Background correction using dinucleotide affinities improves the performance of GCRMA

Background: 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. Results: 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. Conclusion: Modifying the previously published position-dependent affinity model to incorporate dinuc...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Raad Z. Gharaibeh, Anthony Fodor, Cynthia Gibas
• Keywords: Affinity Model, Background Noise, BMCBI 2008, Gene Expression