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.
Similar content being viewed by others
References
Making the most of microarrays. Nature Biotechnology, 24, 1039 (2006)
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.
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.
FDA Clears Breast Cancer Specific Molecular Prognostic Test. Retrieved from http://www.fda.gov/bbs/topics/NEWS/2007/NEW01555.html.
FDA Clears Test that Helps Identify Type of Cancer in Tumor Sample. Retrieved from http://www.fda.gov/bbs/topics/NEWS/2008/NEW01870.html.
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.
Shendure, J. (2008). The beginning of the end for microarrays? Nature Methods, 5, 585–587.
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.
Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995). Serial analysis of gene expression. Science, 270, 484–487.
Kahvejian, A., Quackenbush, J., & Thompson, J. F. (2008). What would you do if you could sequence everything? Nature Biotechnology, 26, 1125–1133.
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.
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.
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.
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.
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.
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.
Acknowledgments
This study was supported by the Chinese State Key Projects for Basic Research (2004CB518707) and Beijing Municipal Key Project (D0905001040731). We would like to thank Dr. Ting Xiao for her helpful discussion and support, Bangrong Cao for his help with the analysis, and Bing Ling for his advice on biological experiments.
Author information
Authors and Affiliations
Corresponding authors
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Feng, L., Liu, H., Liu, Y. et al. Power of Deep Sequencing and Agilent Microarray for Gene Expression Profiling Study. Mol Biotechnol 45, 101–110 (2010). https://doi.org/10.1007/s12033-010-9249-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12033-010-9249-6