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Chris Smith, PhD

Chris Smith, PhD

Chris Smith of Cambridge University, Tyson Clark of Affymetrix, and Melissa Cline of UC Santa Cruz discuss EURASNET's approach for comparing commercial microarrays for investigating alternative splicing

A European consortium of nearly 40 laboratories has conducted a study which identified GeneChip® Human Exon 1.0 ST Arrays as an effective tool for studying alternative splicing—a process that results in multiple isoforms of a single gene, and a major source of protein diversity.

It's interesting to imagine how these sorts of different mechanisms evolved. Obviously there is some reason why the cell does it, but that is still a mystery.

The study compared the ability of microarray platforms to investigate alternative splicing in transcripts regulated by the splicing factor polypyrimidine tract binding (PTB) protein, and its paralog, nPTB. Researchers hope that uncovering the mechanisms which regulate alternative splicing will lead to a better understanding of human disease.

The consortium, dubbed EURASNET (European Commission-funded Network of Excellence), found that GeneChip Human Exon 1.0 ST Arrays outperformed other splice-sensitive array platforms in a study which investigated alternative splicing in cancer (HeLa) cells. GeneChip Exon Arrays, which include approximately 1.4 million probe sets, allow for complementary high-resolution gene expression and alternative splicing analysis. By using these genome-wide arrays, EURASNET researchers were readily able to detect and confirm a total of 38 splicing events.

"Exon arrays are global," said Cambridge University's Dr. Chris Smith, a EURASNET member whose lab designed the study. "They feature a huge number of probe sets ranging from the very well-annotated exons to exons for which there is only a low level of confidence in the prediction. These arrays provide the necessary power for discovering new alternative splicing events."

After the data was collected by the EURASNET team, scientists at Affymetrix grouped the resulting hits by confidence levels using a simple algorithm that normalizes exon signal to the level of gene expression. Exon-level signal intensities reflect a combination of the number of transcripts made from a gene and the frequency in which a particular exon appears in those transcripts. Included in the ordered list of hits were 32 high-confidence splicing events. Smith's group used reverse transcription followed by real-time PCR to validate 95 percent of those events.

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