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Open Access Highly Accessed Research article

Integrating multiple genome annotation databases improves the interpretation of microarray gene expression data

Jun Yin1*, Sarah McLoughlin2, Ian B Jeffery1, Antonino Glaviano2, Breandan Kennedy2 and Desmond G Higgins1

Author Affiliations

1 School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland

2 School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Dublin, Ireland

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BMC Genomics 2010, 11:50  doi:10.1186/1471-2164-11-50

Published: 20 January 2010

Abstract

Background

The Affymetrix GeneChip is a widely used gene expression profiling platform. Since the chips were originally designed, the genome databases and gene definitions have been considerably updated. Thus, more accurate interpretation of microarray data requires parallel updating of the specificity of GeneChip probes. We propose a new probe remapping protocol, using the zebrafish GeneChips as an example, by removing nonspecific probes, and grouping the probes into transcript level probe sets using an integrated zebrafish genome annotation. This genome annotation is based on combining transcript information from multiple databases. This new remapping protocol, especially the new genome annotation, is shown here to be an important factor in improving the interpretation of gene expression microarray data.

Results

Transcript data from the RefSeq, GenBank and Ensembl databases were downloaded from the UCSC genome browser, and integrated to generate a combined zebrafish genome annotation. Affymetrix probes were filtered and remapped according to the new annotation. The influence of transcript collection and gene definition methods was tested using two microarray data sets. Compared to remapping using a single database, this new remapping protocol results in up to 20% more probes being retained in the remapping, leading to approximately 1,000 more genes being detected. The differentially expressed gene lists are consequently increased by up to 30%. We are also able to detect up to three times more alternative splicing events. A small number of the bioinformatics predictions were confirmed using real-time PCR validation.

Conclusions

By combining gene definitions from multiple databases, it is possible to greatly increase the numbers of genes and splice variants that can be detected in microarray gene expression experiments.