1. Introduction

SelTag is one of the best-of-breed tools for analyzing gene expression data.

SelTag allows users to:

• Select genes by their expression levels or other parameters obtained in various experiments
• Sort genes by their expression levels determined in various experiments
• Visualize gene expression profiles using advanced graphical tools
• Search for genes with expression profiles similar to one or more genes
• Assess the correlation between expression profiles of two or more genes
• Cluster gene expression data by the similarity of their gene expression profiles and experiments by the similarity of gene expression levels
• Perform hierarchical clustering of genes or tissues and display the results as similarity trees
• Analyze the correlation and covariance matrices of gene expression profiles using the principal component method and visualize the results obtained
• Integrate with external databases to retrieve the data for analysis (for example, UniGene database)

Thus, using SelTag, researchers can analyze all genes and tissues or their marked groups, choose tissue-specific genes basing on integrated criteria, visualize expression data, identify the genes with a correlated expression in a given set of tissues, isolate disease-specific genes with particular characteristics, such as receptors or secreted proteins.