Abstract
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.
Highlights
The overwhelming majority of sequences in public databases remain experimentally uncharacterized, a trend that is increasing rapidly with the ease of modern sequencing technologies
To evaluate performance of our model, we used data from the Gene Ontology project [6], release 2016-11-01 together with PANTHER version 15.0 [20], which includes about 15,000 reconciled trees reconstructed with the GIGA algorithm [21], modified to include horizontal transfer inference as described in [22]
We presented a model for the evolution of gene function that allows rapid inference of that function, along with the associated evolutionary parameters
Summary
The overwhelming majority of sequences in public databases remain experimentally uncharacterized, a trend that is increasing rapidly with the ease of modern sequencing technologies. We developed a semi-automated method for inferring gene function based on creating an explicit model of function evolution through a gene tree [3]. This approach adopts the “phylogenetic” formulation of function prediction first proposed by Eisen [4], and the use of GO terms to describe function as implemented in the SIFTER software (Statistical Inference of Function Through Evolutionary Relationships) developed by Engelhardt et al [5]. Our semi-automated method has been applied to over 5000 distinct gene families, resulting in millions of annotations for protein coding genes from 142 different fully sequenced genomes This approach requires manual review of GO annotations, and manual construction of distinct models of gene function evolution for each of the 5000 families. The semi-automated process cannot keep up with the revisions that are constantly necessary due to continued growth in experimentally supported GO annotations
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