Molecular Biotechnology

, Volume 45, Issue 2, pp 101–110 | Cite as

Power of Deep Sequencing and Agilent Microarray for Gene Expression Profiling Study

  • Lin Feng
  • Hang Liu
  • Yu Liu
  • Zhike Lu
  • Guangwu Guo
  • Suping Guo
  • Hongwei Zheng
  • Yanning Gao
  • Shujun Cheng
  • Jian Wang
  • Kaitai Zhang
  • Yong Zhang
Research

Abstract

Next-generation sequencing-based Digital Gene Expression tag profiling (DGE) has been used to study the changes in gene expression profiling. To compare the quality of the data generated by microarray and DGE, we examined the gene expression profiles of an in vitro cell model with these platforms. In this study, 17,362 and 15,938 genes were detected by microarray and DGE, respectively, with 13,221 overlapping genes. The correlation coefficients between the technical replicates were >0.99 and the detection variance was <9% for both platforms. The dynamic range of microarray was fixed with four orders of magnitude, whereas that of DGE was extendable. The consistency of the two platforms was high, especially for those abundant genes. It was more difficult for the microarray to distinguish the expression variation of less abundant genes. Although microarrays might be eventually replaced by DGE or transcriptome sequencing (RNA-seq) in the near future, microarrays are still stable, practical, and feasible, which may be useful for most biological researchers.

Keywords

Gene expression profiling Digital Gene Expression tag profiling (DGE) Microarray Real-time PCR Differentially expressed genes 

Supplementary material

12033_2010_9249_MOESM1_ESM.xls (7.5 mb)
(XLS 7685 kb)

References

  1. 1.
    Making the most of microarrays. Nature Biotechnology, 24, 1039 (2006)Google Scholar
  2. 2.
    Kuo, W. P., Liu, F., Trimarchi, J., Punzo, C., Lombardi, M., Sarang, J., et al. (2006). A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies. Nature Biotechnology, 24, 832–840.CrossRefGoogle Scholar
  3. 3.
    van’t Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A., Mao, M., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530–536.CrossRefGoogle Scholar
  4. 4.
    FDA Clears Breast Cancer Specific Molecular Prognostic Test. Retrieved from http://www.fda.gov/bbs/topics/NEWS/2007/NEW01555.html.
  5. 5.
    FDA Clears Test that Helps Identify Type of Cancer in Tumor Sample. Retrieved from http://www.fda.gov/bbs/topics/NEWS/2008/NEW01870.html.
  6. 6.
    Irizarry, R. A., Warren, D., Spencer, F., Kim, I. F., Biswal, S., Frank, B. C., et al. (2005). Multiple-laboratory comparison of microarray platforms. Nature Methods, 2, 345–350.CrossRefGoogle Scholar
  7. 7.
    Shendure, J. (2008). The beginning of the end for microarrays? Nature Methods, 5, 585–587.CrossRefGoogle Scholar
  8. 8.
    Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008). RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18, 1509–1517.CrossRefGoogle Scholar
  9. 9.
    Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995). Serial analysis of gene expression. Science, 270, 484–487.CrossRefGoogle Scholar
  10. 10.
    Kahvejian, A., Quackenbush, J., & Thompson, J. F. (2008). What would you do if you could sequence everything? Nature Biotechnology, 26, 1125–1133.CrossRefGoogle Scholar
  11. 11.
    Harismendy, O., Ng, P. C., Strausberg, R. L., Wang, X., Stockwell, T. B., Beeson, K. Y., et al. (2009). Evaluation of next generation sequencing platforms for population targeted sequencing studies. Genome Biology, 10, R32.CrossRefGoogle Scholar
  12. 12.
    Livak, K. J., & Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25, 402–408.CrossRefGoogle Scholar
  13. 13.
    Wilhelm, B. T., Marguerat, S., Watt, S., Schubert, F., Wood, V., Goodhead, I., et al. (2008). Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature, 453, 1239–1243.CrossRefGoogle Scholar
  14. 14.
    t Hoen, P. A., Ariyurek, Y., Thygesen, H. H., Vreugdenhil, E., Vossen, R. H., de Menezes, R. X., et al. (2008). Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Research, 36, e141.CrossRefGoogle Scholar
  15. 15.
    Thellin, O., Zorzi, W., Lakaye, B., De Borman, B., Coumans, B., Hennen, G., et al. (1999). Housekeeping genes as internal standards: Use and limits. Journal of Biotechnology, 75, 291–295.CrossRefGoogle Scholar
  16. 16.
    Chen, H., & Sharp, B. M. (2002). Oliz, a suite of Perl scripts that assist in the design of microarrays using 50-mer oligonucleotides from the 3′ untranslated region. BMC Bioinformatics, 3, 27.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Lin Feng
    • 1
  • Hang Liu
    • 2
  • Yu Liu
    • 1
  • Zhike Lu
    • 2
  • Guangwu Guo
    • 2
  • Suping Guo
    • 1
  • Hongwei Zheng
    • 1
  • Yanning Gao
    • 1
  • Shujun Cheng
    • 1
  • Jian Wang
    • 2
  • Kaitai Zhang
    • 1
  • Yong Zhang
    • 2
  1. 1.State Key Laboratory of Molecular Oncology, Cancer Institute (Hospital), Peking Union Medical CollegeChinese Academy of Medical SciencesBeijingPeople’s Republic of China
  2. 2.BGI Shenzhen, Bei Shan Industrial ZoneShenzhenPeople’s Republic of China

Personalised recommendations