Adaptable gene‐specific dye bias correction for two‐channel DNA microarrays

• Published in: Molecular systems biology Vol. 5; no. 1; pp. 266 - n/a
• Main Authors: Margaritis, Thanasis, Lijnzaad, Philip, van Leenen, Dik, Bouwmeester, Diane, Kemmeren, Patrick, van Hooff, Sander R, Holstege, Frank CP
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.04.2009; John Wiley & Sons, Ltd; EMBO Press; Nature Publishing Group; Springer Nature
• Subjects: Base Sequence; Bias; Coloring Agents - analysis; Coloring Agents - metabolism; Deoxyribonucleic acid; DNA; DNA chips; DNA microarrays; DNA probes; Dyes; Efficiency; EMBO09; EMBO10; Experiments; fluorescent dye labelling; Gene expression; Genes; Hybridization; Labeling; Methods; mRNA expression profiling; normalization; Oligonucleotide Array Sequence Analysis - methods; Oligonucleotide Array Sequence Analysis - standards; Performance evaluation; Probes; Staining and Labeling - standards; two channel; United States > US; fluorescent dye labelling; DNA microarrays; normalization; mRNA expression profiling; two channel
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/msb.2009.21

DNA microarray technology is a powerful tool for monitoring gene expression or for finding the location of DNA‐bound proteins. DNA microarrays can suffer from gene‐specific dye bias (GSDB), causing some probes to be affected more by the dye than by the sample. This results in large measurement errors, which vary considerably for different probes and also across different hybridizations. GSDB is not corrected by conventional normalization and has been difficult to address systematically because of its variance. We show that GSDB is influenced by label incorporation efficiency, explaining the variation of GSDB across different hybridizations. A correction method (Gene‐ And Slide‐Specific Correction, GASSCO) is presented, whereby sequence‐specific corrections are modulated by the overall bias of individual hybridizations. GASSCO outperforms earlier methods and works well on a variety of publically available datasets covering a range of platforms, organisms and applications, including ChIP on chip. A sequence‐based model is also presented, which predicts which probes will suffer most from GSDB, useful for microarray probe design and correction of individual hybridizations. Software implementing the method is publicly available.