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Open Access Highly Accessed Research article

Transcript-level annotation of Affymetrix probesets improves the interpretation of gene expression data

Hui Yu1, Feng Wang12, Kang Tu14, Lu Xie1, Yuan-Yuan Li13* and Yi-Xue Li14*

Author Affiliations

1 Shanghai Center for Bioinformation Technology, Shanghai 200235, P. R. China

2 School of Life Science and Technology, Shanghai Jiaotong University, Shanghai 200240, P. R. China

3 Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China

4 Bioinformatics Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China

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BMC Bioinformatics 2007, 8:194  doi:10.1186/1471-2105-8-194

Published: 11 June 2007

Abstract

Background

The wide use of Affymetrix microarray in broadened fields of biological research has made the probeset annotation an important issue. Standard Affymetrix probeset annotation is at gene level, i.e. a probeset is precisely linked to a gene, and probeset intensity is interpreted as gene expression. The increased knowledge that one gene may have multiple transcript variants clearly brings up the necessity of updating this gene-level annotation to a refined transcript-level.

Results

Through performing rigorous alignments of the Affymetrix probe sequences against a comprehensive pool of currently available transcript sequences, and further linking the probesets to the International Protein Index, we generated transcript-level or protein-level annotation tables for two popular Affymetrix expression arrays, Mouse Genome 430A 2.0 Array and Human Genome U133A Array. Application of our new annotations in re-examining existing expression data sets shows increased expression consistency among synonymous probesets and strengthened expression correlation between interacting proteins.

Conclusion

By refining the standard Affymetrix annotation of microarray probesets from the gene level to the transcript level and protein level, one can achieve a more reliable interpretation of their experimental data, which may lead to discovery of more profound regulatory mechanism.