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Functional and Comparative Genomics

While the discoveries in the field of computational genomics may seem helpful, it is important to ask what the purpose of such discoveries is, and what good they can do. The answer to this comes in the form of closely related fields called Functional and Comparative Genomics, which is the use of data gathered in Computational Genomics to identify functions of the proteins being encoded by the DNA sequences. In biochemistry, there exist many different proteins in a typical cell, but many of them have parts or domains with overlapping functions. These domains are called motifs, parts of a proteins that serve a specific function such as phosphorylation of proteins, hydrolysis of ATP, actin binding, DNA binding, etc. After identifying the gene sequences for proteins with known functions, the sequence of the gene can be stored in computer databases along with the functions of the protein that gene encodes. This allows geneticists to identify the function of newly discovered genes by comparing their sequence to a database of other sequences that encode known functional motifs, and predict the motifs that are encoded by this gene. This has known applications in fields such as immunology, where functional genomics is used to identify genes that could potentially function in foreign antigen recognition by the immune system.

In addition, the field of Comparative Genomics takes the data acquired from Computational Genomics and allows geneticists to locate genes in other species that are analogous to genes in humans. Geneticists are able to identify the function of some genes in vivo(within a living organism) using this method. When new genes are identified in humans via cDNA formation, geneticists can look for the existence of that same gene in other species. By searching computer databases with known genome sequences of other species, geneticists can determine if the gene also exists in other species, for example, in mice. Once this has been determined, geneticists can use a technique called Gene Knockout to determine the function of the gene. Because the length of time between generations in humans is around 25 years, it would be impractical (and wrong in so many other ways) to knock out genes and determine their function during development from an embryo to adulthood. Thus, the principle of Comparative Genomics is the use of model organisms such as mice, which reproduce and develop at a much faster rate than humans in order to determine the function of an analogous gene to determine the function of newly discovered genes.

One of the newest approaches to doing this is the utilization of gene sequences generated from computational genomics to activate a cellular pathway of gene silencing (RNAi) to inhibit the expression of that gene. It involves the injection of an artificially synthesized double stranded RNA transcript of the gene sequence and activation of the RISC/DICER pathway in rat embryos, an intrinsic pathway normally used by cells as defense against retroviruses. The double stranded RNA that is injected into the cell has the same sequence as a portion of the gene mRNA transcript and will bind to the protein RISC. From the on, RISC will use the "silencing RNA" to bind to mRNA transcripts via complementary base pairing. Upon binding, RISC will cleave the mRNA, resulting in a truncated mRNA, and thus a nonfunctional protein being formed.. This prevents translation of the gene, and thus prevents expression of the protein encoded by the gene.

The mice are allowed to develop and are observed to determine the effects that a loss of function in that gene had on the development and physiology of mice. From then on, geneticists can hypothesize that the gene has a similar function in humans and a mutation in it can cause the same condition observed in the knockout mice. This sort of gene identification, however, would have been impossible without the use of networks and databases in today's modern computers. The genomic and cDNA foundations are based on information stored in databases, and the ability to retrieve information and determine functionality of genes requires modern computing power to run the necessary algorithms.

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