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).
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.


CONCLUSIONS
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.
Zeeberg
Barry R
BR
Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov
Qin
Haiying
H
Narasimhan
Sudarshan
S
Sunshine
Margot
M
Cao
Hong
H
Kane
David W
DW
Reimers
Mark
M
Stephens
Robert M
RM
Bryant
David
D
Burt
Stanley K
SK
Elnekave
Eldad
E
Hari
Danielle M
DM
Wynn
Thomas A
TA
Cunningham-Rundles
Charlotte
C
Stewart
Donn M
DM
Nelson
David
D
Weinstein
John N
JN
D001665:Binding Sites; D002874:Chromosome Mapping; D016000:Cluster Analysis; D017074:Common Variable Immunodeficiency / Q000188:drug therapy / Q000235:genetics*; D003626:Data Display; D030541:Databases, Genetic; D001330:Electronic Data Processing; D020869:Gene Expression Profiling / Q000295:instrumentation*; D006801:Humans; D010641:Phenotype; D040081:Protein Array Analysis / Q000295:instrumentation*; D012552:Schistosomiasis / Q000235:genetics; D012984:Software; D012985:Software Design; D014157:Transcription Factors / Q000378:metabolism; D014584:User-Computer Interface
D014157:Transcription Factors