Background correction using dinucleotide affinities improves the performance of GCRMA

doi:10.1186/1471-2105-9-452

BMC Bioinformatics

Volume 9

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Fodor AA
Gibas CJ

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Methodology article

Background correction using dinucleotide affinities improves the performance of GCRMA

Raad Z Gharaibeh , Anthony A Fodor and Cynthia J Gibas

Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

author email corresponding author email

BMC Bioinformatics 2008, 9:452doi:10.1186/1471-2105-9-452


Published:	23 October 2008

Additional files

Additional File 1:

Boxplots showing the R²of the single nucleotide model (N) (using the 100 free parameters (N100), equation 1, and the 16 free parameters (N16), equation 2) and the dinucleotide model with 64 free parameters (NN 64), equation 4 on the 42 Latin square chips. PM indicates the fit was done on the perfect match probes, MM indicates the fit was done on the mismatch probes, shuffled indicates the fit was done on the shuffled probe sequences and random indicates the fit was done on randomly generated probe sequences.

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