BACKGROUND: Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. RESULTS: GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. CONCLUSIONS: GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).

MOTIVATION: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. RESULTS: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed 2 distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth and enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.

UNLABELLED: TO-GO is a Gene Ontology (GO) navigation tool, which is implemented as a Java application. After the initial data downloading, the GO term tree can be interactively navigated without further network transfer. Local annotation can be incorporated. It supports querying by GO terms or associated gene product information, displaying the result as a table or a sub-tree. The result from the search for a set of external database accessions includes the number of gene products associated with each node, inclusive of sub-nodes. Search results can be further processed by set operations and these set operations can be quite useful for expression profile data analysis. A copy/paste function is also implemented in order to facilitate data exchange between applications. AVAILABILITY: TO-GO is freely available at http://www.ngic.re.kr/togo/index.html CONTACT: [email protected]