FEATURE ARTICLE
Gene Chips and Functional Genomics
A new technology will allow environmental health scientists to track the expression of thousands of genes in a single, fast and easy test
Hisham Hamadeh, Cynthia Afshari
Analysis of Data
Image-analysis software generates a report that contains numerical data, which are more informative than the colorful spotted figure. The results are generated in a variety of formats depending on the software used. The report contains ratios calculated for each gene. These are derived from the intensity values of the two color scans. A gene expressed in equal intensities in the two samples would exhibit a ratio close to one. Using a statistical formula, the software determines the set of genes whose expression is significantly altered in the treated cells. This method is highly sensitive. It is possible to detect changes in the expression level of a gene of about 1.5 times using this technology.
The amount of data that results from these experiments can often be overwhelming. A single gene chip may generate over 10,000 data points. The volume of data grows exponentially when one uses multiple sets of chips, as is the case when replicate experiments are performed. In addition, for some biological studies it may be necessary to examine changes in gene expression over multiple time points or with varying dose levels.
The reams of data pose a considerable analytical problem, one that many biological researchers regard as the toughest part of a study. The emerging science of bioinformatics becomes very helpful in that case. Bioinformatics is the discipline in which large amounts of data are sorted into intuitive databases, analyzed and presented in an understandable form.
Analytical software draws comparisons between samples and genes across different biological conditions. One method that has been adapted for microarray data is called "clustering." Clustering programs use predefined algorithms to group significantly changed genes according to their strength of expression or pattern of expression across different experiments.
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