GoMiner: a resource for biological interpretation of genomic and proteomic data

Abstract

We have developed GoMiner, a program package 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. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources. RationaleGene-expression profiling and other forms of high-throughput genomic and proteomic studies are revolutionizing biology. That much is universally agreed. But the new technologies pose new challenges. The first is the experiment itself, the second is statistical analysis of results, the third is biological interpretation. That third challenge is often the most vexing and time-consuming. In gene-expression microarray studies, for example, one generally obtains a list of dozens or hundreds of genes that differ in expression between samples and then asks: 'What does all of this mean biologically?' The work of the Gene Ontology (GO) Consortium [1] provides a way to address that question. GO organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. In the past, this GO information was queried one gene at a time. Recently, batch processing has been introduced [2], but with a flat-format output that does not communicate the richness of GO's hierarchical structure.We have developed, and present here, the program package GoMiner as a freely available computer resource that fully incorporates the hierarchical structure of the Gene Ontology to automate the functional categorization of gene lists of any length. GoMiner is downloadable free of charge from [3] or [4]. GoMiner was developed particularly for biological interpretation of microarray data; one can input a list of underand overexpressed genes and a list of all genes on the array, and then calculate enrichment or depletion of categories with genes that have changed expression. GoMiner thus facilitates analysis and organization of the results for rapid interpretation of 'omic' [5,6] data. For concreteness, the descriptions in

Gene expression profiling identifies molecular subgroups among nodal peripheral T-cell lymphomas

“…Functional annotation Functional annotation of genes discriminating each lymphomas subtype was performed using GO (Ashburner et al, 2000) and GoMiner (Zeeberg et al, 2003). Significance of over-or under-representation was calculated using Fisher's exact test at 0.1% risk.…”

Cellular Effect of High Doses of Silica-Coated Quantum Dot Profiled with High Throughput Gene Expression Analysis and High Content Cellomics Measurements

“…We classified the genes into functional categories using the GoMiner program. 32 Figure 3B lists the categories. The functional categories of the changed genes are consistent across the two different dosages.…”

Transcriptome analysis of microdissected pancreatic intraepithelial neoplastic lesions

“…In order to obtain hints at the cell biological implications of the differences detected between PanIN lesions and normal duct cells, it was of major interest to query the data for accumulations of genes with interesting expression patterns in distinct functional categories. To this end, we used the 'GoMiner' tool (Zeeberg et al, 2003) to query the Gene Ontology (GO) database (http://www.geneon tology.org) for functional categories associated with the 1251 differentially expressed genes. Differentially expressed genes were then organised into functional categories such as development, structure, signal transduction, etc.…”