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Differential Expression Analysis of Long Noncoding RNAs

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RNA Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2284))

Abstract

Long noncoding RNA (lncRNA) expression data have been increasingly used in identifying diagnostic and prognostic biomarkers in clinical studies. Low-expression genes are commonly observed in lncRNA and need to be effectively accommodated in differential expression analysis. In this chapter, we describe a protocol based on existing R packages for lncRNA differential expression analysis, including lncDIFF, ShrinkBayes, DESeq2, edgeR, and zinbwave, and provide an example application in a cancer study. In order to establish guidelines for proper application of these packages, we also compare these tools based on the implemented core algorithms and statistical models. We hope that this chapter will provide readers with a practical guide on the analysis choices in lncRNA differential expression analysis.

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Acknowledgments

We thank Denise Kalos for providing helpful comments and editing for this chapter. This work has been supported in part by the Biostatistics and Bioinformatics Core at the H. Lee Moffitt Cancer Center & Research Institute, a comprehensive cancer center designated by the National Cancer Institute and funded in part by Moffitt’s Cancer Center Support Grant (P30-CA076292). This work was also supported in part by the Environmental Determinants of Diabetes in the Young (TEDDY) study, funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK).

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Correspondence to Xuefeng Wang .

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Li, Q., Wang, X. (2021). Differential Expression Analysis of Long Noncoding RNAs. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1307-8_11

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  • DOI: https://doi.org/10.1007/978-1-0716-1307-8_11

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1306-1

  • Online ISBN: 978-1-0716-1307-8

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