Abstract
BackgroundGenome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.ResultsGATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0).ConclusionsGATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments.
Highlights
Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology
This analysis indicated that 70.5% of the assigned non-coding RNAs (ncRNAs) showed differential expression with p-values < 0.01 (p-values corrected by Bonferroni method). This means that 4,274 ncRNAs changed in all replicates in at least one set of tissues. These results reveal the importance of considering expression signals coming from ncRNAs in transcriptomic studies
The most reproducible and widely used microarrays are high-density oligonucleotide microarrays, which feature synthetic oligos based on cDNA and EST sequences
Summary
Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. Logical databases do not integrate expression signal data and they do not provide tools to use up-to-date probe mapping with query expression datasets. Databases such as GEO include large collections of expression datasets with powerful analysis tools, but they lack microarray probe mapping at nucleotide level and presentation in a genomic context, and instead consider "probesets" as genes [5]. It provides the possibility to discriminate between alternate isoforms of the same gene Such analyses require unambiguous assignment of the array probes to the functional entities defined in current transcriptomes (i.e. gene loci, transcripts, exons, ncRNAs), including their specific genomic location. The application integrates information from multiple biological sources and includes several bioinformatic tools to allow a novel perspective and interpretation of microarray expression data
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