Widespread existence of uncorrelated probe intensities from within the same probeset on Affymetrix GeneChips.

We have developed a computational pipeline to analyse large surveys of Affymetrix GeneChips, for example NCBI's Gene Expression Omnibus. GEO samples data for many organisms, tissues and phenotypes. Because of this experimental diversity, any observed correlations between probe intensities can be associated either with biology that is robust, such as common co-expression, or with systematic biases associated with the GeneChip technology. Our bioinformatics pipeline integrates the mapping of probes to exons, quality control checks on each GeneChip which identifies flaws in hybridization quality, and the mining of correlations in intensities between groups of probes. The output from our pipeline has enabled us to identify systematic biases in GeneChip data. We are also able to use the pipeline as a discovery tool for biology. We have discovered that in the majority of cases, Affymetrix probesets on Human GeneChips do not measure one unique block of transcription. Instead we see numerous examples of outlier probes. Our study has also identified that in a number of probesets the mismatch probes are an informative diagnostic of expression, rather than providing a measure of background contamination. We report evidence for systematic biases in GeneChip technology associated with probe-probe interactions. We also see signatures associated with post-transcriptional processing of RNA, such as alternative polyadenylation.