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.

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.

ChARMview
[Chromosomal Abberation Region Miner and Viewer]

ChARMView is a visualization and analysis system for guided discovery of chromosomal abnormalities from microarray data. Our system facilitates manual or automated discovery of aneuploidies through dynamic visualization and integrated statistical analysis. ChARMView can be used with array CGH and gene expression microarray data, and multiple experiments can be viewed and analyzed simultaneously.

• Myers CL, Chen X, Troyanskaya OG. Visualization-based discovery and analysis of genomic abberations in microarray data. BMC Bioinformatics, 6:146, 2005.
• Myers CL, Dunham M, Kung SY, Troyanskaya OG. Accurate detection of aneuploidies in array CGH and gene expression microarray data. Bioinformatics, 20:3533-3543, 2004.

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.

KNNimpute
[K-Nearest Neighbors Imputation]

KNNimpute is an implementation of the k-nearest neighbors algorithm for estimation of missing values in microarray data. In our comparative study of several different methods used for missing value estimation we determined that KNNimpute provides superior performance in a variety of situations.

• Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB. Missing value estimation methods for DNA microarrays. Bioinformatics, 17:520-5, 2001.