Gene expression analysis is most simply described as the study of the way genes are transcribed to synthesize functional gene products, functional RNA species or protein products. Gene expression is the process by which genetic instructions are used to synthesize gene products. Measuring this gene expression is a key element in the study of life sciences.
Gene expression measurement is usually achieved by quantifying levels of the gene product, which is often a protein. However, the gene expression level can also be inferred by measuring the level of mRNA, which is achieved using a technique called Northern blotting.
Another technique for measuring mRNA is reverse transcription followed by quantitative polymerase chain reaction (RT-qPCR). Here, a DNA template is made from mRNA using reverse transcription. This template, which is called cDNA (complementary DNA) is then amplified. As the DNA amplification proceeds, hybridization probes emit changing levels of fluorescence, which can be used to measure the original number of mRNA copies.
Differential expression analysis means taking the normalized read count data and performing statistical analysis to discover quantitative changes in expression levels between experimental groups. For example, we use statistical testing to decide whether, for a given gene, an observed difference in read counts is significant, that is, whether it is greater than what would be expected just due to natural random variation.
There are different methods for differential expression analysis such as edgeR and DESeq based on negative binomial (NB) distributions or baySeq and EBSeq which are Bayesian approaches based on a negative binomial model. It is important to consider the experimental design when choosing an analysis method. While some of the differential expression tools can only perform pairwise comparison, others such as edgeR, limma-voom, DESeq and maSigPro can perform multiple comparisons.
Absolute quantitation techniques such as serial analysis of gene expression (SAGE) use tags, calculating the number of tags for a given sequence by the total number of tags to gain the overall abundance of a gene product. Microarrays are also very popular for large scale gene expression analysis comparisons, allowing for tens of thousands of targets to be compared simultaneously. The most popular approach is to compare expression patterns from a sample representing the condition of interest to the same set of genes from a reference sample, thus relative expression levels between the two populations can be calculated.
When a virus infects a human cell, it successfully incorporates its DNA or RNA into the genome and forces the cell to manufacture viral proteins through expression of the viral genes. Measuring this gene expression can help in understanding of the viral mechanism as well as providing a useful tool in the development of vaccines.
Measuring the expression level of cancer-causing genes (oncogenes) can help to determine a person’s susceptibility to cancer. For example, tumor suppressor genes code for a protein that can prevent the development of tumors. When these genes are under-expressed, the cancer risk is increased.
The most common use of gene expression analysis is to compare expression levels of one or more genes from different samples.
Interesting and common comparisons include:
- Normal vs Disease
- Mutant vs Wild-type
- Before and after treatment
- Time course during development or across treatment regime
- Spatial variation within organs, tissues or other sample types