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

by George G. Vega Yon, Duncan C. Thomas, John Morrison, Huaiyu Mi, Paul D. Thomas, Paul Marjoram

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 app roaches, 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-validati on, and we further validated some of the predictions in the experimental scientific literature.

Source: PLoS Computational Biology - February 18, 2021 Category: Biology Authors: George G. Vega Yon Source Type: research