High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID)

Barry R Zeeberg^*¹ , Haiying Qin^*² , Sudarshan Narasimhan³ , Margot Sunshine³ , Hong Cao³ , David W Kane³ , Mark Reimers³ , Robert M Stephens⁴ , David Bryant⁴ , Stanley K Burt⁴ , Eldad Elnekave⁵ , Danielle M Hari⁵ , Thomas A Wynn⁵ , Charlotte Cunningham-Rundles⁶ , Donn M Stewart² , David Nelson² and John N Weinstein¹

¹Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA

²Metabolism Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA

³SRA International, 4300 Fair Lakes CT, Fairfax, VA 22033, USA

⁴Advanced Biomedical Computing Center, National Cancer Institute at Frederick, SAIC Frederick, PO Box B, Frederick, MD, 21702, USA

⁵Laboratory of Parasitic Disease, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA

⁶The Mount Sinai Medical Center, 1425 Madison Avenue, New York, NY 10029, USA

author email corresponding author email* Contributed equally

BMC Bioinformatics 2005, 6:168doi:10.1186/1471-2105-6-168


Published:	5 July 2005

Abstract

Background

We previously developed GoMiner, an application that organizes lists of 'interesting' genes (for example, under-and overexpressed genes from a microarray experiment) for biological interpretation 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 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: 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. 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.