RIO: Analyzing proteomes by automated phylogenomics using resampled inference of orthologs

Abstract

BackgroundWhen analyzing protein sequences using sequence similarity searches, orthologous sequences (that diverged by speciation) are more reliable predictors of a new protein's function than paralogous sequences (that diverged by gene duplication). The utility of phylogenetic information in high-throughput genome annotation ("phylogenomics") is widely recognized, but existing approaches are either manual or not explicitly based on phylogenetic trees.ResultsHere we present RIO (Resampled Inference of Orthologs), a procedure for automated phylogenomics using explicit phylogenetic inference. RIO analyses are performed over bootstrap resampled phylogenetic trees to estimate the reliability of orthology assignments. We also introduce supplementary concepts that are helpful for functional inference. RIO has been implemented as Perl pipeline connecting several C and Java programs. It is available at http://www.genetics.wustl.edu/eddy/forester/. A web server is at http://www.rio.wustl.edu/. RIO was tested on the Arabidopsis thaliana and Caenorhabditis elegans proteomes.ConclusionThe RIO procedure is particularly useful for the automated detection of first representatives of novel protein subfamilies. We also describe how some orthologies can be misleading for functional inference.

Automated Protein Subfamily Identification and Classification

“…Methods developed for phylogenomic inference that use species information in conjunction with phylogenetic tree analysis to identify orthologs include RIO [29], Orthostrapper [28], and SIFTER [27]. While functional inference based on orthology is likely to have the highest specificity, this requirement is effectively quite limiting in practical application.…”

“…Many of these automate the process of collecting, aligning, and clustering homologs, but do not actually assign function [19–24]. Others focus primarily on the assignment of function [25–29]. While each of these approaches has distinct advantages, none are designed for fast classification of novel sequences or identification of new functional subtypes.…”

“…Existing methods for de novo identification of specific subtypes fall into two camps: those that define clusters using pairwise similarity, e.g., InParanoid [24], OrthoMCL [21], Ncut [25], CD-HIT [22,34], and those that cluster by cutting a phylogenetic or hierarchical tree, e.g., RIO [29], Orthostrapper [28], Secator [35], SCI-PHY.…”

“…However, most profile/HMM libraries (e.g., PFAM [40], the NCBI CDD [41], SMART [42], etc.) have focused on modeling large diverse clusters of proteins spanning many different functions, enabling high sensitivity, but affording only a fairly coarse level of functional annotation [29]. …”

Protein Molecular Function Prediction by Bayesian Phylogenomics

“…Phylogenetic information, if leveraged correctly, addresses many of the weaknesses of sequence-similarity-based annotation transfer [16], such as ignoring variable mutation rates [17,18]. Orthostrapper [19] and RIO [20] provide examples of methods that exploit phylogenetic information, but these methods simplify the problem by extracting pairwise comparisons from the phylogeny, and by using heuristics to convert these comparisons into annotations. SIFTER is a more thoroughgoing approach to automating phylogenomics that makes use of a statistical model of molecular function evolution to propagate all observed molecular function annotations throughout the phylogeny.…”