Discovering biological connections between experimental conditions based on common patterns of differential gene expression.

ABSTRACT:

Background

Identifying similarities between patterns of differential gene expression provides an opportunity to identify similarities between the experimental and biological conditions that give rise to these gene expression alterations. The growing volume of gene expression data in open data repositories such as the NCBI Gene Expression Omnibus (GEO) presents an opportunity to identify these gene expression similarities on a large scale across a diverse collection of datasets. We have developed a fast, pattern-based computational approach, named openSESAME (Search of Expression Signatures Across Many Experiments), that identifies datasets enriched in samples that display coordinate differential expression of a query signature. Importantly, openSESAME performs this search without p

SUBMITTER: Gower AC 

PROVIDER: S-EPMC3203354 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature