Serial analysis of gene expression

Thursday, January 21, 2010

Serial analysis of gene expression (SAGE) is a technique used by molecular biologists to produce a snapshot of the messenger RNA population in a sample of interest in the form of small tags that correspond to fragments of those transcripts. The original technique was developed by Dr. Victor Velculescu at the Oncology Center of Johns Hopkins University and published in 1995. Several variants have been developed since, most notably a more robust version, LongSAGE, RL-SAGEand the most recent SuperSAGE that enables very precise annotation of existing genes and discovery of new genes within genomes because of an increased tag-length of 25–27 bp.

Overview
SAGE experiments proceed as follows:
1. Isolate the mRNA of an input sample (e.g. a tumour).
2. Extract a small chunk of sequence from a defined position of each mRNA molecule.
3. Link these small pieces of sequence together to form a long chain (or concatemer).
4. Clone these chains into a vector which can be taken up by bacteria.
5. Sequence these chains using modern high-throughput DNA sequencers.
6. Process this data with a computer to count the small sequence tags.
Applications
Although SAGE was originally conceived for use in cancer studies, it has been successfully used to describe the transcriptome of other diseases and in a wide variety of organisms.

Comparison to DNA microarrays
The general goal of the technique is similar to the DNA microarray. However, SAGE is a sequence-based sampling technique. Observations are not based on hybridization, which result in more qualitative, digital values. In addition, the mRNA sequences do not need to be known a priori, so genes or gene variants which are not known can be discovered. Microarray experiments are much cheaper to perform, so large-scale studies do not typically use SAGE. Quantifying gene expressions is more exact in SAGE because it involves directly counting the number of transcripts whereas spot intensities in microarrays fall in non-discrete gradients and are prone to background noise.

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