Large-scale inference of gene function through phylogenetic annotation of Gene Ontology terms: case study of the apoptosis and autophagy cellular processes.

Paul D. Thomas,Huaiyu Mi,Marc Feuermann,Suzanna E. Lewis,Pascale Gaudet

doi:10.1093/database/baw155

Abstract

We previously reported a paradigm for large-scale phylogenomic analysis of gene families that takes advantage of the large corpus of experimentally supported Gene Ontology (GO) annotations. This ‘GO Phylogenetic Annotation’ approach integrates GO annotations from evolutionarily related genes across ∼100 different organisms in the context of a gene family tree, in which curators build an explicit model of the evolution of gene functions. GO Phylogenetic Annotation models the gain and loss of functions in a gene family tree, which is used to infer the functions of uncharacterized (or incompletely characterized) gene products, even for human proteins that are relatively well studied. Here, we report our results from applying this paradigm to two well-characterized cellular processes, apoptosis and autophagy. This revealed several important observations with respect to GO annotations and how they can be used for function inference. Notably, we applied only a small fraction of the experimentally supported GO annotations to infer function in other family members. The majority of other annotations describe indirect effects, phenotypes or results from high throughput experiments. In addition, we show here how feedback from phylogenetic annotation leads to significant improvements in the PANTHER trees, the GO annotations and GO itself. Thus GO phylogenetic annotation both increases the quantity and improves the accuracy of the GO annotations provided to the research community. We expect these phylogenetically based annotations to be of broad use in gene enrichment analysis as well as other applications of GO annotations.Database URL: http://amigo.geneontology.org/amigo

Highlights

The Gene Ontology (GO) is an international effort between multiple groups aimed at describing the functions of gene products in a uniform manner [1]
To illustrate our process-based phylogenetic annotation approach, we show our results from the annotation of two wellcharacterized processes, apoptosis and autophagy
The curator infers the most likely point in evolution at which a GO function first evolved, taking into account the distribution of that function among experimentally characterized genes. This model is used in a second step to create inferred annotations over the entire tree, with each function being inherited from the ancestor(s) in which it first evolved, as determined in step 1

Summary

Introduction

The Gene Ontology (GO) is an international effort between multiple groups aimed at describing the functions of gene products in a uniform manner [1]. The domains are manually assigned putative functions, and these functions are propagated automatically onto proteins containing those specific domains Another strategy is to use the evolutionary relations between sequences to predict function based on the known roles of members of the same phylogenetic group, for example the EnsemblCompara-GeneTrees method PAINT annotations are done manually, with curators reviewing all available data to construct an explicit evolutionary model This construction process includes selectively choosing which particular annotations can be propagated based on other available, relevant evidence, such as the conservation of important residues and the conservation of processes across different taxonomic ranges. To demonstrate the usefulness of PAINT in providing a large number of high confidence annotations and a coherent annotation corpus across representative species, we annotated the families of those proteins involved in two well conserved, well characterized processes: apoptosis and autophagy. Both processes are evolutionarily quite ancient, which allows inference of protein function across large phylogenetic distances

Results and discussion

Phospholipid scramblase

Protects ATG13 from proteasomal degradation

Methods