Abstract
The sequencing of the human genome and other organisms has been accompanied by major methodological and scientific advances in biology and molecular genetics technologies. Currently, in the post-genomic era, it is expected that the data accumulated for over 15 years of projects are finally translated into practical applications. This has generated a growing interest in the scientific community and a series of expectations about future applications of genetics in the understanding and diagnosis of complex diseases like cancer, diabetes, psychiatric and neurological disorders in general.
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References
Chuaqui RF, et al. Post-analysis follow-up and validation of microarray experiments. Nat Genet. 2002;32:509–14.
Fodor SP, et al. Multiplexed biochemical assays with biological chips. Nature. 1993;364:555–6.
Göhlmann H, Talloen W. Gene expression studies using affymetrix microarrays. 1st ed. Chapman & Hall/CRC: Boca Raton; 2009.
Guindalini CSC, Tufik S. Use of microarrays in the search of gene expression patterns—application to the study of complex phenotypes. Rev Bras Psiquiatr. 2007;29:370–4.
Jafari P, Azuaje F. An assessment of recently published gene expression data analyses: reporting experimental design and statistical factors. BMC Med Inform Decis Mak. 2006;6:27.
Kendziorski CM, et al. The efficiency of pooling mRNA in microarray experiments. Biostatistics. 2003;4:465–77.
Koremberg MJ. Microarrays data analysis: methods and applications, Series methods in molecular biology, vol. 377. New York: Humana Press; 2007.
Morey JS, et al. Microarray validation: factors influencing correlation between oligonucleotide microarrays and real-time PCR. Biol Proced Online. 2006;8:175–93.
Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467–70.
Shendure J. The beginning of the end for microarrays? Nat Methods. 2008;5:585–7.
Shi L, et al. Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential. Curr Opin Biotechnol. 2008a;19:10–8.
Shi L, et al. The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies. BMC Bioinformatics. 2008b;9:S10.
Shih JH, et al. Effects of pooling mRNA in microarray class comparisons. Bioinformatics. 2004;20:3318–25.
Slonim DK, Yanai I. Getting started in gene expression microarray analysis. PLoS Comput Biol. 2009;5:1–4.
Stanislav M, et al. Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics. 2005;6:214.
Zhang SD, Gant TW. A statistical framework for the design of microarray experiments and effective detection of differential gene expression. Bioinformatics. 2004;20:2821–8.
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Guindalini, C., Pellegrino, R. (2016). Gene Expression Studies Using Microarrays. In: Andersen, M., Tufik, S. (eds) Rodent Model as Tools in Ethical Biomedical Research. Springer, Cham. https://doi.org/10.1007/978-3-319-11578-8_13
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DOI: https://doi.org/10.1007/978-3-319-11578-8_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11577-1
Online ISBN: 978-3-319-11578-8
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