Predicting Functional Gene Links from Phylogenetic-Statistical Analyses of Whole Genomes

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Abstract
An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks. A typical fully sequenced genome from a bacterial species contains several thousand genes, and those from multicellular animals may contain many thousands of genes. Understanding the function of these genes is one of the key goals of the developing fields of bioinformatics and proteomics, and the results are of interest to life scientists. The authors describe a computational statistical method that can identify pairs of genes whose functions may be linked, in the sense of participating in a common metabolic pathway or from some physical interaction. The method is applied to phylogenetic trees of related organisms and identifies instances in which a pair of genes is either gained or lost together during evolution. They find that genes that have co-evolved like this on two or more occasions during their evolutionary history are almost certainly functionally linked. These methods can be applied in an automated way to large numbers of species for which fully annotated genomes are available to identify candidate sets of functionally linked genes, and to characterize gene networks.