Free online tool to provide deeper analysis of microarray data
A new software programme enables researchers to see the whole picture of gene expression in a sample
- Date : 23 Jul 2012
- Topic : Personalised medicine
Until now, researchers who attempt to piece together complex biological processes by analysing changes in the levels of expression of genes in cells experienced some difficulties. Limitations with the DNA microarray analysis didn’t allow a direct comparison of results from separate experiments. Now, a new software programme designed by scientists at the Stanford University School of Medicine enables researchers to see the whole picture of gene expression in a sample. The programme, called the Gene Expression Commons, is publicly available at https://gexc.stanford.edu/. Its developers expect from it to transform studies of gene expression.
It is exciting that a researcher is able to 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. Dr Irving Weissman, professor of pathology and his team believe that this programme will rapidly become the most important tool for discovery in a number of fields, including stem cells, cancer and regenerative medicine. Dr Weissman, director of Stanford's Institute for Stem Cell Biology and Regenerative Medicine, is the senior author of the research, which was published July 18 in PLoS ONE. He is also the Virginia and D.K. Ludwig Professor for Clinical Investigation in Cancer Research at Stanford, and a member of the Stanford Cancer Institute. Dr Jun Seita, instructor of pathology, is the first author of the study.
The Gene Expression Commons overcomes an inherent shortcoming of microarray technology
The new tool overcomes the fact that experimental results are delivered as relative differences in gene expression within individual experiments, rather than absolute values that can be compared among many samples.
The study researchers hit on the idea of analysing collections of thousands of publicly available DNA microarray experiments. About 25000 of the experiments had been performed with human data; 10000 with data from mice. Individually, each data set suffered from the same drawbacks described above. But together they can be viewed as a continuum, or a stable common reference.
The resulting Gene Expression Commons maps data submitted by the user onto this common reference, and returns absolute expression levels that can then be compared among many combinations of samples. To test their idea, the Weissman group performed and submitted microarray data from 39 highly purified, distinct cell types in the blood and immune system to the programme. Now any researcher can explore the expression pattern of any gene in the system with just a few clicks of a computer mouse.
Microarray technology was developed at Stanford in the 1990s. At its heart, it relies on the fact that single, complementary (or matching) strands of nucleotides are driven to bind lengthwise to one another. 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.
The distinction can be biologically important. For example, a difference in the absolute number of RNA molecules from 10 to 20 would appear similar to an increase from 10000 to 20000, although the latter is far more likely to more-profoundly affect cellular function.
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. The new software overcomes this by merging data from thousands of experiments. For each gene, the researchers get about 30000 values.
This method takes raw data from individual microarrays and normalizes them against a global database, or common reference. It gives an instant readout of absolute expression levels.
The Gene Expression Commons is designed as an open platform, so new users can input data themselves. And the Gene Expression Commons is open for more than DNA microarray technology. It should work well for any type of high-throughput data.
This study was funded by the USA National Institute of Health and the California Institute for Regenerative Medicine. The Stanford's Department of Pathology also supported the work.
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