Validation of Oligoarrays for Quantitative Exploration of the Transcriptome

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Validation of Oligoarrays for Quantitative Exploration of the Transcriptome

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Title: Validation of Oligoarrays for Quantitative Exploration of the Transcriptome
Author: Nygaard, Vigdis; Holden, Marit; Trimarchi, Jeff; Ohno-Machado, Lucila; Frigessi, Arnoldo; Glad, Ingrid K; Wiel, Mark A van de; Hovig, Eivind; Lyng, Heidi; Liu, Fang; Kuo, Winston Patrick; Cepko, Connie

Note: Order does not necessarily reflect citation order of authors.

Citation: Nygaard, Vigdis, Fang Liu, Marit Holden, Winston P. Kuo, Jeff Trimarchi, Lucila Ohno-Machado, Connie L. Cepko, et al. 2008. Validation of oligoarrays for quantitative exploration of the transcriptome. BMC Genomics 9: 258.
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Abstract: Background: Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5·105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE. Results: A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8·105 (oligoarrays), 1.1·105 (MPSS) and 7.6·104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1·106 (oligoarrays), 2.8·105 (MPSS) and 3.8·105 (SAGE). Conclusion: The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5·105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.
Published Version: doi:10.1186/1471-2164-9-258
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2430212/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4866542

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