Microarrays pp 213-225 | Cite as

Design and Fabrication of Spotted Long Oligonucleotide Microarrays for Gene Expression Analysis

  • Cheng-Chung Chou
  • Konan Peck
Part of the Methods in Molecular Biology™ book series (MIMB, volume 381)


DNA microarray technology has advanced rapidly since the first use of cDNA microarrays almost a decade ago. For gene expression studies on organisms, for which the genomes have been sequenced, cDNA microarrays are being gradually replaced by gene-specific oligonucleotide microarrays. Although, cDNA microarrays give higher signal intensity than oligonucleotide microarrays, they cannot be used for the measurement of gene-specific expression, whereas, oligonucleotide microarrays can. To obtain both a high signal intensity and specificity in gene expression measurements, gene-specific oligonucleotide probes as long as 150-mers, designed using sequence databases and algorithms to identify unique sequences of genes, are used as microarray probes. In order to achieve a high signal intensity, specificity, and accurate measurement of expression, in addition to the length and sequence of the probes, it is necessary to optimize other parameters such as the surface chemistry of the microarray slides, the addition of spacers and linkers to the probes, and the composition of the hybridization solution.

Key Words

Gene-specific microarray gene expression microarray probe oligonucleotide microarray probe design probe optimization 


  1. 1.
    Schena, M., Shalon, D., Davis, R. W., and Brown, P. O. (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–470.CrossRefGoogle Scholar
  2. 2.
    Lockhart, D. J., Dong, H., Byrne, M. C., et al. (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 14, 1675–1680.CrossRefGoogle Scholar
  3. 3.
    Holloway, A. J., Van Laar, R. K., Tothill, R. W., and Bowtell, D. D. (2002) Options available-from start to finish-for obtaining data from DNA microarrays II. Nat. Genet. 32, 481–489.CrossRefGoogle Scholar
  4. 4.
    Kuo, W. P., Jenssen, T. K., Butte, A. J., Ohno-Machado, L., and Kohane, I. S. (2002) Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18, 405–412.CrossRefGoogle Scholar
  5. 5.
    Zhang, L., Zhou, W., Velculescu, V. E., et al. (1997) Gene expression profiles in normal and cancer cells. Science 276, 1268–1272.CrossRefGoogle Scholar
  6. 6.
    Jordan, B. R. (2004) How consistent are expression chip platforms? Bioassays 26, 1236–1242.CrossRefGoogle Scholar
  7. 7.
    Selinger, D. W., Cheung, K. J., Mei, R., et al. (2000) RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nat. Biotechnol. 18, 1262–1268.CrossRefGoogle Scholar
  8. 8.
    Chou, C.-C., Chen, C.-H., Lee, T.-T., and Peck, K. (2004) Optimization of probe length and the number of probes per gene for optimal microarray analysis of gene expression. Nucleic Acids Res. 32, E99.CrossRefGoogle Scholar
  9. 9.
    Wang, E., Miller, L. D., Ohnmacht, G. A., Liu, E. T., and Marincola, F. M. (2000) High-fidelity mRNA amplification for gene profiling. Nat. Biotechnol. 18, 457–459.CrossRefGoogle Scholar
  10. 10.
    Hughes, T. R., Mao, M., Jones, A. R., et al. (2001) Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat. Biotechnol. 19, 342–347.CrossRefGoogle Scholar
  11. 11.
    Relogio, A., Schwager, C., Richter, A., Ansorge, W., and Valcarcel, J. (2002) Optimization of oligonucleotide-based DNA microarrays. Nucleic Acids Res. 30, E51.CrossRefGoogle Scholar
  12. 12.
    Olson, M., Hood, L., Cantor, C., and Botstein, D. (1989) A common language for physical mapping of the human genome. Science 245, 1434–1435.CrossRefGoogle Scholar
  13. 13.
    Pesole, G., Liuni, S., Grillo, G., et al. (2002) UTRdb and UTRsite: specialized databases of sequences and functional elements of 5′ and 3′ untranslated regions of eukaryotic mRNAs. Update 2002. Nucleic Acids Res. 30, 335–340CrossRefGoogle Scholar
  14. 14.
    Lee, Y., Tsai, J., Sunkara, S., et al. (2005) The TIGR Gene Indices: clustering and assembling EST and known genes and integration with eukaryotic genomes. Nucleic Acids Res. 33, D71–D74.CrossRefGoogle Scholar
  15. 15.
    Kane, M. D., Jatkoe, T. A., Stumpf, C. R., Lu, J., Thomas, J. D., and Madore, S. J. (2000) Assessment of the sensitivity and specificity of oligonucleotide (50-mer) microarrays. Nucleic Acids Res. 28, 4552–4557.CrossRefGoogle Scholar
  16. 16.
    Delcher, A. L., Phillippy, A., Carlton, J., and Salzberg, S. L. (2002) Fast algorithms for large-scale genome alignment and comparison. Nucleic Acids Res. 30, 2478–2483.CrossRefGoogle Scholar
  17. 17.
    Walter, A. E., Turner, D. H., Kim, J., et al. (1994) Coaxial stacking of helixes enhances binding of oligoribonucleotides and improves predictions of RNA folding. Proc. Natl. Acad. Sci. USA 91, 9218–9222.CrossRefGoogle Scholar
  18. 18.
    Schuler, G. D. (1997) Sequence mapping by electronic PCR. Genome Res. 7, 541–550.Google Scholar
  19. 19.
    Southern, E., Mir, K., and Shchepinov, M. (1999) Molecular interactions on microarrays. Nat. Genet. 21, 5–9.CrossRefGoogle Scholar
  20. 20.
    Xiang, C. C., Kozhich, O. A., Chen, M., et al. (2002) Amine-modified random primers to label probes for DNA microarrays. Nat. Biotechnol. 20, 738–742.CrossRefGoogle Scholar

Copyright information

© Humana Press Inc., Totowa, NJ 2007

Authors and Affiliations

  • Cheng-Chung Chou
    • 1
  • Konan Peck
    • 2
  1. 1.Department of Life Science and Institute of Molecular BiologyNational Chung Cheng UniversityChia-YiTaiwan, Republic of China
  2. 2.Institute of Biomedical SciencesAcademia SinicaTaipeiTaiwan, Republic of China

Personalised recommendations