ZFIN ID: ZDB-PUB-100126-9
Integrating multiple genome annotation databases improves the interpretation of microarray gene expression data
Yin, J., McLoughlin, S., Jeffery, I.B., Glaviano, A., Kennedy, B., and Higgins, D.G.
Date: 2010
Source: BMC Genomics   11: 50 (Journal)
Registered Authors: Kennedy, Breandan N., McLoughlin, Sarah, Yin, Jun
Keywords: none
Microarrays: GEO:GSE19320
MeSH Terms:
  • Alternative Splicing
  • Animals
  • Chromosome Mapping/methods
  • Computational Biology
  • DNA Probes
  • Databases, Genetic*
  • Gene Expression Profiling/methods*
  • Oligonucleotide Array Sequence Analysis/methods*
  • Sequence Alignment
  • Sequence Analysis, DNA/methods
  • Zebrafish/genetics
PubMed: 20089164 Full text @ BMC Genomics
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.