ArticleBMC bioinformatics2007
AffyMAPSDetector: a software tool to characterize Affymetrix GeneChip expression arrays with respect to SNPs.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Prognostic signatures associated with high infiltration of Tregs in bone metastatic prostate cancer.Aging · 2021Article
- The LO-BaFL method and ALS microarray expression analysis.BMC bioinformatics · 2012Article
- 'maskBAD'--a package to detect and remove Affymetrix probes with binding affinity differences.BMC bioinformatics · 2012Article
- Differentially expressed genes in hypothalamus in relation to genomic regions under selection in two chicken lines resulting from divergent selection for high or low body weight.Neurogenetics · 2011Article
- ArrayInitiative - a tool that simplifies creating custom Affymetrix CDFs.BMC bioinformatics · 2011Article
- Gene expression and genetic variation data implicate PCLO in bipolar disorder.Biological psychiatry · 2011Article
- A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data.Nucleic acids research · 2010Article
- A white-box approach to microarray probe response characterization: the BaFL pipeline.BMC bioinformatics · 2009Article
- Development and evaluation of new mask protocols for gene expression profiling in humans and chimpanzees.BMC bioinformatics · 2009Article
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3 authors.
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Abstract
backgroundAffymetrix gene expression arrays incorporate paired perfect match (PM) and mismatch (MM) probes to distinguish true signals from those arising from cross-hybridization events. A MM signal often shows greater intensity than a PM signal; we propose that one underlying cause is the presence of allelic variants arising from single nucleotide polymorphisms (SNPs). To annotate and characterize SNP contributions to anomalous probe binding behavior we have developed a software tool called AffyMAPSDetector.
resultsAffyMAPSDetector can be used to describe any Affymetrix expression GeneChip with respect to SNPs. When AffyMAPSDetector was run on GeneChip HG-U95Av2 against dbSNP-build-123, we found 7286 probes (belonging to 2,582 probesets) containing SNPs, out of which 325 probes contained at least one SNP at position 13. Against dbSNP-build-126, 8758 probes (belonging to 3,002 probesets) contained SNPs, of which 409 probes contained at least one SNP at position 13. Therefore, depending on the expressed allele, the MM probe can sometimes be the transcript complement. This information was used to characterize probe measurements reported in a published, well-replicated lung adenocarcinoma study. The total intensity distributions showed that the SNP-containing probes had a larger negative mean intensity difference (PM-MM) and greater range of the difference than did probes without SNPs. In the sample replicates, SNP-containing probes with reproducible intensity ratios were identified, allowing selection of SNP probesets that yielded unique sample signatures. At the gene expression level, use of the (MM-PM) value for SNP-containing probes resulted in different Presence/Absence calls for some genes. Such a change in status of the genes has the clear potential for influencing downstream clustering and classification results.
conclusionOutput from this tool characterizes SNP-containing probes on GeneChip microarrays, thus improving our understanding of factors contributing to expression measurements. The pattern of SNP binding examined so far indicates distinct behavior of the SNP-containing probes and has the potential to help us identify new SNPs. Knowing which probes contain SNPs provides flexibility in determining whether to include or exclude them from gene-expression intensity calculations; selected sets of SNP-containing probes produce sample-unique signatures. AffyMAPSDetector information is available at http://www.binf.gmu.edu/weller/BMC_bioinformatics/AffyMapsDetector/index.html.
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