Abstract
PIWI-interacting RNAs (piRNAs) are endogenously originated predominantly germline oriented newly described class of small non-coding RNAs or transcripts. piRNAs are found to be crucial ones in rendering translational arrest of proteins thereby protecting the genome germline integrity from invasive transposable elements. piRNAs are demanding more attention because of its potential role in the process of spermatogenesis and male infertility. Though there exist several computational approaches to predict piRNA sequences, a parameter based piRNA prediction strategy was not attempted yet. Understanding this scenario, a comprehensive computational schema has been developed based on Bayes and Tree classifiers. The proposed method provides an integrated platform to analyze, predict and visualize piRNA dataset from other noncoding RNAs in a multi-threaded environment. Moreover, a comparative study of different classification algorithms applicable to piRNA predictions is presented here.
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Acknowledgements
This study received funding from Kerala State Council for Science, Technology and Environment (KSCSTE). The authors thank Dr. Achuthsankar S. Nair, Head, Department of Computational Biology and Bioinformatics, University of Kerala for all kind of advice and support during the time of this work.
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Rahiman, A.A., Ajitha, J., Chandra, V. (2015). An Integrated Computational Schema for Analysis, Prediction and Visualization of piRNA Sequences. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_75
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DOI: https://doi.org/10.1007/978-3-319-22180-9_75
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