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There are now two papers about our GoMiner tools, GoMiner and High-Throughput GoMiner. If you with to cite either tool, please reference the corresponding paper below.
GoMiner: A Resource for Biological Interpretation of Genomic and Proteomic Data. Barry R. Zeeberg, Weimin Feng, Geoffrey Wang, May D. Wang, Anthony T. Fojo, Margot Sunshine, Sudarshan Narasimhan, David W. Kane, William C. Reinhold, Samir Lababidi, Kimberly J. Bussey, Joseph Riss, J. Carl Barrett, and John N. Weinstein. Genome Biology, 2003 4(4):R28 (published 25 March 2003)
Download the article in PDF format.
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.
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, Haiying Qin, Sudarshan Narasimhan, Margot Sunshine, Hong Cao, David W. Kane, Mark Reimers, Robert Stephens, David Bryant, Stanley K. Burt, Eldad Elnekave, Danielle M. Hari, Thomas A. Wynn, Charlotte Cunningham-Rundles, Donn M. Stewart, David Nelson and John N. Weinstein. BMC Bioinformatics, 2005 6:168.
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.
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.
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.
We would like to hear from you. You can reach the team via email.
GoMiner was originally developed jointly by the Genomics and Bioinformatics Group (GBG) of LMP, NCI, NIH and the Medical Informatics and Bioimaging group of BME, Georgia Tech/Emory University. It is now maintained and under continuing development by GBG.