5916 Nucleic Acids Research, 2005, Vol. 33, No. 18 from the ‘affy’ package. Other normalization methods yielded similar results (data not shown). Probe annotation Manufacturer’s annotations for the Affymetrix platform were downloaded from the NetAffx web site (https://www. affymetrix.com/analysis/netaffx/) on March 15, 2005. Illu- mina also provided a table of annotations. We supplemented these annotations with our own sequence analysis based on comparing sequences with the Human genome sequence (assembly hg17, July 2004). For Affymetrix, we merged and joined the individual probe sequences to form a ‘pseudo- target’ sequence; we found that aligning these to the genome was much more effective and efﬁcient than attempting to align individual probes or using the Affymetrix ‘target’ sequences (the merging procedure is depicted in a Supplementary Figure). For Illumina the input sequences were the 50 bp oligonucleotide sequences provided by the manufacturer. All sequences used are provided as Supplementary Data or are available from the manufacturers. Each sequence was compared with the genome sequence using BLAT (10) with minimum score set to 20 and an initial minimum identity set to 0.5 (all other parameters were left to the default setting). Each hit was then given an overall score equal to (m g)/s, where m is the number of matches, g is the number of gaps in the alignment, and s is the size of the query sequences. This score differs slightly from the default in the GoldenPath genome browser in that the gap penalty is lower, but we found it gave us higher sensitivity when aligning shorter sequences and permits good gapped alignments of the collapsed Affymetrix sequences. A threshold of 0.9 applied to this score yielded multiple BLAT hits for many of the probes. These hits were associated with genes as follows. The location of each hit was compared with the ‘refGene’ and ‘knownGene’ tables in the hg17 Golden Path database (11). A BLAT hit overlapping or falling within the anno- tated limits of a gene (on the correct strand) was retained as an initial hit. Each BLAT hit was further scored based on two criteria. First, the fraction of bases which overlapped with annotated exons or mRNAs (as represented in the hg17 database tables knownGene, refGene and all_mrna). Second, we computed the distance of the 30 end of the BLAT hit from the 30 end of the annotated transcript (using the center of the BLAT hit made no difference in the conclusions; see Supplementary Data). The ﬁnal ‘best’ match for a probe was the transcript closest to the probe’s 30 end and with the largest non-intronic overlap. This means that probes falling entirely within introns were given similarity scores of zero, and when there were two alternative 30 ends for a gene, the one with the 30 end nearest to the probe was selected as the targeted gene. In cases where there were two or more equivalent ‘best’ hits to different sites in the genome (i.e. a tie), one was arbitrarily chosen (247 cases for Affymetrix, 231 cases for Illumina). These often repre- sented alignments to sequences duplicated in the assembly (e.g. parts of chromosome 1 and chromosome 1_random; 10% of cases). Other ties often involved closely related genes, probably reﬂecting duplications (e.g. CGB and CGB5). To analyze the relationship of cross-platform agreement with probe location, the distance between two probes was measured as the distance between the centers of their align- ments on the genome. Investigations where we varied these parameters or meth- odologies did not change our main conclusions, though the results for individual probes are naturally affected by the exact criteria used. The full sets of annotations we derived are avail- able as Supplementary Data, along with additional details of the results of the BLAT analysis. A summary of the annota- tions used in this study is given in Table 1. Clustering For clustering only, the data matrices for each platform were adjusted so the probe expression vectors had a mean of zero and variance one. A combined data matrix was constructed such that each probe for a gene on one platform was used to form new combined expression vectors with each probe for the same gene on the other platform. This means that if a gene appeared twice on each platform, a total of four new expres- sion vectors were constructed. This ﬁnal matrix had 36 024 rows (approximately a factor of two over what would have been obtained had we averaged the probes for each gene). The rows of this matrix were subjected to hierarchical clus- tering using XCluster (http://genetics.stanford.edu/~sherlock/ cluster.html), with average linkage and Euclidean distance. The results were visualized with matrix2png (12). Statistical analysis Analyses were carried out in the R statistical language or using custom Java programs. The dilution proﬁle was described as a simple factor in a linear model used to ﬁt each gene. The correlation of each gene expression proﬁle was used as a statistic for further analyses; P-values for the deviations from a correlation of zero were computed using standard nor- mal assumptions. To establish statistical thresholds via false Table 1. Summary statistics for array designs studied, comparing two annotation methods Array Illumina (Mfg)a Illumina (BLAT) Affymetrix (Mfg) Affymetrix (BLAT) 24 114 54 675 Probes or probe sets Probes for ‘known’ genesb Known genes assayed Unassigned probesb Probes/gene (average) 17 143 19 924 34 089 33 922 14 420 16 847 16 610 18 417 6971 2978c 20 586d 20 600c 1.19 1.18 2.05 1.84 aMfg, Manufacturer’s annotations; BLAT, our own annotations computed using BLAT alignments to the genomic sequence. b‘Known genes’ are genes identified in the GoldenPath ‘refGene’ or ‘knownGene’ tables, including transcript information from the ‘all_mrna’ table to determine exon overlaps. We designate all other potential transcripts ‘unassigned probes’. cIncludes probes not yielding BLAT results. dIncludes 14 334 probes where no gene name is listed by the manufacturer.