BACKGROUND

Monitoring gene expression using high-density microarrays is a frequently-used technique in the study of cell functions and the associated biochemical pathways, candidate gene identification, cellular response to drug compounds, and classification of disease states. Because many important decisions as to whether a gene should be pursued as a candidate for a particular biological system under study are based on determined expression ratios as well as on determined differential expression, processes that provide for more accurate estimates of these derived statistics can be valuable to users of oligonucleotide array technology.

Conventional normalization methods include 1) linear normalization and nonlinear regression, and 2) methods using housekeeping genes or staggered spike-in controls. These methods have drawbacks, however. The linear normalization technique does not account well for nonlinear relations. Non-linear regression can be inadequate if the expression profiles of the various arrays vary greatly from each other. Finally, many of the genes conventionally used as housekeeping genes have ranges of differential expression similar to other genes whose differential expression patterns are deemed biologically relevant to the system under study.

DESCRIPTION

Researchers at the University of California have developed an algorithm and methodology that more accurately reflects normalized data between arrays than methods that are currently in use. This methodology is covered by US Patent 6,571,005 and is available for licensing. If you would like more information, please contact the licensing officer listed below.

Patent Status

Inventors

• Li, Cheng
• Wong, Wing H.

Contact

Sandy Auyoung / sauyoung@research.ucla.edu / tel: View Phone Number. Please reference Tech ID #10177.

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