Advertisement

Transcriptomics

RNA-seq
  • Rikke Heidemann Olsen
  • Henrik ChristensenEmail author
Chapter
Part of the Learning Materials in Biosciences book series (LMB)

Abstract

The RNA sequencing (RNA-seq) method is a relative abundance measurement technology. The primary goal of the differential gene expression analysis is to quantitatively measure differences in the levels of transcripts between two or more treatments and groups. RNA sequencing (RNA-seq) is based on high-throughput sequencing which will allow a genome-wide detection of transcribed genes. The workflow for RNA-seq is that extracted RNA is converted to cDNA, it is sequenced with a next-generation sequencing platform such as Illumina, and finally, the sequence data are matched to annotated genes by sequence alignment. Data from sequencing will be provided in FASTQ format. Data management includes assessing data for the quality, aligning of the reads to a reference genome, and normalization of the data, before the differential gene expression analysis can be conducted. There are still some technical problems with the technique awaiting resolution, for instance, with respect to PCR amplification bias and bias with the library construction.

References

  1. Alwine JC, Kemp DJ, & Stark GR. 1977. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc. Natl. Acad. Sci. U.S.A. 74 (12): 5350–5354.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Bester-Van Der Merwe A, Blaauw S, Du Plessis J, Roodt-Wilding R. Transcriptome-wide single nucleotide polymorphisms (SNPs) for abalone (Haliotis midae): validation and application using GoldenGate medium-throughput genotyping assays. Int J Mol Sci. 2013 Sep 23;14(9):19341–60. doi:  https://doi.org/10.3390/ijms140919341.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Buermans HP, den Dunnen JT. 2014. Next generation sequencing technology: Advances and applications. Biochim Biophys Acta. Oct;1842(10):1932-1941. doi:  https://doi.org/10.1016/j.bbadis.2014.06.015.CrossRefGoogle Scholar
  4. Chu Y, Corey DR. 2012. RNA sequencing: platform selection, experimental design, and data interpretation. Nucleic Acid Ther. Aug;22(4):271–4. doi: 10.1089/nat.2012.0367. Epub 2012 Jul 25CrossRefPubMedPubMedCentralGoogle Scholar
  5. Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR, Vailaya A, Wang PL, Adler A, Conklin BR, Hood L, Kuiper M, Sander C, Schmulevich I, Schwikowski B, Warner GJ, Ideker T, Bader GD. 2007. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2:2366–82.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Cui P, Lin Q, Ding F, Xin C, Gong W, Zhang L, Geng J, Zhang B, Yu X, Yang J, Hu S, Yu J. 2010. A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics. Nov;96(5):259-65. doi:  https://doi.org/10.1016/j.ygeno.2010.07.010.CrossRefPubMedGoogle Scholar
  7. Depardieu F, Podglajen I, Leclercq R, Collatz E, Courvalin P. 2007. Modes and modulations of antibiotic resistance gene expression. Clin Microbiol Rev. 2007 Jan;20(1):79–114CrossRefPubMedPubMedCentralGoogle Scholar
  8. Kumar A, Kankainen M, Parsons A, Kallioniemi O, Mattila P, Heckman CA. The impact of RNA sequence library construction protocols on transcriptomic profiling of leukemia. 2017. BMC Genomics. Aug 17;18(1):629. doi:  https://doi.org/10.1186/s12864-017-4039-1.
  9. Liu Y, Zhou J, White KP. 2014. RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30:301–4.CrossRefPubMedGoogle Scholar
  10. Malone JH, Oliver B. 2011. Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol. May 31;9:34. doi:  https://doi.org/10.1186/1741-7007-9-34.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Rolfe MD, Rice CJ, Lucchini S, Pin C, Thompson A, Cameron AD, Alston M, Stringer MF, Betts RP, Baranyi J, Peck MW, Hinton JC. 2012. Lag phase is a distinct growth phase that prepares bacteria for exponential growth and involves transient metal accumulation. J Bacteriol. Feb;194(3):686–701. doi:  https://doi.org/10.1128/JB.06112-11. Epub 2011 Dec 2.CrossRefGoogle Scholar
  12. Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. 2010. Sequencing depth and coverage: key considerations in genomic analyses. Nat Rev Genet. 2014 Feb;15(2):121–32. doi:  https://doi.org/10.1038/nrg3642.CrossRefPubMedGoogle Scholar
  13. Sultan M, Amstislavskiy V, Risch T, Schuette M, Dökel S, Ralser M, Balzereit D, Lehrach H, Yaspo HL.2014. Influence of RNA extraction methods and library selection schemes on RNA-seq data. BMC Genomics. 2014; 15(1): 675. Published online 2014 Aug 11. doi:  https://doi.org/10.1186/1471-2164-15-675.CrossRefPubMedPubMedCentralGoogle Scholar
  14. Tan SC, Yiap BC. 2009. DNA, RNA, and protein extraction: the past and the present. J Biomed Biotechnol.:574398. doi:  https://doi.org/10.1155/2009/574398.CrossRefGoogle Scholar
  15. Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 10, 57–63.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Veterinary Animal SciencesUniversity of CopenhagenCopenhagenDenmark

Personalised recommendations