BMC Genomics 2008, 9:230 http://www.biomedcentral.com/1471-2164/9/230 genomes. For oligos that target ORFs in more than one genome, BLASTN E-values for the multiple target ORFs are always lower than 10-7 and nucleotide percent identities are always greater than 88%. Carter and co-workers, using Escherichia coli multistrain arrays with 70 mers probes, state that an identity greater than 80% by BLASTN search matches with hybridization signal intensity [27]. For microarray hybridization 4 μg of genomic DNA was completely digested using EcoRI and subsequently puri- fied and labeled using the BioPrime® Plus Array CGH Genomic Labeling System kit (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. Control of DNA fragment size distribution and quantity after enzyme digestion and labeling was performed using the Bioana- lyzer 2100 (Agilent Technologies, Palo Alto, CA) and the ND-1000 (NanoDrop Technologies, Wilmington, DE). Microarray processing consisted of hybridization, wash- ing and scanning. Microarray hybridization and washing were performed according to the protocols recommended by The Pathogen Functional Genomic Resource Center [28]. G2534-60001 microarray hybridization chambers were used (Agilent Technologies, Palo Alto, CA). The hybridization solution was a mixture of the two differen- tially labeled probes. Microarrays were scanned using the Agilent G2565BA scanner. Four replicates were done for each different pair of test strain and control, resulting in a total of 16 hybridiza- tions. The four different hybridizations were R vs T, R vs Mix (T+R+G), G vs T and G vs Mix (T+R+G). Dye swaps were applied in each set of four replicates so that two had the test sample in the red channel and the other two in the green channel. Microarray hybridization raw data was deposited in the ArrayExpress [29] public database with the following accession number: E-MEXP-1390. Data analysis Microarray images were analyzed using Feature Extraction 9.1 software (Agilent Technologies, Palo Alto, CA). Spots that were covered by visually identified image artifacts were flagged and discarded. The signal of each spot was background corrected by subtracting the minimum fea- ture signal in the array. A spatial detrend model was fitted to correct for spatial effects. Intensity-specific bias was removed through lowess global normalization. The resulting spot average pixel intensities were used to com- pute the logarithm (decimal) of the test/control signal ratio, here referred as log-ratios or LR. The resulting text data files were imported and further processed in the Matlab 7.3 (MathWorks, Natick, MA) computing environment. Gal files provided by The Path- ogen Functional Genomic Resource Center [26] contain- ing the gene identification for each spot on the array, as well as information on its presence in each of the three ref- erence genomes were also imported into Matlab. Since each gene was spotted four times per array, data retrieved from each of the valid spots were averaged for a particular gene. Quantile normalization was used to obtain LR dis- tributions of the combined replicate arrays. For a given set of spots within an individual array (where the set can be all spots on the array or subsets as discussed below), and for each possible LR threshold value in that set, we identi- fied each spot as a true positive (TP), true negative (TN), false positive (FP) or false negative (FN). TP spots have a LR greater than or equal to the threshold and target a gene that is present in the test strain. TN spots have a LR lower than the threshold and target a gene not present in the test strain. FP spots have a LR greater than or equal to the threshold and target a gene that is not present in the test strain. FN spots have a LR lower than the threshold and target a gene that is present in the test strain. With the number of TP, TN, FP and FN we can calculate the accu- racy (Acc), sensitivity (Sn) and specificity (Sp) according to the following formulas: Acc = TP +TN TP +TN + FP + FN Sn = Sp = TP TP + FN TN TN + FP The LR threshold producing the maximum Acc value was chosen as the optimal threshold for the corresponding set of spots. ROC curves were produced by plotting Sn versus 1-Sp val- ues for each possible LR threshold and the area under the ROC curve (AUC) was then estimated for each array or class of spots in each array, which is a measure of the dis- criminatory power of LR distributions independently of the threshold choice [23]. For both types of control sample we evaluated LR discrim- inatory power considering all spots together or divided by classes. When the T control is used there are two classes of spots with different control signals (T, TR, TG and TRG spots form one class and R, G and RG form the other class, Table 1). When the mix control is used, there are three classes of spots with different control signals because they target genes present in one control strain (T, R and G spots), two control strains (TR, TG and RG spots) or all Page 9 of 11 (page number not for citation purposes)