Nucleic Acids Research, 2005, Vol. 33, Web Server issue W641 Table 2. Graphical outputs and their functions for the RACE Statistical Tests tool Output Correlation matrix plot Sample cluster StdDev plots p-Value histogram Volcano plots FDR versus p-value plot MvA plot Function Check whether intra-group correlations are higher than correlations between groups; identify outlier samples Check whether uploaded groups yield separated clusters; identify sample subgroups Compare distributions of expression standard deviations in the different groups; assess variability in different groups Obtain a visual impression of the amount of differentially expressed genes by the height of a potential peak at small p-values Check for genes with high significance and large expression changes across groups Find the appropriate p-value threshold to limit the estimated FDR of the resulting gene list below a fixed value Visualize mean expression and log changes of all genes; label genes which were selected according to user’s defined p-value and fold-change cutoffs Figure 3. ‘p-Value histogram’ output. The number of genes (‘Frequency’) which fall into each p-value bin is presented. In the insert, the False Discovery Rate versus the p-value threshold is plotted. signiﬁcant expression differences between these two RNAs. By specifying a p-value and fold-change threshold, the user deﬁnes a candidate list of regulated genes. The inset shows the estimated FDR as a function of the p-value threshold. Figure 4 shows the output type ‘MvA plot’ for the experi- ment in which human placenta and testis RNA were compared. Each point represents one gene. M is the log (base 2) of the fold-change in expression between testis and placenta, and A is the log average of the expression level. Very large expres- sion differences over a wide range of expression intensities are seen. Genes which meet user-deﬁned p-value and fold-change criteria are labeled in the output graph. Additionally, the selec- tion criteria and the ﬁle name of the list which contains the labeled genes accompanied by annotations and statistical mea- sures are presented in the output graph. In this example, the 100 most signiﬁcant genes with fold-changes >2 ( p < 0.01) have been selected. GO-term Analysis tool Purpose and required data input format. The aim of the GO-term Analysis tool is to assist in the biological inter- pretation of gene lists by identifying functional annotations (GO terms) which are enriched among the user-provided input genes. Users can choose among the different ontology Figure 4. ‘MvA plots’ output. The expression ratio (log base 2) of genes is plotted against their average expression intensity. Circles identify genes that pass user-defined p-value and fold-change value thresholds. categories and GO-term levels and can select threshold combinations for list coverage (minimum number of genes corresponding to each GO term) and statistical signiﬁcance (p-value) for the overrepresentation of each GO term. GO terms which meet these criteria are reported together with the corresponding genes. A tab-delimited ﬁle containing Affymetrix identiﬁers in one column is required as input. Optionally, another column may contain log ratios, which can then be used to analyze the GO terms according to the under- or overexpression of the genes being analyzed. Gene lists generated by the Statistical Tests tool can be used directly as input ﬁles. Description. GO (23) provides three structured, controlled vocabularies (ontologies) that describe gene products species-independently in terms of their associated biological processes, cellular components and molecular functions. GO terms are organized in directed acyclic graphs, representing networks where each term may be a ‘child’ (more specialized term) of one or more ‘parents’ (less specialized terms). The networks deﬁne the ‘is a’ or ‘part of’ relationships between terms and allow the grouping of all GO terms into different levels. As the GO term level increases, the informational speciﬁcity increases and the genome coverage decreases (24; also see http://www.geneontology.org/ for a more detailed description). RACE uses the Bioconductor meta-data packages for the mappings of Affymetrix identiﬁers to LocusLink identiﬁers and of LocusLink identiﬁers to GO terms. GO-term levels are derived from the ‘gene_ontology.obo’ text ﬁle provided