A Comparative Study of the Impact of G-Stack Probes on Various Affymetrix GeneChips of Mammalia

We have previously discovered that probes containing runs of four or more contiguous guanines are not reliable for measuring gene expression in the Human HG_U133A Affymetrix GeneChip data. These probes are not correlated with other members of their probe set, but they are correlated with each other. We now extend our analysis to different3′GeneChip designs of mouse, rat, and human. We find that, in all these chip designs, the G-stack probes (probes with a run of exactly four consecutive guanines) are correlated highly with each other, indicating that such probes are not reliable measures of gene expression in mammalian studies. Furthermore, there is no specific position of G-stack where the correlation is highest in all the chips. We also find that the latest designs of rat and mouse chips have significantly fewer G-stack probes compared to their predecessors, whereas there has not been a similar reduction in G-stack density across the changes in human chips. Moreover, we find significant changes in RMA values (after removing G-stack probes) as the number of G-stack probes increases.

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

SummaryWe have developed a computational pipeline to analyse large surveys of A ymetrix 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, A ymetrix 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.