Online tool provides deeper analysis of microarray data
19 Jul 2012
A new software program designed by scientists at the Stanford University enables researchers to see the whole picture of gene expression.
This development was in response to limitations of DNA microarray analysis which meant that results from separate experiments couldn’t be directly compared.
The program, named the Gene Expression Commons, is publicly available here. Its developers claim it will “transform studies of gene expression”.
“It is so simple that a researcher can just type in the name of a gene and, within seconds, see the absolute level of the expression of that gene in every cell type in a panel,” said professor of pathology Irving Weissman, MD.
“We believe that this program will rapidly become the most important tool for discovery in a number of fields, including stem cells, cancer and regenerative medicine.”
Challenges with microarray technology
In microarrays, scientists affix thousands of tiny dots of specific nucleotide sequences – each representing a different gene – to glass slides in precise patterns, or arrays.
Researchers apply a sample of interest to each slide and then can assess the relative levels of expression of each sequence in these samples.
However, because some sequences will inherently bind to their targets more or less strongly than others, it’s not possible to directly compare signal intensities among different spots on the same chip.
So researchers could learn that genes X and Y were both expressed at higher levels in one sample than in another, but they had no way of knowing how the absolute levels of X and Y compared.
“This has been a huge limitation for researchers,” said Jun Seita, MD, PhD, instructor of pathology. “We say we’re profiling gene expression levels, but that’s not really what we’ve been doing. Instead we’ve been profiling the relative differences in gene expression, and we had no way of learning more.”
Traditional microarray technology also doesn’t identify ranges of sensitivity: if expression levels are very high, for example, the signal will appear maximally intense regardless of true differences among samples.
Merging data from thousands of experiments
The new software overcomes this by merging data from thousands of experiments.
“For each gene, we get about 30,000 values,” said so-author Debashis Sahoo. “This gives us an idea of the low and high range of expression in many cell types. We can get a sense of the range of the values, and then find where any individual sample fits within this continuum.”
The Gene Expression Commons is designed as an open platform, so new users can input data themselves.
A neurologist, for example, may want to analyse data from microarrays conducted on cells of the nervous system.
And the Gene Expression Commons is open for more than DNA microarray technology. “This ’common reference’ strategy should work well for any type of high-throughput data,” said Seita. “We really acknowledge the effort to share published microarray data across the scientific community, so it’s time to give back.”
