RT Journal
A1 Hu, Gang Ken
A1 Madore, Steven J
A1 Moldover, Brian
A1 Jatkoe, Tim
A1 Balaban, David
A1 Thomas, Jeffrey
A1 Wang, Yixin
T1 Predicting Splice Variant from DNA Chip Expression Data
JF Genome Research 
JO Genome Research 
YR 2001 
FD July 01 
VO 11 
IS 7 
SP 1237 
OP 1245 
DO 10.1101/gr.165501 
UL http://genome.cshlp.org/content/11/7/1237.abstract 
AB 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.