Abstract
Long noncoding RNAs (lncRNAs) belong to a class of RNA transcripts that do not have the potential to code for proteins. LncRNAs were largely discovered in the transcriptomes of human and several model organisms, using next-generation sequencing (NGS) approaches, which have enabled a comprehensive genome scale annotation of transcripts. LncRNAs are known to have dynamic expression status and have the potential to orchestrate gene regulation at the epigenetic, transcriptional, and posttranscriptional levels. Here we describe the experimental methods involved in the discovery of lncRNAs from the transcriptome of a popular model organism zebrafish (Danio rerio). A structured and well-designed computational analysis pipeline subsequent to the RNA sequencing can be instrumental in revealing the diversity of the lncRNA transcripts. We describe one such computational pipeline used for the discovery of novel lncRNA transcripts in zebrafish. We also detail the validation of the putative novel lncRNA transcripts using qualitative and quantitative assays in zebrafish.
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Acknowledgments
This work was funded by the Council of Scientific and Industrial Research (CSIR), India. S.M., A.S., P.S., and K.K. acknowledge Senior Research Fellowships from CSIR, India.
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Mathew, S. et al. (2019). Methods to Study Long Noncoding RNA Expression and Dynamics in Zebrafish Using RNA Sequencing. In: Lai, X., Gupta, S., Vera, J. (eds) Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8982-9_4
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DOI: https://doi.org/10.1007/978-1-4939-8982-9_4
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