Abstract
With the ability to obtain tens of millions of reads, high-throughput messenger RNA sequencing (RNA-Seq) data offers the possibility of estimating abundance of isoforms and finding novel transcripts. In this chapter, we describe a protocol to construct an RNA-Seq library for sequencing on Illumina NGS platforms, and a computational pipeline to perform RNA-Seq data analysis. The protocols described in this chapter can be applied to the analysis of differential gene expression in control versus 17β-estradiol treatment of in vivo or in vitro systems.
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References
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63
Ozsolak F, Milos PM (2011) RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 12:87–98
Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18(9):1509–1517
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat Methods 5(7):621–628
Twine NA, Janitz K, Wilkins MR, Janitz M (2011) Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer’s disease. PLoS One 6(1), e16266
Eksi R, Li HD, Menon R et al (2013) Systematically differentiating functions for alternatively spliced isoforms through integrating RNA-seq data. PLoS Comput Biol 9(11), e1003314
http://www.illumina.com/applications/sequencing/rna/mrna-seq.html
Leggett RM, Ramirez-Gonzalez RH, Clavijo BJ, Waite D, Davey RP (2013) Sequencing quality assessment tools to enable data-driven informatics for high throughput genomics. Front Genet 4:288
Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
R: A language and environment for statistical computing. http://www.r-project.org/
Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80
Gaidatzis D, Lerch A, Hahne F, Stadler MB (2014) QuasR: quantification and annotation of short reads in R. Bioinformatics pii, btu781
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140
Acknowledgments
We thank Dr. Thomas Girke at the University of California Riverside for sharing his R scripts.
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Liang, H., Zeng, E. (2016). RNA-Seq Experiment and Data Analysis. In: Eyster, K.M. (eds) Estrogen Receptors. Methods in Molecular Biology, vol 1366. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3127-9_9
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DOI: https://doi.org/10.1007/978-1-4939-3127-9_9
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-3126-2
Online ISBN: 978-1-4939-3127-9
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