Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.Author summary: Understanding the individual role that genes play in life is a key issue in biomedical science. While information regarding gene functions is continuously growing, the number of genes with uncharacterized biological functions is still greater. Because of this, scientists have dedicated much of their time to build and design tools that automatically infer gene functions. One of the most promising approaches (sometimes called “phylogenomics”) attempts to construct a model of inheritance and divergence of function along branches of the phylogenetic tree that relates different members of a gene family. If the functions of one or more of the family members has been characterized experimentally, the presence or absence of these functions for other family members can be predicted, in a probabilistic framework, based on the evolutionary relationships. Previously proposed Bayesian approaches to parameter estimation have proved to be computationally intractable, preventing development of such a probabilistic model. In this paper, we present a simple model of gene-function evolution that is highly-scalable, which means that it is possible to perform parameter estimation not only on one family, but simultaneously for hundreds of gene-families, comprising thousands of genes. The parameter estimates we obtain coherently agree with what theory dictates regarding how gene-functions evolved. Finally, notwithstanding its simplicity, the model’s prediction quality is comparable to other more complex alternatives. Although we believe further improvements can be made to our model, even this simple model makes verifiable predictions, and suggests areas in which existing annotations show inconsistencies that may indicate errors or controversies.