SPELL
[Serial Pattern of Expression Levels Locator]

SPELL is an online search engine for large compendia of gene expression microarray data. The current SPELL website is based on a collection of ~2400 S. cerevisiae microarray conditions. The underlying algorithm is a query-driven search utilizing a novel application of SVD for signal balancing and using the Fisher Z-transform for improved comparability between datasets.

• Hibbs MA, Hess DC, Myers CL, Huttenhower C, Li K, Troyanskaya OG. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics, 2007.

HIDRA
[Horizontally Integrated Dataset Relationship Analysis]

HIDRA is a visualization and analyis framework for simultaneously exploring multiple microarray datasets at once. HIDRA allows users to quicky identify patterns common across many datasets as well as patterns unique to individual datasets. HIDRA is currently in beta testing and is still under development.

• Hibbs MA, Dunham M, Wallace G, Li K, Troyanskaya OG. A Platform for Integrated, Scalable Analysis and Visualization of Gene Expression Microarray Data Compendia. In preparation, 2008.
• Hibbs MA, Wallace G, Dunham M, Li K, Troyanskaya OG. Viewing the Larger Context of Genomic Data through Horizontal Integration. 11th Int. Conf. on Information Visualization (IV07), 2007.

GOLEM
[Gene Ontology Local Exploration Map]

GOLEM is a tool for viewing, navigating, and analyzing the hierarchical structure and annotations to the gene ontology. The visualization component allows a user to see the local graph structure around a GO term of interest and navigate to nearby nodes. GOLEM also provides the ability to look for statistical enrichment of GO terms in lists of genes and then observe the relationships between those terms. GOLEM is available both as an applet for use online and as a standalone download.

• Sealfon RSG, Hibbs MA, Huttenhower C, Myers CL, Troyanskaya OG. GOLEM: an interactive graph-based gene ontology navigation and analysis tool. BMC Bioinformatics 2006, 7:443.

geneVAnD
[Genomic Visualization and Analysis of Datasets]

geneVAnD is an implementation of several visualization techniques that incorporate meaningful statistics that are noise-robust for the purpose of analyzing the results of clustering algorithms on microarray data. This includes a rank-based visualization method that is more robust to noise, a difference display method to aid assessments of cluster quality and detection of outliers, and a projection of high dimensional data into a three dimensional space in order to examine relationships between clusters. Our methods are interactive and are dynamically linked together for comprehensive analysis. Further, our approach applies to both protein and gene expression microarrays, and our architecture is scalable for use on both desktop/laptop screens and large-scale display devices.

• Hibbs, MA, Dirksen NC, Li K, Troyanskaya OG. Visualization Methods for Statistical Analysis of Microarray Clusters. BMC Bioinformatics, 6:115, 2005.

GRIFn
[Gene Relationship Identification in Functional data]

GRIFn is a system for evaluation of datasets and methods using a functional genomics gold standard based on curation by expert biolgists. It allows users to assess the ability of their datasets or methods to recapitulate known biology both in a global sense and in the context of specific biological processes. GRIFn allows enables fair comparisons between various data types and methods.

• Myers CL, Barrett D, Hibbs MA, Huttenhower C, Troyanskaya OG. Finding function: evaluation methods for functional genomic data. BMC Genomics 2006, 7:187.

bioPIXIE
[Biological Pathway Inference from eXperimental Interaction Evidence]

bioPIXIE is a novel system for biological data integration and visualization for S. cereviciae. It allows the user to discover interaction networks and pathways in which the user's gene(s) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data.

• Myers CL, Robson D, Wible A, Hibbs M, Chiriac C, Theesfeld CL, Dolinski K, Troyanskaya OG. Discovery of biological networks from diverse functional genomic data. Genome Biology 2005, 6(13):R114.