BMC Genomics 2008, 9:230 http://www.biomedcentral.com/1471-2164/9/230 distributions (Figure 2) and accuracy (top row in Figure 3), although these were due to the decrease of different types of error. When all spots are analyzed together, the mix control is responsible for higher specificity, without loss of sensitiv- ity. In the hybridizations with the T control, the increased false positives are mainly G spots, when the test sample was R, or R spots when the test sample was G. These spots have an expected ratio of 1, which is also the expected ratio of the more abundant spot type TRG that will have more weight in defining the value of the optimal thresh- old. A good threshold for correctly identifying the present TRG spots is naturally a bad threshold for identifying the absent G spots (or R spots, according to the test strain), whose LR distributions are almost completely overlap- ping (Figure 1). If spots are analyzed in different classes, the mix control is associated with a gain in sensitivity higher than the loss in specificity. Most of the divergence in the results of each control option is related with spots that recognize genes in the genome of two strains. In hybridizations with both types of control, the LR distributions have greater overlaps among spot types that are specific for two reference strains as compared with spot types that are specific for just one strain (Figure 1), which could be related to the fact that oligos designed for multiple strains can more easily present lower identities with their targets than oligos designed for ORFs present in just one strain. Hybridiza- tions with the T control produce less false present calls while arrays with mix control have less false absent calls. To obtain further insights into this behavior we looked at the individual sets of spots that are in each class analyzed separately. In hybridizations with the T control one optimal thresh- old is chosen from the LR values of the spot class recogniz- ing T genes (T, TR and TG spots) while another threshold is adopted for the spot class not recognizing T genes (R, G and RG spots). This division joins in the same class sets of spots specific for one and two reference strains, creating an extensive overlap between LR distributions of present and absent genes. In the T recognizing class, the present set (TR or TG according to the test sample) is mixed with the more abundant absent sets, especially with the one that is specific for two reference strains (Figure 1), origi- nating more false absent calls. In the class that does not target T genes, the present set that is specific for one strain only is well separated from the absent set (that is also spe- cific for one strain only). Most errors come from the present RG spot set that overlaps significantly with the absent set that is centered on an LR value of 0. Hence, by producing more false negatives (false absent calls) than false positives (false present calls), both sets contribute to the higher specificity associated with hybridizations using the T control. For hybridizations using the mix control the two classes of spots analyzed separately were the spot class recognizing genes present in one strain only (T, R and G spots) and the spot class identifying genes present in two strains (TR, TG and RG spots). In contrast with the classes analyzed with the T control, this division separates into a single class the spots that are specific for genes present in only one refer- ence strain. As stated previously, among these spot sets, absent and present genes are well segregated and, conse- quently, the classification in this class produces few errors. The threshold value of the second class is mainly condi- tioned by present spots (TR or TG, according to the test sample, and RG) because they are more abundant. Conse- quently a higher error rate is obtained in the absent spots (TG or TR, according to the test sample), producing more false present calls. Notwithstanding, the error rates in the present sets are much lower than the ones observed for hybridizations with the T control, comparatively produc- ing significantly less false absent calls, contributing to the higher sensitivity obtained using the mix control. Conclusion The overall data favors the use of the mix control in mul- tistrain microarray CGH experiments. Additionally, the lower accuracies described for hybridizations with the sin- gle strain control are associated with genes that are already known not to be part of the core genome of the species since they are not shared by all strains used in designing the array. Genes represented in TRG spots, that include those defining the core genome and a fraction of those included in the accessory genome, were not analyzed because, as stated in the methods section, they should be analyzed separately when the mix control is used. Since all these spots are known to be present, the methodology used to evaluate the discriminatory power of LR distribu- tions does not apply. Still, we expect both control choices to behave similarly for TRG spots because the control channel in both approaches has the same expected signal intensity (Table 1) and therefore the resulting LR distribu- tions for these spots are similar in terms of location and, in comparison with other spot classes, present lower dis- persion (Figure 1). The remaining classes considered included genes found in a single strain or in two of the strains used to design the array, and therefore represent the accessory or dispensable genome. More variation between strains of the same species is expected for these genes that were specifically analyzed in this work, rein- forcing the importance of the reported better performance associated with the use of the mix control. From a biolog- ical standpoint, the identification of particular ecotypes or variants with enhanced virulence among strains of the same species, are examples of studies that rely on the accu- Page 7 of 11 (page number not for citation purposes)