EASE is a customizable software tool for rapidly interpreting gene lists derived from high-throughput genomic data, such as microarray, proteomics, and SAGE. It identifies biological themes by analyzing gene over-representation in various classification systems, including the Gene Ontology, SWISS-PROT, and others. EASE provides a systematic way to convert gene lists into biological themes, which are robust to different normalization and statistical methods. The software uses customizable text files for gene identification, annotation, and linking to online tools. It also includes a feature for calculating over-representation using the Fisher exact probability and a modified score (EASE score) that adjusts for multiple comparisons, making it more reliable for identifying significant biological themes. EASE has been tested against manually determined themes in published studies, showing that it can accurately recapitulate these themes. For example, in a study on primate muscle, EASE identified themes consistent with those manually determined by Kayo et al. Similarly, in proteomics and SAGE studies, EASE found themes that matched the manually predicted ones. The software is also robust to variations in gene selection methods, as demonstrated by its ability to consistently identify similar biological themes across different gene lists derived from the same experiment. EASE offers several advantages over similar tools like MAPPfinder and GoMiner. It provides a user-friendly interface, allows for easy customization, and includes features such as automated updates and the ability to link to online tools. It is also more statistically rigorous, using the EASE score to adjust for multiple comparisons and providing more accurate probability estimates. EASE is freely available to non-profit researchers and can be used on Windows operating systems, with an online version available on the DAVID website. Overall, EASE is a powerful tool for interpreting high-throughput genomic data and identifying biological themes that can guide further research.EASE is a customizable software tool for rapidly interpreting gene lists derived from high-throughput genomic data, such as microarray, proteomics, and SAGE. It identifies biological themes by analyzing gene over-representation in various classification systems, including the Gene Ontology, SWISS-PROT, and others. EASE provides a systematic way to convert gene lists into biological themes, which are robust to different normalization and statistical methods. The software uses customizable text files for gene identification, annotation, and linking to online tools. It also includes a feature for calculating over-representation using the Fisher exact probability and a modified score (EASE score) that adjusts for multiple comparisons, making it more reliable for identifying significant biological themes. EASE has been tested against manually determined themes in published studies, showing that it can accurately recapitulate these themes. For example, in a study on primate muscle, EASE identified themes consistent with those manually determined by Kayo et al. Similarly, in proteomics and SAGE studies, EASE found themes that matched the manually predicted ones. The software is also robust to variations in gene selection methods, as demonstrated by its ability to consistently identify similar biological themes across different gene lists derived from the same experiment. EASE offers several advantages over similar tools like MAPPfinder and GoMiner. It provides a user-friendly interface, allows for easy customization, and includes features such as automated updates and the ability to link to online tools. It is also more statistically rigorous, using the EASE score to adjust for multiple comparisons and providing more accurate probability estimates. EASE is freely available to non-profit researchers and can be used on Windows operating systems, with an online version available on the DAVID website. Overall, EASE is a powerful tool for interpreting high-throughput genomic data and identifying biological themes that can guide further research.