Nucleic Acids Research, 2008, Vol. 36, No. 7 2449 Figure 1. Schematic illustration of probabilistic test settings. The ﬁgure shows the binomial distributions of real match pair population (red dots) and of non-pair population (blue dots) for N equal to 30 and PM and Pnon-M, for the real match pair population and the non-pair population, equal to 0.9 and 0.4. The red, blue and green bars deﬁne regions of ‘diﬀerent’ (mnon-M set equal to 1), ‘uncertain’ and ‘similar’ (mM set equal to 2) SPIA test calls. The smaller the number of SNPs is, the narrower the region of uncertainty and the higher the probability of making an incorrect call. The area limits depend on the level of conﬁdence which is needed for the application. In brief, the mean number of successes kmean is equal to NPM and the standard deviation (sdkmean) is sdkmean ¼ pﬃNﬃﬃﬃPﬃﬃﬃMﬃﬃﬃﬃðﬃﬃ1ﬃﬃﬃﬃﬃﬃﬃﬃPﬃﬃﬃMﬃﬃﬃﬃÞﬃ. The probability that a distance measurment falls within m standard deviations from the mean (i.e. within the interval [kmean m sdkmean, kmean + m sdkmean]) is given by the integral of the distribution function. By setting the parameter m one can deﬁne the area limits corresponding to ‘diﬀerent, ‘uncertain’ and ‘similar’. For example, setting m = 2, the integral of the distribution function is 0.954. Figure 1 shows the binomial distributions of real match pair population (red dots) and of non-pair population (blue dots) for N equal to 30; the probabilities PM and Pnon-M, for the real match pair population and the non- pair population, are set to 0.9 and 0.4 respectively and mM and mnon-M are set to 2 and 1, respectively. The red, blue and green bars deﬁne regions of ‘diﬀerent, ‘uncertain’ and ‘similar’ SPIA test calls. The smaller the number of SNPs is, the narrower the region of uncertainty and the higher the probability of making an incorrect call. In theory, for Hardy–Weinberg SNPs we expect Pnon-M to be equal to 0.375. For exactly the same DNA sample we expect PM being equal to 1. In fact, if we genotype the same DNA sample twice using the same platform, the expected number of mismatches depends on the platform reproducibility error. If, for the same individual, we compare the DNA extracted from normal tissue and from tumor tissue, we expect some variation, most commonly derived from loss of heterozygosity. Similarly, if we compare DNA extracted years apart, some genomic variation can be expected. To empirically evaluate PM (probability of match for a real match pair) we calculated the mean percentage of matches using seven paired cell line samples, constituted by tumor cell line DNA and blood extracted DNA from the same individual. Using the 100 top ranked SNPs, the value of PM was estimated as 90%. Sequenom platform for genotyping human cells We tested a SPIA panel using Sequenom mass spectro- metric genotyping technology (22). This MALDI-TOF mass spectrometer system can diﬀerentiate SNP alleles given the diﬀerent molecular weights of the allele-speciﬁc products. First, a software package supplied by the manufacturer is used to design a series of primers that enable SNP detection in a multiplexed fashion. To carry out SNP detection, tumor-derived genomic DNA is ﬁrst subjected to whole genome ampliﬁcation to generate enough material for a series of multiplexed reactions, which are carried out in parallel on microtitre plates. Next, multiplexed PCR is performed (in 96- or 384-well plate format) on tumor genomic DNA to amplify regions harboring loci of interest, or ‘query’ nucleotides. After denaturation, PCR products are incubated with oligonu- cleotides that anneal immediately adjacent to the query nucleotide, and a primer extension reaction is performed in the presence of chain-terminating dideoxynucleotides that generate allele-speciﬁc DNA products. Primer exten- sion products are spotted onto a specially designed chip and analyzed by MALDI-TOF mass spectrometry to determine the single allele. Since allele calling depends exclusively on the mass of the resulting primer extension product, the Sequenom assay does not require expensive ﬂuorescence primer labeling and has a very low error rate. SPIA was written in R (23) (the R code is available on request). RESULTS Our approach to selection of a SNP panel to distinguish any cell line or tumor pair in use by the research community is shown in Figure 2. SNP computational selection is based on empirical data from 155 cell lines genotyped on 50 K oligonucleotide SNP arrays. The number of independent loci chosen depends on the level of conﬁdence one needs to make a deﬁnitive identiﬁcation. Although the panel is trained on the identiﬁcation of cell lines, this approach is suitable for additional applications,