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Shawn Levy directs the Microarray Shared Resource, which utilizes bioinformatics not only for microarray analysis, but also to make that analysis possible.

Bioinformatics permeates microarray core

by Leigh MacMillan

About 1200 pages. That’s how many pages of data the average microarray experiment produces. “You can imagine the inefficiency that would be involved if you tried to read through one of these data files without using a computer,” says Shawn Levy, director of the Vanderbilt Microarray Shared Resource.

Using a computer, using bioinformatics tools, to analyze microarray data is not just efficient, it is essential. “Bioinformatics reduces the dimensionality of the data down to something comprehensible,” Levy says. “That’s a lot of what bioinformatics does; it enables people to see and recognize patterns that they can’t see without a computer.”

Like the pattern of gene expression changes in a tumor sample compared to a normal tissue sample.

Microarrays have made their mark as a tool for examining gene expression, particularly in the fields of cancer and autoimmune diseases, Levy says. The tens of thousands of DNA “spots” on a single array allow investigators to probe entire genomes. “Assuming a mammalian gene number of somewhere in the range of 30,000 to 50,000,” Levy says, “microarrays can offer a true genetic snapshot of a cell, or of an organ, or of a patient biopsy.”

With single experiments that result in over a million collected data points, the need for sophisticated bioinformatic analysis tools is obvious. These tools range from pre-packaged software – with modifications – to custom-tailored programs.

In the world of microarrays, bioinformatics impacts more than data analysis. “We have a very large need for bioinformatics to make the analysis possible,” Levy says.

It starts with the annotation of the libraries of clones used to make the microarrays. “What gene are you looking at, what is its sequence, what protein does it produce, what is its function, where is it expressed in the cell...you can see how the information explodes,” Levy explains.

And then, he says, there’s the issue of keeping track of clones that are in 96 well plates for PCR before they get spotted onto microscope slides at a density of 10,000 spots per slide. Which spot is the DNA from well B7 on plate 4?

These considerations don’t begin to take into account the bioinformatics associated with the samples that come in. What is the source of the RNA? How was it isolated? How was it labeled? What technologies were used to hybridize, wash and scan the microarrays?

The microarray field is in the process of developing standards for all of these issues, Levy says, which is important because the technology used to arrive at a final answer impacts that answer. It’s not like DNA sequencing where there is a definitive “right” answer, Levy points out. “Microarray technology is not at the point yet where you could send the same RNA sample to five different labs and get exactly the same answer, and this puts a lot more weight on the shoulders of the informatics that support it.”

The Vanderbilt Microarray Shared Resource is focusing its bioinformatics development efforts on tools for data management and access – tools that create an “electronic lab notebook” of sorts, Levy says.

He plans for the tools to offer interactive ways for users to keep track of what samples were run, which comparisons were made, which microarray was used, and what genes are on the microarray. “We’re trying to create the tools that pave the road to analysis,” he says. And following the analysis, when there is a putative answer, the tools Levy and colleagues are developing will offer high throughput ways for users to understand the genes that have been identified.

Other Vanderbilt investigators, including groups in the Program in Human Genetics (Jason Moore) and in Biostatistics (Yu Shyr) are developing new bioinformatics tools for microarray data analysis. “They’re really on the cutting edge of microarray analysis tools,” Levy says. “We do more of the bioinformatic bookkeeping.”

In addition to creating new bioinformatics tools, Levy and colleagues in the microarray core are working to improve microarray technology and to develop new applications. Particularly promising, Levy says, are techniques that allow investigators to use a single cell’s worth of RNA for gene expression profiling. “This opens up the clinical biopsy arena,” Levy says. “With properly handled tissue from a needle biopsy, we can produce a gene expression report.”

Microarrays are also being applied to efforts to detect chromosomal abnormalities, like changes in DNA copy number, and to sequence DNA and detect single nucleotide polymorphisms (SNPs). The fluorescent technologies used for microarray studies can be adapted to applications like in situ hybridizations that traditionally relied on radioactivity, Levy says.

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