SBIR-STTR Award

The goal of this work is to provide a software workbench for scientists trying to characterize sequenced genes using GeneChips, Micro-arrays or similar technologies. The workbench will allow classification, archiving, analysis and visualization of a variety of experiments. This is important as the current tools are insufficient, and scientists have too much data to analyze manually. This phase I application focuses upon analysis and visualization, particularly for time series and other multiple condition, expression experiment. The visualization includes animation and graphing; the analysis will include cluster analysis, and condition identification, such as "over-expressed in experiments A and B relative to C and D." We believe this software would contribute to the overall goals of the Human Genome Project by assisting researchers to identify and characterize genes on a large (genomic) scale. PROPOSED COMMERCIAL APPLICATION: Production of software to aid in gene function identification through expression studies. Production of software to aid in biochemical function identification through expression studies.

This project will result in a powerful, user friendly, software workbench for scientists trying to determine gene function or behavior though microarrays or Affymetrix chips. The workbench will help scientists deal with the millions or billions of numbers generated in these experiments, which cannot be analyzed by hand. An important part of this project is automatically identifying features of interest to the scientist in large data sets. This will save enormous amounts of time, and allow analyses that would never otherwise be done. The technologies developed here will be instrumental in allowing genomic expression experiments to scale up and provide useful information on the function and behavior of every identified gene. PROPOSED COMMERCIAL APPLICATIONS: Production of software to aid in gene function identification through expression studies Production of software to aid in biochemical function identification through expression studies