D608 Nucleic Acids Research, 2012, Vol. 40, Database issue node-by-node basis and clearly show each step of the sam- ple treatments in association with a link to its speciﬁc protocol and parameters that were used. A general con- vention is used within the database that replicates are sufﬁxed with a tag (e.g. ‘A1’) where the letter corresponds to the biological replicate and the number corresponds to the technical replicate. The graphical Array browser (Figure 1C) allows the user to explore the layout of the array in more detail, searching for further information about the reporters printed; including the PCR primers used for template ampliﬁcation on the PCR product arrays or the selected sequences for the newer oligonucleotide or IJISS arrays. Supporting layout ﬁles for various software applications (e.g. ImaGene, BlueFuse) are also available for download. The original raw data generated by the feature extrac- tion software can be downloaded as a Zip ﬁle (including a description HTML ﬁle) from the main experiment sum- mary tree (Figure 1A). Alternatively the data ﬁles can be browsed through a list view and individual details ac- cessed, including the feature extraction software versions and settings, quality control metrics and links to any analyses processes in which the ﬁle was included. An experiment-wide quality control summary report is avail- able (Figure 2) which provides a series of metrics to identify any problems that may exist in the raw data ﬁles. These include checks to make sure that the printed slide has been scanned in the correct orientation, the Cy3 or Cy5 dyes have not been swapped, the overall range of intensity of the elements on the array (a ‘trafﬁc light’-like system allows quick visualization of all data ﬁles in an experiment) and to identify blank rows in a data ﬁle (this provides evidence of a corrupted or edited raw data ﬁle). Analysed data can also be downloaded as a Zip ﬁle, or browsed in list view; data is stored in column form and/ or its original raw format (e.g. images, Microsoft Powerpoint ﬁles). These are linked from the raw data through a series of successive transformation steps, each annotated with a protocol. Typically the normalized data for each array is stored followed by the output of any Figure 2. Each experiment has a Quality Control summary report. From left to right: (i) Data ﬁle ID, (ii) data ﬁle name, (iii) ‘trafﬁc light’-like QC display (iv) ‘view’ link to display details of the calcula- tions, (v) test for correct channel identiﬁcation, (vi) test for correct array orientation when scanned, (vii) test for signiﬁcant signal intensity (foreground signal intensity is greater than three standard deviations above background intensity). The ‘trafﬁc light’-like system allows quick visualization of all data ﬁles; red means a higher percentage of spots are of lower intensity (<1000), green shows a better intensity range (1000 < intensity < 10 000), the third box suggests if there is signal sat- uration in the data set. number of ﬁltering steps. The exact analysis process can be visualized on a schematic data tree, also linked from the experimental summary tree (Figure 1D). Analysed data in Newick format tree ﬁles can be viewed using the integrated tree viewer applet, PhyloWidget (6). PhyloWidget enables display of phylogenetic trees in several forms (unrooted, circular etc) as well as tree editing, external tree ﬁle loading and publication-quality tree image export. DATA MINING The data can be queried in several ways through a centralized search page (Figure 3A). Experimental/publi- cation details can be queried to identify studies of interest; these include text based searches of the publication title, abstract or author list as well as MO controlled vocabu- lary searches of the experimental design or factor types. The experiment can also be queried by the sample descrip- tions contained within; species, strain, genotype etc can all be queried to identify studies of interest. Array designs can be identiﬁed by organism and design version, and subsequently visualized using the array viewer as previously mentioned; array annotation can then be searched through the ‘Spot Search’ function. Alternatively a user-supplied sequence can be searched using BLAST against array reporters (PCR products or oligonucleotides). Analysed data can be searched by ﬁrst identifying a gene of interest, either through the systematic name, gene name or some associated attribute (e.g. GO term or PFAM domain identiﬁer). A gene summary page (Figure 3B) presents annotation data with a link to a genome browser view (Figure 3D) as well as links to exter- nal database resources. An embedded query form provides a search of database wide gene lists (e.g. up or down- regulated lists) or normalized data (ﬁltering on ratio, log ratio or P-value if required); matching data sets are re- turned grouped by experiment (Figure 3C). If gene lists are searched, a comparison function enables a further search of all these matching lists and identiﬁes other genes, which are also present; ordering by occurrence can identify genes which may show similar patterns to the gene of interest. FUTURE DIRECTIONS While originally developed for microarray data, the modular infrastructure developed for recording biological sample information, processing raw data and integrating complex analysis tools behind an intuitive web-interface, is ideal for effective expansion to additional genomics tech- nology platforms and other Systems biology approaches. Thus development to integrate other forms of microbial functional genomic data, e.g. RNA-Seq, ChIP-Seq is cur- rently underway. An updated interface is in development, harnessing the power of Web 2.0 drag-and-drop technologies, which will create a more dynamic user experience and provide access to a suite of analysis tools that are also in develop- ment. The analysis tools will also be used to perform a