VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data.

TitleVAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data.
Publication TypeJournal Article
Year of Publication2005
AuthorsHsiao A, Ideker T, Olefsky JM, Subramaniam S
JournalNucleic Acids Res
IssueWeb Server issue
Date Published2005 Jul 1
KeywordsBayes Theorem, Data Interpretation, Statistical, Database Management Systems, Gene Expression Profiling, Internet, Oligonucleotide Array Sequence Analysis, Software, User-Computer Interface

Microarrays are invaluable high-throughput tools used to snapshot the gene expression profiles of cells and tissues. Among the most basic and fundamental questions asked of microarray data is whether individual genes are significantly activated or repressed by a particular stimulus. We have previously presented two Bayesian statistical methods for this level of analysis, collectively known as variance-modeled posterior inference with regional exponentials (VAMPIRE). These methods each require a sophisticated modeling step followed by integration of a posterior probability density. We present here a publicly available, web-based platform that allows users to easily load data, associate related samples and identify differentially expressed features using the VAMPIRE statistical framework. In addition, this suite of tools seamlessly integrates a novel gene annotation tool, known as GOby, which identifies statistically overrepresented gene groups. Unlike other tools in this genre, GOby can localize enrichment while respecting the hierarchical structure of annotation systems like Gene Ontology (GO). By identifying statistically significant enrichment of GO terms, Kyoto Encyclopedia of Genes and Genomes pathways, and TRANSFAC transcription factor binding sites, users can gain substantial insight into the physiological significance of sets of differentially expressed genes. The VAMPIRE microarray suite can be accessed at

PubMed URL
Alternate TitleNucleic Acids Res.
PubMed ID15980550