High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2005-07-05

AUTHORS

Barry R Zeeberg, Haiying Qin, Sudarshan Narasimhan, Margot Sunshine, Hong Cao, David W Kane, Mark Reimers, Robert M Stephens, David Bryant, Stanley K Burt, Eldad Elnekave, Danielle M Hari, Thomas A Wynn, Charlotte Cunningham-Rundles, Donn M Stewart, David Nelson, John N Weinstein

ABSTRACT

BackgroundWe 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.ResultsWe 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.ConclusionHigh-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. More... »

PAGES

168

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-6-168

DOI

http://dx.doi.org/10.1186/1471-2105-6-168

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047729779

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/15998470


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RDF/XML is a standard XML format for linked data.

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