used as a sole feature for the DNN classifier using manually annotated training datasets from LNCipedia and RefSeq database, obtaining accuracy of 98.07 %, sensitivity of 98.98 %, and specificity of 97.19 %, respectively, on test dataset. The k-mer information content generated on the basis of Shannon entropy function has resulted in improved classifier accuracy. This classification framework was also tested on known human genome dataset, and the framework has successfully identified known lncRNAs with 99 % accuracy rate. The said algorithm has been implemented as a web prediction tool, which is available on server interface http://bioserver.iiita.ac.in/deeplnc.

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Acknowledgments

We are thankful to Department of Bioinformatics, Indian Institute of Information Technology-Allahabad, India for providing the computational facility to perform the study.

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  1. Department of Bioinformatics, Indian Institute of Information Technology-Allahabad, Allahabad, UP, India

    Rashmi Tripathi, Sunil Patel, Vandana Kumari, Pavan Chakraborty & Pritish Kumar Varadwaj

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  1. Rashmi Tripathi
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  2. Sunil Patel
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Correspondence to Pritish Kumar Varadwaj.

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Tripathi, R., Patel, S., Kumari, V. et al. DeepLNC, a long non-coding RNA prediction tool using deep neural network. Netw Model Anal Health Inform Bioinforma 5, 21 (2016). https://doi.org/10.1007/s13721-016-0129-2

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