Quantitative miRNA expression analysis: comparing microarrays with next-generation sequencing
Abstract
Recently, next-generation sequencing has been introduced as a promising, new platform for assessing the copy number of transcripts, while the existing microarray technology is considered less reliable for absolute, quantitative expression measurements. Nonetheless, so far, results from the two technologies have only been compared based on biological data, leading to the conclusion that, although they are somewhat correlated, expression values differ significantly. Here, we use synthetic RNA samples, resembling human microRNA samples, to find that microarray expression measures actually correlate better with sample RNA content than expression measures obtained from sequencing data. In addition, microarrays appear highly sensitive and perform equivalently to next-generation sequencing in terms of reproducibility and relative ratio quantification.
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miRNA profiling results obtained using different platforms can vary dramatically for individual miRNAs despite good overall inter-platform correlation.
“In addition to the pre-analytical potential challenges, researchers from many laboratories have now found that miRNA profiling results obtained using different platforms can vary dramatically for individual miRNAs despite good overall inter-platform correlation [17–21].”
From Expression of miR-15/107 Family MicroRNAs in Human Tissues and Cultured Rat Brain Cells (2014) · cites this paper as [21]
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Research works printed but not linked: 7
- Arikawa E, Sun Y, Wang J, Zhou Q, Ning B, Dial SL, Guo L, Yang J. 2008. Cross-platform comparison of SYBR Ò Green real-time PCR with TaqMan PCR, microarrays and other gene expression measurement technologies evaluated in the MicroArray Quality Control (MAQC) study. BMC Genomics 9: 328. doi: 10.1186/1471-2164-9-328. · DOI
- Beissbarth T, Hyde L, Smyth GK, Job C, Boon WM, Tan SS, Scott HS, Speed TP. 2004. Statistical modeling of sequencing errors in SAGE libraries. Bioinformatics (Suppl 1) 20: i31-i39.
- Chen J, Agrawal V, Rattray M, West MA, St Clair DA, Michelmore RW, Coughlan SJ, Meyers BC. 2007. A comparison of microarray and MPSS technology platforms for expression analysis of Arabidopsis. BMC Genomics 8: 414. doi: 10.1186/1471- 2164-8-414. · DOI
- Dohm JC, Lottaz C, Borodina T, Himmelbauer H. 2008. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res 36: e105. doi: 10.1093/nar/gkn425. · DOI · Paper page
- Langmead B, Trapnell C, Pop M, Salzberg SL. 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10: R25. doi: 10.1186/gb-2009-10- 3-r25. · DOI · Paper page
- Liu F, Jenssen TK, Trimarchi J, Punzo C, Cepko CL, Ohno- Machado L, Hovig E, Kuo WP. 2007. Comparison of hybridiza- tion-based and sequencing-based gene expression technologies on biological replicates. BMC Genomics 8: 153. doi: 10.1186/1471- 2164-8-153. · DOI
- Hoen PA, Ariyurek Y, Thygesen HH, Vreugdenhil E, Vossen RH, de Menezes RX, Boer JM, van Ommen GJ, den Dunnen JT. 2008. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five micro- array platforms. Nucleic Acids Res 36: e141. doi: 10.1093/nar/ gkn705. · DOI · Paper page
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Expression of miR-15/107 Family MicroRNAs in Human Tissues and Cultured Rat Brain Cells
Wang, W.-X., Danaher, R. J., Miller, C. S., Berger, J. R., Nubia, V. G., Wilfred, B. S., Neltner, J. H., Norris, C. M., & Nelson, P. T. (2014) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2013.10.003 · Cited by 67 · see citing works
- Authors
- Willenbrock, H., Salomon, J., Søkilde, R., Barken, K. B., Hansen, T. N., Nielsen, F. C., Møller, S., & Litman, T.
- Venue
- RNA
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2009-09-10
- Volume
- 15
- Issue
- 11
- Pages
- 2028-2034
- ISSN
- 1355-8382
- 1469-9001
- Cited by
- 144
- References
- 0
- Language
- en
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