Comparison of hybridization-based and sequencing-based gene expression technologies on biological replicates
1 Department of Tumor Biology, Rikshopitalet-Radiumhospitalet Medical Center, Montebello, NO-0310 Oslo, Norway
2 Department of Medical Informatics, Rikshopitalet-Radiumhospitalet Medical Center, Montebello, NO-0310 Oslo, Norway
3 PubGene AS, Vinderen, NO-0319 Oslo, Norway
4 Department of Genetics, Harvard Medical School, Boston, MA, USA
5 Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
6 Decision Systems Group, Brigham and Women's Hospital, Boston, MA, USA
7 Department of Developmental Biology, Harvard School of Dental Medicine, Boston, MA, USA
8 Department of Organismic and Evolutionary Biology/Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
BMC Genomics 2007, 8:153 doi:10.1186/1471-2164-8-153Published: 7 June 2007
High-throughput systems for gene expression profiling have been developed and have matured rapidly through the past decade. Broadly, these can be divided into two categories: hybridization-based and sequencing-based approaches. With data from different technologies being accumulated, concerns and challenges are raised about the level of agreement across technologies. As part of an ongoing large-scale cross-platform data comparison framework, we report here a comparison based on identical samples between one-dye DNA microarray platforms and MPSS (Massively Parallel Signature Sequencing).
The DNA microarray platforms generally provided highly correlated data, while moderate correlations between microarrays and MPSS were obtained. Disagreements between the two types of technologies can be attributed to limitations inherent to both technologies. The variation found between pooled biological replicates underlines the importance of exercising caution in identification of differential expression, especially for the purposes of biomarker discovery.
Based on different principles, hybridization-based and sequencing-based technologies should be considered complementary to each other, rather than competitive alternatives for measuring gene expression, and currently, both are important tools for transcriptome profiling.