Phylogenomics: Improving Functional Predictions for Uncharacterized Genes by Evolutionary Analysis

Abstract

The ability to accurately predict gene function based on gene sequence is an important tool in many areas of biological research. Such predictions have become particularly important in the genomics age in which numerous gene sequences are generated with little or no accompanying experimentally determined functional information. Almost all functional prediction methods rely on the identification, characterization, and quantification of sequence similarity between the gene of interest and genes for which functional information is available. Because sequence is the prime determining factor of function, sequence similarity is taken to imply similarity of function. There is no doubt that this assumption is valid in most cases. However, sequence similarity does not ensure identical functions, and it is common for groups of genes that are similar in sequence to have diverse (although usually related) functions. Therefore, the identification of sequence similarity is frequently not enough to assign a predicted function to an uncharacterized gene; one must have a method of choosing among similar genes with different functions. In such cases, most functional prediction methods assign likely functions by quantifying the levels of similarity among genes. I suggest that functional predictions can be greatly improved by focusing on how the genes became similar in sequence (i.e., evolution) rather than on the sequence similarity itself. It is well established that many aspects of comparative biology can benefit from evolutionary studies (Felsenstein 1985), and comparative molecular biology is no exception