Levy directs the Microarray Shared Resource, which utilizes
bioinformatics not only for microarray analysis, but also to
make that analysis possible.
permeates microarray core
by Leigh MacMillan
About 1200 pages. Thats 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. Thats a lot of what bioinformatics does;
it enables people to see and recognize patterns that they cant
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, theres 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 dont 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.
Its 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,
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. Were
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.
Theyre 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 cells
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
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,