5/16/2023 0 Comments Microarray probe to gene r taplyFinally, all of the expression inferences must be integrated with everything else that is known about the genes, culled from text databases and proteomic experiments and from the investigator's own stores of biological insight. Or we may combine expression studies with genotyping and surveys of regulatory sequences to investigate the mechanisms that are responsible for similar profiles of gene expression. Similarly, we might want to evaluate whether some subset of genes show similar expression profiles and so form natural clusters of functionally related genes. More and more, investigators are interested not just in asking how large the magnitude of an expression difference is, but whether it is significant, given the other sources of variation in the experiment. Having performed the experiment, quality control checks, statistical analysis, and data-mining are performed. If, however, a difference in transcript abundance is observed between two or more conditions, it is natural to infer that the difference might point to an interesting biological phenomenon.Ī general approach to performing gene expression profiling experiments is indicated as a flow diagram in Figure 1. This is because the visualization is done at the level of transcript abundance, but just seeing a transcript does not guarantee that the protein is produced or functional. It is the comparison of gene expression profiles that is usually of most interest. The output of all microarray hybridizations is ultimately a series of numbers, which covers a range of almost four orders of magnitude, from perhaps one transcript per ten cells to a few thousand transcripts per cell ( Velculescu 1999). With appropriate replication, normalization, and statistics, though, quantitative differences in abundance as small as 1.2-fold can readily be detected. Trivially, if fluorescence is observed for a gene in one population but not another, the gene can be inferred to be on or off, respectively. So a microarray is a massively parallel way to survey the expression of thousands of genes from different populations of cells. The intensity of the signal produced by 1,000 molecules of a particular labeled transcript should be twice as bright as the signal produced by 500 molecules and, similarly, that produced by 10,000 molecules half as bright as one produced by 20,000 molecules. Once the microarray is constructed, the target mRNA population is labeled, typically with a fluorescent dye, so that hybridization to the probe spot can be detected when scanned with a laser. The idea of a microarray is simply to lay down a field of thousands of these probes in perhaps a 5 sq cm area, where each probe represents the complement of at least a part of a transcript that might be expressed in a tissue. And in some cases, the results of a microarray screen that was initially designed as an effort at cataloguing expression differences are so unexpected that they immediately suggest novel conclusions and areas of enquiry.Īll microarray experiments rely on the core principle that transcript abundance can be deduced by measuring the amount of hybridization of labeled RNA to a complementary probe. In general, without a hypothesis only the most obvious features of a complex dataset will be seen, while clear formulation of the scientific question undoubtedly fuels better experimental design. In many gene expression profiling experiments, the hypotheses being addressed are genome-wide integrative ones rather than single-gene reductionist queries. Hypothesis generation is just as important as testing, and very often expression profiling provides the necessary shift in perspective that will fuel a new round of progress. The notion that this is the major objective of microarray studies has engendered the oft-repeated criticism that the approach only amounts to “fishing expeditions.” The sophistication of microarray analysis very much blurs the distinction between hypothesis testing and data gathering. Gene expression profiling has moved well beyond the simple goal of identifying a few genes of interest. The output of a microarray experiment is called a “gene expression profile.” Microarrays are simply a method for visualizing which genes are likely to be used in a particular tissue at a particular time under a particular set of conditions. As the technology becomes more accessible, microarray analysis is finding applications in diverse areas of biology. Applied creatively, they can be used to test as well as generate new hypotheses. Microarrays are used to survey the expression of thousands of genes in a single experiment.
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