Experimental optimization of probe length to increase the sequence specificity of high-density oligonucleotide microarrays
- Equal contributors
1 Department of Bioinformatics Engineering, Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
2 Complex Systems Biology Project, ERATO, Japan Science and Technology Corporation, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
3 Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
BMC Genomics 2007, 8:373 doi:10.1186/1471-2164-8-373Published: 16 October 2007
High-density oligonucleotide arrays are widely used for analysis of genome-wide expression and genetic variation. Affymetrix GeneChips – common high-density oligonucleotide arrays – contain perfect match (PM) and mismatch (MM) probes generated by changing a single nucleotide of the PMs, to estimate cross-hybridization. However, a fraction of MM probes exhibit larger signal intensities than PMs, when the difference in the amount of target specific hybridization between PM and MM probes is smaller than the variance in the amount of cross-hybridization. Thus, pairs of PM and MM probes with greater specificity for single nucleotide mismatches are desirable for accurate analysis.
To investigate the specificity for single nucleotide mismatches, we designed a custom array with probes of different length (14- to 25-mer) tethered to the surface of the array and all possible single nucleotide mismatches, and hybridized artificially synthesized 25-mer oligodeoxyribonucleotides as targets in bulk solution to avoid the effects of cross-hybridization. The results indicated the finite availability of target molecules as the probe length increases. Due to this effect, the sequence specificity of the longer probes decreases, and this was also confirmed even under the usual background conditions for transcriptome analysis.
Our study suggests that the optimal probe length for specificity is 19–21-mer. This conclusion will assist in improvement of microarray design for both transcriptome analysis and mutation screening.