We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations

We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. Results We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of ‘false discovery rate’ multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and GO categories. Conclusion High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. ‘interesting’ genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation Ngfr in the context of the Gene Ontology. The original version of GoMiner was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface. Although OSI-420 that version can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and original GoMiner includes no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays. We wanted to provide a computational resource that automates the analysis of multiple microarrays and then integrates the results across all of them in useful exportable output files and visualizations. Results We now introduce a new tool, High-Throughput GoMiner, that has those capabilities and a number of others: OSI-420 It (i) efficiently performs the computationally-intensive task of automated batch processing of an arbitrary number of microarrays, (ii) produces a human-or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories, (iii) integrates the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories, (iv) provides a fast form of ‘false discovery rate’ multiple comparisons calculation, and (v) provides annotations and visualizations for relating transcription factor binding sites to genes and OSI-420 GO categories. Conclusion High-Throughput GoMiner achieves the desired goal of providing a computational resource that automates the analysis of multiple microarrays and integrates results across all of the microarrays. For illustration, we show an application of this new tool to the interpretation of altered gene expression patterns in Common Variable Immune Deficiency (CVID). High-Throughput GoMiner will be useful in a wide range of applications, including the study of time-courses, evaluation of multiple drug treatments, comparison of multiple gene knock-outs or knock-downs, and screening of large numbers of chemical derivatives generated from a promising lead compound. Background The original version of GoMiner [1,2] was oriented toward visualization and interpretation of the results from a single microarray (or other high-throughput experimental platform), using a graphical user interface (GUI). Although the GUI can be used to examine the results from a number of microarrays one at a time, that is a rather tedious task, and there is no apparatus for obtaining a global picture of results from an experiment that consists of multiple microarrays: ? Suppose, for example, that combinatorial chemistry were used to generate a large number of derivatives of a lead compound. If microarrays were used to monitor the efficacy of those derivatives, then it is likely that none, or at most a few, of the microarrays would be interesting. It would be a thankless task to use the GUI to analyze and interpret the large number of uninformative microarrays. It would make much more sense to apply an automated batch procedure to generate a report that highlighted the interesting microarrays and then to examine just those in the GUI. ? As another example, suppose that a series of microarrays were used to generate a time-course. One would want to obtain a high-level, global picture of the relationships of the categories that were significant at different time points C for instance, to differentiate phases of a disease process or to explore the temporal sequence of events consequent to treatment with a drug. High-Throughput GoMiner performs those tasks. As a tool for investigators with large sets of results, it matches and stretches the GUI version’s analysis and visualization capabilities. Both the control collection and web software interfaces of High-Throughput OSI-420 GoMiner are freely available to all users [3]. To our knowledge, this is the 1st source that integrates info and illuminates patterns from multiple microarrays in relationship to the Gene Ontology. In the original GoMiner article [1,2], we mentioned the Fisher’s precise p-values require adjustment to account for the multiple comparisons problem. We proposed a resampling approach that would avoid major drawbacks of the Bonferroni correction (see, for example [4]) C the assumption of independence of groups and the likelihood of rejecting too many mouse or human being) or to any combination of varieties represented within the GO Consortium database. That is a functionally important type of flexibility. 9. Acknowledgement of HUGO gene titles by High-Throughput GoMiner. The High-Throughput GoMiner database can identify HUGO names as well as any of the additional identifier types provided by the GO Consortium database. The ability to identify HUGO names is not an inherent feature of the annotation provided by the GO Consortium, so users of GO::TermFinder.