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Overview of Gene Expression Analysis: Transcriptomics

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Gene Expression Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1783))

Abstract

Currently, the study of the transcriptome is widely used to interpret the functional elements of the genome and molecular constituents of cells and tissues in an effort to unravel biological pathways associated with development and disease. The advent of technologies is now enabling the study of such comprehensive transcriptional characterization of mRNA, miRNA, lncRNA, and small RNA in a robust and successful manner. Transcriptomic strategies are gaining momentum across diverse areas of biological, plant sciences, medical, clinical, and pharmaceutical research for biomarker discovery, and disease diagnosis and prognosis.

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References

  1. Crick FH (1970) DNA: test of structure? Science 167(3926):1694

    Article  CAS  PubMed  Google Scholar 

  2. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63. https://doi.org/10.1038/nrg2484

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Xu S (2017) Transcriptome profiling in systems vascular medicine. Front Pharmacol 8:563. https://doi.org/10.3389/fphar.2017.00563

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wooten DJ, Quaranta V (2017) Mathematical models of cell phenotype regulation and reprogramming: make cancer cells sensitive again! Biochim Biophys Acta 1867(2):167–175. https://doi.org/10.1016/j.bbcan.2017.04.001

    Article  CAS  Google Scholar 

  5. Raghavachari N, Liu P, Barb JJ, Yang Y, Wang R, Nguyen QT, Munson PJ (2014) Integrated analysis of miRNA and mRNA during differentiation of human CD34+ cells delineates the regulatory roles of microRNA in hematopoiesis. Exp Hematol 42(1):14–27.e11–12. https://doi.org/10.1016/j.exphem.2013.10.003

    Article  CAS  Google Scholar 

  6. de Andres-Pablo A, Morillon A, Wery M (2017) LncRNAs, lost in translation or licence to regulate? Curr Genet 63(1):29–33. https://doi.org/10.1007/s00294-016-0615-1

    Article  PubMed  CAS  Google Scholar 

  7. Pertea M (2012) The human transcriptome: an unfinished story. Genes 3(3):344–360. https://doi.org/10.3390/genes3030344

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Guo X, Gao L, Liao Q, Xiao H, Ma X, Yang X, Luo H, Zhao G, Bu D, Jiao F, Shao Q, Chen R, Zhao Y (2013) Long non-coding RNAs function annotation: a global prediction method based on bi-colored networks. Nucleic Acids Res 41(2):e35. https://doi.org/10.1093/nar/gks967

    Article  PubMed  CAS  Google Scholar 

  9. Esteller M (2011) Non-coding RNAs in human disease. Nat Rev Genet 12(12):861–874. https://doi.org/10.1038/nrg3074

    Article  PubMed  CAS  Google Scholar 

  10. Taft RJ, Pang KC, Mercer TR, Dinger M, Mattick JS (2010) Non-coding RNAs: regulators of disease. J Pathol 220(2):126–139. https://doi.org/10.1002/path.2638

    Article  PubMed  CAS  Google Scholar 

  11. Wang RF, Cao WW, Johnson MG (1991) Development of a 16S rRNA-based oligomer probe specific for Listeria monocytogenes. Appl Environ Microbiol 57(12):3666–3670

    PubMed  PubMed Central  CAS  Google Scholar 

  12. Giulietti A, Overbergh L, Valckx D, Decallonne B, Bouillon R, Mathieu C (2001) An overview of real-time quantitative PCR: applications to quantify cytokine gene expression. Methods 25(4):386–401. https://doi.org/10.1006/meth.2001.1261

    Article  PubMed  CAS  Google Scholar 

  13. Schuler GD, Boguski MS, Stewart EA, Stein LD, Gyapay G, Rice K, White RE, Rodriguez-Tome P, Aggarwal A, Bajorek E, Bentolila S, Birren BB, Butler A, Castle AB, Chiannilkulchai N, Chu A, Clee C, Cowles S, Day PJ, Dibling T, Drouot N, Dunham I, Duprat S, East C, Edwards C, Fan JB, Fang N, Fizames C, Garrett C, Green L, Hadley D, Harris M, Harrison P, Brady S, Hicks A, Holloway E, Hui L, Hussain S, Louis-Dit-Sully C, Ma J, MacGilvery A, Mader C, Maratukulam A, Matise TC, McKusick KB, Morissette J, Mungall A, Muselet D, Nusbaum HC, Page DC, Peck A, Perkins S, Piercy M, Qin F, Quackenbush J, Ranby S, Reif T, Rozen S, Sanders C, She X, Silva J, Slonim DK, Soderlund C, Sun WL, Tabar P, Thangarajah T, Vega-Czarny N, Vollrath D, Voyticky S, Wilmer T, Wu X, Adams MD, Auffray C, Walter NA, Brandon R, Dehejia A, Goodfellow PN, Houlgatte R, Hudson JR Jr, Ide SE, Iorio KR, Lee WY, Seki N, Nagase T, Ishikawa K, Nomura N, Phillips C, Polymeropoulos MH, Sandusky M, Schmitt K, Berry R, Swanson K, Torres R, Venter JC, Sikela JM, Beckmann JS, Weissenbach J, Myers RM, Cox DR, James MR, Bentley D, Deloukas P, Lander ES, Hudson TJ (1996) A gene map of the human genome. Science 274(5287):540–546

    Article  CAS  PubMed  Google Scholar 

  14. 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. https://doi.org/10.1038/nmeth.1226

    Article  CAS  PubMed  Google Scholar 

  15. Aziz MA, Yousef Z, Saleh AM, Mohammad S, Al Knawy B (2017) Towards personalized medicine of colorectal cancer. Crit Rev Oncol Hematol 118:70–78. https://doi.org/10.1016/j.critrevonc.2017.08.007

    Article  PubMed  Google Scholar 

  16. Dominguez A, Munoz E, Lopez MC, Cordero M, Martinez JP, Vinas M (2017) Transcriptomics as a tool to discover new antibacterial targets. Biotechnol Lett 39(6):819–828. https://doi.org/10.1007/s10529-017-2319-0

    Article  PubMed  CAS  Google Scholar 

  17. Flint SM, McKinney EF, Lyons PA, Smith KG (2015) The contribution of transcriptomics to biomarker development in systemic vasculitis and SLE. Curr Pharm Des 21(17):2225–2235

    Article  CAS  PubMed  Google Scholar 

  18. Gobert GN, Jones MK (2008) Discovering new schistosome drug targets: the role of transcriptomics. Curr Drug Targets 9(11):922–930

    Article  CAS  PubMed  Google Scholar 

  19. Granata S, Dalla Gassa A, Bellin G, Lupo A, Zaza G (2016) Transcriptomics: a step behind the comprehension of the polygenic influence on oxidative stress, immune deregulation, and mitochondrial dysfunction in chronic kidney disease. Biomed Res Int 2016:9290857. https://doi.org/10.1155/2016/9290857

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Kan M, Shumyatcher M, Himes BE (2017) Using omics approaches to understand pulmonary diseases. Respir Res 18(1):149. https://doi.org/10.1186/s12931-017-0631-9

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lillicrap D (2002) Gene expression: overview and clinical implications. Vox Sang 83 Suppl 1:77–79

    Article  PubMed  Google Scholar 

  22. Trapp J, McAfee A, Foster LJ (2017) Genomics, transcriptomics and proteomics: enabling insights into social evolution and disease challenges for managed and wild bees. Mol Ecol 26(3):718–739. https://doi.org/10.1111/mec.13986

    Article  PubMed  Google Scholar 

  23. Gao Y, Wang F, Eisinger BE, Kelnhofer LE, Jobe EM, Zhao X (2017) Integrative single-cell transcriptomics reveals molecular networks defining neuronal maturation during postnatal neurogenesis. Cereb Cortex 27(3):2064–2077. https://doi.org/10.1093/cercor/bhw040

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Geriatrics and Clinical Gerontology, National Institute on Aging, Bethesda, MD, USA

    Nalini Raghavachari

  2. Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA

    Natàlia Garcia-Reyero

Authors
  1. Nalini Raghavachari
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  2. Natàlia Garcia-Reyero
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Corresponding author

Correspondence to Natàlia Garcia-Reyero .

Editor information

Editors and Affiliations

  1. Division of Geriatrics and Clinical Gerontology, National Institute on Aging, Bethesda, Maryland, USA

    Nalini Raghavachari

  2. Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, Mississippi, USA

    Natàlia Garcia-Reyero

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Raghavachari, N., Garcia-Reyero, N. (2018). Overview of Gene Expression Analysis: Transcriptomics. In: Raghavachari, N., Garcia-Reyero, N. (eds) Gene Expression Analysis. Methods in Molecular Biology, vol 1783. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7834-2_1

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  • DOI: https://doi.org/10.1007/978-1-4939-7834-2_1

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7833-5

  • Online ISBN: 978-1-4939-7834-2

  • eBook Packages: Springer Protocols

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