Abstract
Long noncoding RNAs (lncRNAs) constitutes a class of noncoding RNAs which are versatile molecules and perform various regulatory functions. Hence, identifying its target mRNAs is an important step in predicting the functions of these molecules. Current lncRNA target prediction tools are not efficient enough to identify lncRNA-mRNA interactions accurately. The reliability of these methods is an issue, as interaction site detections are inaccurate quite often. In this paper our aim is to predict the lncRNA-mRNA interactions efficiently, incorporating the sequence, structure, and energy-based features of the lncRNAs and mRNAs. A brief study on the existing tools for RNA-RNA interaction helped us to understand the different binding sites, and after compiling the tools, we have modified the algorithms to detect the accessible sites and their energies for each interacting RNA sequence. Further RNAstructure tool is used to get the hybrid interaction structure for the accessible lncRNA and mRNA sites. It is found that our target prediction tool gives a better accuracy over the existing tools, after encompassing the sequence, structure, and energy features.
The original version of this chapter was revised: The chapter has been retracted. The retraction note to this chapter is available at https://doi.org/10.1007/978-981-10-8569-7_39
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Change history
23 August 2018
An erratum has been published.
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Acknowledgements
We would like to thank Dr. Zhumur Ghosh (Assistant Professor, Bose Institute) and Sibun Parida (Research Associate, Bioinformatics Center) for their valuable support.
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Sharma, D., Meena, G. (2018). RETRACTED CHAPTER: In-silico Analysis of LncRNA-mRNA Target Prediction. In: Reddy Edla, D., Lingras, P., Venkatanareshbabu K. (eds) Advances in Machine Learning and Data Science. Advances in Intelligent Systems and Computing, vol 705. Springer, Singapore. https://doi.org/10.1007/978-981-10-8569-7_28
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DOI: https://doi.org/10.1007/978-981-10-8569-7_28
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