ArticleBMC bioinformatics2008
Background correction using dinucleotide affinities improves the performance of GCRMA.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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3 authors.
Funding
Abstract
backgroundHigh-density short oligonucleotide microarrays are a primary research tool for assessing global gene expression. Background noise on microarrays comprises a significant portion of the measured raw data, which can have serious implications for the interpretation of the generated data if not estimated correctly.
resultsWe introduce an approach to calculate probe affinity based on sequence composition, incorporating nearest-neighbor (NN) information. Our model uses position-specific dinucleotide information, instead of the original single nucleotide approach, and adds up to 10% to the total variance explained (R2) when compared to the previously published model. We demonstrate that correcting for background noise using this approach enhances the performance of the GCRMA preprocessing algorithm when applied to control datasets, especially for detecting low intensity targets.
conclusionModifying the previously published position-dependent affinity model to incorporate dinucleotide information significantly improves the performance of the model. The dinucleotide affinity model enhances the detection of differentially expressed genes when implemented as a background correction procedure in GeneChip preprocessing algorithms. This is conceptually consistent with physical models of binding affinity, which depend on the nearest-neighbor stacking interactions in addition to base-pairing.
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