A microarray is a sequence of DNA, protein or tissue dots arranged on an array for simultaneous analysis. The most famous is the DNA microarray, which plays a vital role in gene expression profiling. Microarrays can be constructed in various ways and are used frequently in the analysis of an organism’s entire genome. However, there is a lack of standards and data overflow can make it difficult to make sense of the results.
A microarray is a sequence of DNA, protein or tissue dots arranged on an array for easy simultaneous analysis. The most famous is the DNA microarray, which plays a vital role in gene expression profiling. The substrate material is glass, plastic or a silicon chip. Alternative names for DNA microarray include gene chip, DNA chip, and biochip. The small fragments of DNA are called probes.
A microarray can be constructed in various ways. The most primitive is simply dropping DNA onto glass slides using pins. More sophisticated techniques use photolithography, electrochemical approaches and even inkjet printing, all of which can deliver DNA in a precise spot with precision.
DNA microarrays are a product of the late 1980s and early 1990s when biotechnology really started to take off. The microarray allows a researcher to run a sequence of tests on all samples simultaneously, greatly speeding research. Traditional biological experimentation works on things one at a time. The probes are sometimes marked with dots called fluorophores when they can’t be easily distinguished.
Some of the largest microarrays, made by companies like NimbleGen Systems, hold as many as 390,000 spots. Microarray is used frequently in the analysis of an organism’s entire genome, which may contain dozens of chromosomes or more, each with its own DNA and RNA. Unfortunately, not all microarrays are compatible with each other and there is a manifest lack of standards. Standardization efforts are ongoing, but without much success so far.
Like most cutting-edge biotech research programs, microarray testing can produce massive amounts of data, giving rise to the field of bioinformatics, which tries to make sense of it all. There may be discoveries and correlations that we have already measured, but simply not noticed due to data overflow. Data mining is widely used to analyze the results of these complex experiments.
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