Functional Genomics Information Resources

Functional Genomics

Once various genome projects had been completed, the research paradigm moved away from merely mapping the genome, towards actively understanding gene functions, known as functional genomics. The field specifically derives many key concepts from molecular biology, making use of the array of data produced by genomic projects to describe gene and protein functioning and interaction. The development and application of genome-wide experimental approaches to assess gene function is the focus of the discipline, which utilises data provided by structural genomics. The strategy employed by functional genomics researchers is to study all genes and proteins at once in a highly methodical fashion, escalating the remit of their investigation.

The function of most of the estimated 30,000 genes within the human body is still unknown, providing an opportunity for significant research in this field. Understanding what genes do is the primary scope of functional genomics (thus the name - functional). Research in the field of functional genomics utilises ‘model organisms’ such as mice, bacterium, yeast, roundworm and the fruit fly since these have relatively simplistic genomes and also since inheritable characteristics can be traced through multiple generations within a relatively short time period. Mice have more complex DNA structures than worms, flies or bacterium, having roughly the same number of nucleotides in their genomes, and around the same number of genes. There are only a few cases where no mouse counterpart can be found for a particular human gene (around 1%). Other advantages of using mice in laboratory research is that they reproduce rapidly, have relatively short life spans, are inexpensive and can be handled easily, as well as being able to have their genes manipulated at a molecular level.

Various technological advances have been developed within the field of functional genomics including conditional or tissue-specific gene expression within animal models. Another area of developing technological interest is the down-regulating of gene expression to study uninhibited functional potential. The data gathered from functional genomic experiments is liable to large amounts of noise. One method of combating these effects is to take averages from a spectrum of genes, which are then divided into broad categories for analysis. This minimises the effect of noise on each individual gene, since the noise will vary for each gene, lowering the average noise level per gene. Similarly, rather than looking at the expression of an individual gene over time, taking an average of all genes eludes more robust conclusions about the degree that a functional gene system changes over time.

One of the most recent and notable advances in the field of functional genomics is the notion that complex cellular systems can be modelled mathematically, so that predictions drawn from such models can be tested experimentally. This approach is called systems biology, which rests on the premise that generating and screening thousands of mutations in order to characterise phenotypes is facile and that modelling should be used before the experimental stage. This new concept will surely save considerable amounts of research time, allowing for greater emphasis on analysis of findings.
.