Nucleic Acids Research, 2005, Vol. 33, Web Server issue W639 Submitted jobs are queued and a customized analysis script is generated by a set of Perl scripts. The analysis script is executed in a subprocess. All statistical analysis is performed using the free high-level interpreted statistical language R (R Core, 2004, http://www. R-project.org) and various Bioconductor packages (http:// www.Bioconductor.org). The design of the software is modu- lar to facilitate the addition of further analysis tools. User accounts RACE can be used with an anonymous guest account but personal password-protected access is recommended. Regis- tered users can store data in a personal account on the server, making it possible to run multiple tasks without the need to re-upload input ﬁles. Moreover, waiting times are avoided as the user is automatically emailed at the completion of a job. RACE creates for each job a directory for storing the input ﬁles, the selected parameters, the utilized R script and the results. File handling The upload ﬁles module allows users to upload and store ﬁles, decompress ZIP ﬁles and organize the data in different sub- directories. After setting the parameters in the analysis tools, the user is given the option of providing the input data either by a new upload or by copying or splitting previously uploaded or generated data. The download ﬁles module allows users to access their password-protected directories, to browse their data and to download or delete ﬁles. Every ﬁle is deleted automatically by the system 1 week after its creation. RACE ANALYSIS TOOLS RACE currently offers three analysis tools accessible via the web interface, namely Data Quality Checks & Normalization, Statistical Tests and GO-term Analysis. Each tool is structured into three sections. The ﬁrst section contains links to three help pages describing the purpose and implemented methods, the required input data format and the output ﬁles generated. Parameters which are required for the analysis are set in the second section. Parameters which can be optionally changed to customize the output ﬁles generated are set in the third section. At the bottom of the second section the user can provide the data to be analyzed. After the submission of an analysis request, a conﬁrmation message, including a link to the output page, is displayed. When the job is completed, authenticated users will receive the link to the output page by email. The output page contains the user data, the customized R script used for the analysis, all result ﬁles, ZIP archives and a log ﬁle which tracks job start and completion as well as problems that may have occurred during the run. Data Quality Checks & Normalization tool Purpose and required data input format. The Data Quality Checks & Normalization tool is dedicated to the visualization, quality checking and normalization of Affymetrix GeneChip data. Data should be provided as Affymetrix CEL ﬁles in ASCII format, optionally zipped. Description. The Data Quality Checks & Normalization tool uses primarily methods implemented in the Bioconductor packages ‘affy’ (14) and ‘affyPLM’. To quality check the perfect match (PM), probe levels are summarized in spatial and dens- ity plots. Individual probes in each probe set are numbered starting from the 50 end of the transcript, and the mean 50 to 30 probe intensity bias for each array is determined. The probe- level intensities for probe sets are summarized to deﬁne a measure of the individual gene expression. To make data from different arrays comparable, RACE provides several normalization methods. The ﬁrst of these is MAS 5.0, the current Affymetrix default algorithm. However, several studies (15,16) suggest that measures based only on the PM probes outperform the MAS 5.0 algorithm. For this reason RACE also provides access to two of the most prominent PM-based algorithms: RMA (Robust Multichip Average; 17) and gcRMA (see the Bioconductor website: http://www. Bioconductor.org). RMA includes quantile normalization and a robust multi-array probe-level ﬁt, and gcRMA addi- tionally exploits sequence information for the background adjustment. Based on the normalized expression values the Pearson correlation and the standard deviation of gene-wise expression differences between two arrays are calculated to evaluate similarities of the gene expression proﬁle for each pair of samples. Moreover, a hierarchical sample cluster is built using Ward’s minimum variance method. Output. The principle output of this tool is a ﬁle containing normalized gene expression levels. In addition, multiple data visualizations are provided to assist in judging the quality of the data and the success of the normalization. Figure 1 shows two examples of the output type ‘PLM pseudo images’ (see Table 1) displaying the spatial distribution of the residuals obtained from a probe-level ﬁt over multiple arrays. High-quality data have characteristics similar to Figure 1a, which shows only a few small defects. In general, small defects do not seriously bias the expression levels, since probes representing one gene are distributed across the array and robust summary methods are used. However, extensive regions with large residuals—the dark regions seen in Figure 1b—are a clear indication of an experi- mental artifact (e.g in array production, hybridization or processing) and the array should be considered for exclusion from the analysis. (a) (b) Figure 1. ‘PLM pseudo image’ tool output. The spatial distribution of residuals obtained from probe-level fitting over multiple arrays is shown. (a) High- quality data showing almost no defects; (b) low-quality data showing large artifacts.