Abstract
BackgroundAccurate identification of orthologs is crucial for evolutionary studies and for functional annotation. Several algorithms have been developed for ortholog delineation, but so far, manually curated genome-scale biological databases of orthologous genes for algorithm evaluation have been lacking. We evaluated four popular ortholog prediction algorithms (MultiParanoid; and OrthoMCL; RBH: Reciprocal Best Hit; RSD: Reciprocal Smallest Distance; the last two extended into clustering algorithms cRBH and cRSD, respectively, so that they can predict orthologs across multiple taxa) against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser.ResultsExamination of sensitivity [TP/(TP+FN)], specificity [TN/(TN+FP)], and accuracy [(TP+TN)/(TP+TN+FP+FN)] across a broad parameter range showed that cRBH was the most accurate and specific algorithm, whereas OrthoMCL was the most sensitive. Evaluation of the algorithms across a varying number of species showed that cRBH had the highest accuracy and lowest false discovery rate [FP/(FP+TP)], followed by cRSD. Of the six species in our set, three descended from an ancestor that underwent whole genome duplication. Subsequent differential duplicate loss events in the three descendants resulted in distinct classes of gene loss patterns, including cases where the genes retained in the three descendants are paralogs, constituting ‘traps’ for ortholog prediction algorithms. We found that the false discovery rate of all algorithms dramatically increased in these traps.ConclusionsThese results suggest that simple algorithms, like cRBH, may be better ortholog predictors than more complex ones (e.g., OrthoMCL and MultiParanoid) for evolutionary and functional genomics studies where the objective is the accurate inference of single-copy orthologs (e.g., molecular phylogenetics), but that all algorithms fail to accurately predict orthologs when paralogy is rampant.
Highlights
Orthologous genes are homologs that originated by speciation events, whereas paralogs are homologs that originated by gene duplication events [1]
We found that CRBH almost always outperformed all other algorithms, suggesting that simpler algorithms may often perform better than more complex ones in identifying orthologs across species, but that the FALSE DISCOVERY RATE of all algorithms was dramatically increased when groups of paralogs stemming from the whole genome duplication (WGD) event were examined
We considered all genes shared between the test group and its corresponding gold group as true positive (TP), and any genes in the test group that did not belong to the gold group as false positive (FP) (Figure 2 and Text S1)
Summary
Orthologous genes are homologs that originated by speciation events, whereas paralogs are homologs that originated by gene duplication events [1]. Accurate determination of orthologs and paralogs is fundamental to molecular evolution analyses, the first step in any comparative molecular biology study, and incredibly useful for functional prediction and annotation [2,3,4,5,6]. A number of graph-based algorithms use similarity searches, such as BLAST [9], to predict groups of orthologous genes (orthogroups), either in pairwise (between two taxa) or clustering (between multiple taxa) fashion [3,6,10,11,12,13,14,15,16,17]. We evaluated four popular ortholog prediction algorithms (MULTIPARANOID; and ORTHOMCL; RBH: Reciprocal Best Hit; RSD: Reciprocal Smallest Distance; the last two extended into clustering algorithms CRBH and CRSD, respectively, so that they can predict orthologs across multiple taxa) against a set of 2,723 groups of high-quality curated orthologs from 6 Saccharomycete yeasts in the Yeast Gene Order Browser
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