Research article
Validation of oligoarrays for quantitative exploration of the transcriptome
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* Corresponding author: Heidi Lyng heidi.lyng@rr-research.no
- † Equal contributors
1 Department of Tumor Biology, Institute for Cancer Research, Norwegian Radium Hospital, Montebello, Oslo, Norway
2 PubGene AS, Vinderen, Oslo, Norway
3 Norwegian Computing Center, Oslo, Norway
4 Department of Developmental Biology, Harvard School of Dental Medicine, Boston, MA, USA
5 Laboratory for Innovative Translational Technologies, Harvard School of Dental Medicine, Boston, MA, USA
6 Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA
7 Department of Genetics, Harvard Medical School, Boston, MA, USA
8 Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
9 Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway
10 Department of Mathematics, University of Oslo, Norway
11 Department of Mathematics, Vrije Universiteit Amsterdam, The Netherlands
12 Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
13 Department of Medical Informatics, Institute for Cancer Research, Norwegian Radium Hospital, Montebello, Oslo, Norway
14 Department of Radiation Biology, Institute for Cancer Research, Norwegian Radium Hospital, Montebello, Norway
BMC Genomics 2008, 9:258 doi:10.1186/1471-2164-9-258
Published: 30 May 2008Abstract
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.