5922 Nucleic Acids Research, 2005, Vol. 33, No. 18 for genes predicted to be differentially expressed. Beyond this conclusion, two more speciﬁc ﬁndings we wish to highlight in the discussion are that expression level plays a major role in determining reproducibility across platforms, and that the pre- cise location of the probe on the genome affects the measure- ments to a substantial degree, such that two probes which do not map to the same location cannot be assumed to be mea- suring the same thing. When these two factors are taken into account, the agreement of the results across platforms is very high, though still not perfect. Contrary to our general ﬁndings, a number of groups have found that concordance of results across expression analysis platforms is low (4,5,15–18). The reason for the discordance between such ﬁndings and ours, as well as that of a number of other groups (19–26) is not always clear, especially as in some cases the data are not publicly available. At least one group (24) has reported higher reproducibility than in a previous analysis of the same data (15), suggesting that data treatment and choice of comparison metric plays a role. One group reported high reproducibility of Affymetrix and long oligonu- cleotide arrays (which share similarities to the BeadArrays in the type of sequence used), but not of cDNA arrays (21), suggesting that there could be real differences in the repro- ducibility of different platforms, and that arrays based on long clones may have particular problems with speciﬁcity (16,17). Our study reinforces the idea that a failure to consider annotations and expression levels sufﬁciently carefully can help explain some of the observed differences. For example, we note that Tan et al. (who compared three platforms) (4) relied on GenBank or UniGene identiﬁers to match genes across platforms. We believe this approach may be unsuitable for high-sensitivity comparisons across platforms, because of the coarseness of resolution of UniGene or GenBank IDs compared with the actual probes used on the arrays. The cita- tion of the particular accession number only indicates the source of the probe sequence and does not imply that that GenBank sequence is speciﬁc for a particular gene. In extreme cases, the GenBank accession number referenced by the manu- facturer includes multiple genes. Thus, even when two manu- facturers cite the same GenBank accession number, there is no guarantee that the same transcripts are being assayed. For this reason, we have discarded identiﬁer cross-references as a primary means of matching probes across platforms. Tan et al. (4) also do not document any consideration of the impact of expression level on agreement. In contrast, Park et al. (5) reported both an expression level and a probe speciﬁcity effect. It will be of interest to re-examine other cases of poor agreement across platforms in light of such considerations. In our study, we identiﬁed thousands of probes on both platforms which show extremely good conﬁrmation of results. In contrast to studies where a few results are checked by quantitative PCR, we have built-in cross-validation of a huge fraction of the results of the experiment. This set of cross-validated probes, though identiﬁed using conservative criteria on only one set of samples, could be considered as a starting point for identifying probes that perform well on these platforms. It is likely that many other probes on both platforms also perform well, but could not be evaluated due to insufﬁ- cient signals in the tissues we studied. The complete list of probes on both platforms, with their agreement statistics across platforms, is included as Supplementary Data. Our recommendation for groups which plan to compare or combine data across platforms (whether array-based or using another technology), or even across laboratories using the same platform, is to take the following issues into considera- tion. First, not surprisingly, genes which give weak signals are hard to conﬁrm. Therefore the failure of one platform to conﬁrm a result on a rare transcript should be interpreted cautiously. Second, careful bioinformatics analysis of each platform is necessary to maximize the precision of the com- parison. At the ﬁrst level of analysis, the manufacturer’s anno- tations should be evaluated for comparability or replaced with a customized analysis that uses a common approach, to avoid conﬂicts due to differences in annotation methodology. At a second level of analysis, one can consider a ﬁner level of stratiﬁcation of probes based on their relative locations. Some questions still remain. There are a still fairly numer- ous probes which, based on dilution effect, location and expression level criteria, would be predicted to yield repro- ducible results, but do not. As mentioned, we could not fully address the possibility that cross-hybridization may play an important role (16). Resolving this will likely require addi- tional data. We also cannot eliminate the possibility that reﬁnements of transcript assignment would resolve some cases of ‘disagreement’. Our assignment of probes to genes was based on limited databases of mRNAs and known genes, and transcripts not represented in these databases would not be reﬂected in our analysis. This could lead to incorrectly pre- dicting the same hybridization pattern for two probes located at nearby locations in the genome. A likely explanation for some of the effects we see have to do with differences in the technologies, such as differences in RNA labeling protocols, or the linker and ‘bar code’ sequences on the Illumina arrays compared with the direct attachment of the Affymetrix sequences to the substrate. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online and at http:// microarray.cu-genome.org/platformCompare. ACKNOWLEDGEMENTS Thanks to Illumina for providing and running the BeadArrays as part of a Customer Service Evaluation. We thank Kiran Keshav for assistance in preparing the manuscript. This work was supported in part by the Children’s Hospital Research Foundation of Cincinnati, the Schmidlapp Foundation, and National Institutes of Health Grants GM076880, AR47363, AR47784 and AR50688. Funding to pay the Open Access publication charges for this article was provided by NIH grant AR048929. Conflict of interest statement. None declared. REFERENCES 1. Gunderson,K.L., Kruglyak,S., Graige,M.S., Garcia,F., Kermani,B.G., Zhao,C., Che,D., Dickinson,T., Wickham,E., Bierle,J. et al. (2004) Decoding randomly ordered DNA arrays. Genome Res., 14, 870–877. 2. Lockhart,D.J., Dong,H., Byrne,M.C., Follettie,M.T., Gallo,M.V., Chee,M.S., Mittmann,M., Wang,C., Kobayashi,M., Horton,H. et al.