Two fundamental approaches are commonly used to analyze data sets from oligonucleotide microarrays tailored to identify genetic variation in specific DNA segments purely by hybridization (1, (3) (4) (5) 9) . One approach involves identifying statistically significant gains of target hybridization signal to oligonucleotide probes complementary to specific sequence variants (9) . In theory, the gain of signal approach has the advantage of both detecting the presence of genetic variation and identifying the nature of the sequence change in the target. However, it is not feasible to screen for virtually all possible insertions and deletions due to the overwhelming The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oupjournals.org number of mutation-specific probes needed for this analysis. Furthermore, little effort has been made to systematically access the hybridization properties of probes complementary to these small insertions and deletions. The second approach involves identifying losses of hybridization signal to perfect match (PM) probes that are fully complementary to the DNA segment of interest (8, 25, 27, 30, 31) . In theory, the loss of signal approach allows one to screen for all possible sequence changes, including insertions and deletions, that cause a given target nucleic acid sequence to contain mismatches with specific PM probes. However, this necessitates the sequencing of specific DNA regions to identify the nature of the sequence changes (8, 25, 27, 30, 31) . Thus, a combination of the gain and loss of hybridization signal analysis could provide the most robust means of identifying and characterizing mutations using non-enzymatic oligonucleotide microarray assays.