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Vol. 11, Issue 7, 1237-1245, July 2001

METHODS
Predicting Splice Variant from DNA Chip Expression Data

Gang Ken Hu,1,5 Steven J Madore,1 Brian Moldover,1,3 Tim Jatkoe,1,4 David Balaban,2 Jeffrey Thomas,1 and Yixin Wang1,4

1 Bioinformatics, Department of Molecular Science, Pfizer Global Research and Development, Ann Arbor Laboratories, Ann Arbor, Michigan 48105, USA; 2 Department of Bioinformatics, Affymetrix Inc., Santa Clara, California 95051, USA

Alternative splicing of premessenger RNA is an important layer of regulation in eukaryotic gene expression. Splice variation of a large number of genes has been implicated in various cell growth and differentiation processes. To measure tissue-specific splicing of genes on a large scale, we collected gene expression data from 11 rat tissues using a high-density oligonucleotide array representing 1600 rat genes. Expression of each gene on the chip is measured by 20 pairs of independent oligonucleotide probes. Two algorithms have been developed to normalize and compare the chip hybridization signals among different tissues at individual oligonucleotide probe level. Oligonucleotide probes (the perfect match [PM] probe of each probe pair), detecting potential tissue-specific splice variants, were identified by the algorithms. The identified candidate splice variants have been compared to the alternatively spliced transcripts predicted by an EST clustering program. In addition, 50% of the top candidates predicted by the algorithms were confirmed by RT-PCR experiment. The study indicates that oligonucleotide probe-based DNA chip assays provide a powerful approach to detect splice variants at genome scale.

Present addresses: 3Aventis Pharmaceuticals, NJ, USA; 4Johnson and Johnson Company, CA , USA.

5 Corresponding author.

11:1237-1245 ©2001 by Cold Spring Harbor Laboratory Press  ISSN 1088-9051/01 $5.00
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