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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li J, Han L, Roebuck P, Diao L, Liu L, Yuan Y, Weinstein JN, Liang H (2015) TANRIC: an interactive open platform to explore the function of lncRNAs in cancer. Cancer Research 75(18):3728–3737. https://doi.org/10.1158/0008-5472.can-15-0273
Li Q, Yu X, Chaudhary R, Slebos RJ, Chung CH, Wang X (2018) lncDIFF: a novel distribution-free method for differential expression analysis of long non-coding RNA. bioRxiv. https://doi.org/10.1101/420562
Zheng H, Brennan K, Hernaez M, Gevaert O (2019) Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples. Gigascience 8(12):giz145. https://doi.org/10.1093/gigascience/giz145
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635
Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12(4):357–360. https://doi.org/10.1038/nmeth.3317
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359. https://doi.org/10.1038/nmeth.1923
Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34(5):525–527. https://doi.org/10.1038/nbt.3519
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14(4):417–419. https://doi.org/10.1038/nmeth.4197
Anders S, Pyl PT, Huber W (2015) HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169. https://doi.org/10.1093/bioinformatics/btu638
Liao Y, Smyth GK, Shi W (2013) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30(7):923–930. https://doi.org/10.1093/bioinformatics/btt656
Li B, Dewey CN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12(1):323. https://doi.org/10.1186/1471-2105-12-323
Abbas-Aghababazadeh F, Li Q, Fridley BL (2018) Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing. PLoS One 13(10):e0206312. https://doi.org/10.1371/journal.pone.0206312
Yan X, Hu Z, Feng Y, Hu X, Yuan J, Zhao SD, Zhang Y, Yang L, Shan W, He Q (2015) Comprehensive genomic characterization of long non-coding RNAs across human cancers. Cancer Cell 28(4):529–540
van de Wiel MA, Neerincx M, Buffart TE, Sie D, Verheul HMW (2014) ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs. BMC Bioinformatics 15(1):116. https://doi.org/10.1186/1471-2105-15-116
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. https://doi.org/10.1093/bioinformatics/btp616
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15(12):550. https://doi.org/10.1186/s13059-014-0550-8
Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P (2018) A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun 9(1):284. https://doi.org/10.1038/s41467-017-02554-5
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300
Li Q, Noel-MacDonnell JR, Koestler DC, Goode EL, Fridley BL (2018) Subject level clustering using a negative binomial model for small transcriptomic studies. BMC Bioinformatics 19(1):474. https://doi.org/10.1186/s12859-018-2556-9
Robinson MD, Smyth GK (2008) Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9(2):321–332. https://doi.org/10.1093/biostatistics/kxm030
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
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
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1307-8_11
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1306-1
Online ISBN: 978-1-0716-1307-8
eBook Packages: Springer Protocols