The precision of microarray assays has been a significant concern among end users, since the tests can produce a high level of false positives and false negatives. Standardization is also receiving a lot of attention from researchers-especially with the need to compare data obtained from the different platforms now available.

This lack of reliability and standardization can be a significant barrier to the adoption of microarrays in industries that traditionally require robust and cost-effective test platforms with a high degree of precision.

Standardization can be achieved if developers can agree on a unified technology platform to design, process, read, and analyze DNA arrays. One innovation is "gene expression mark-up language" (GEML), an open-standard format that preserves expression profile information even when used under different database schemes.

The Microarray Markup Language (MAML) Working Group has created the Microarray Gene Expression Database (MGED) to provide a standard platform for submitting and analyzing the vast amounts of microarray expression data generated by different laboratories.

MAML's goal is to facilitate the adoption of standards for DNA-array annotation and data representation, as well as the introduction of standard experimental controls and data normalization methods.

"The goal of MAML is to enable the establishment of gene data repositories, so that data from different sources can be compared and analyzed, and so that the diffe-rent software and database platforms can be made to be interoperable," says Technical Insights Research Analyst Katherine Austin.

Both academic and commercial researchers have realized that the huge amounts of data on a single chip can be cumbersome to manage and use. This problem grown with researchers beginning to study the proteins produced by the genes. The quantity of data generated could soon swamp the systems used to analyze them.

Advances in bioinformatics expect to help in the standardization of storage, sharing, and publishing platforms to aid developers in coping with the profusion of data. This techno-logy deals with the design of computer hardware/soft-ware and data-storage and analysis platforms that enable researchers to access and evaluate their results.