Background correction using dinucleotide affinities improves the performance of GCRMA.

TitleBackground correction using dinucleotide affinities improves the performance of GCRMA.
Publication TypeJournal Article
Year of Publication2008
AuthorsGharaibeh RZ, Fodor AA, Gibas CJ
JournalBMC Bioinformatics
Volume9
Pagination452
Date Published2008 Oct 23
ISSN1471-2105
KeywordsAnimals, Artifacts, Cluster Analysis, Computational Biology, Energy Transfer, Gene Expression Profiling, Humans, Oligonucleotide Array Sequence Analysis, Oligonucleotide Probes, Reproducibility of Results
Abstract

<p><b>BACKGROUND: </b>High-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.</p><p><b>RESULTS: </b>We 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.</p><p><b>CONCLUSION: </b>Modifying 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.</p>

DOI10.1186/1471-2105-9-452
Alternate JournalBMC Bioinformatics
PubMed ID18947404
PubMed Central IDPMC2579310
Grant ListR01 GM072619 / GM / NIGMS NIH HHS / United States
1R01GM072619-01 / GM / NIGMS NIH HHS / United States