QuartetS: a fast and accurate algorithm for large-scale orthology detection

The unparalleled growth in the availability of genomic data offers both a challenge to develop orthology detection methods that are simultan-eously accurate and high throughput and an opportunity to improve orthology detection by leveraging evolutionary evidence in the accumulated sequenced genomes. Here, we report a novel orthology detection method, termed QuartetS, that exploits evolutionary evidence in a computationally efficient manner. Based on the well-established evolutionary concept that gene duplication events can be used to discriminate hom-ologous genes, QuartetS uses an approximate phylogenetic analysis of quartet gene trees to infer the occurrence of duplication events and discrim-inate paralogous from orthologous genes. We used function- and phylogeny-based metrics to perform a large-scale, systematic comparison of the orthology predictions of QuartetS with those of four other methods [bi-directional best hit (BBH), outgroup, OMA and QuartetS-C (QuartetS followed by clustering)], involving 624 bacterial genomes and>2 million genes. We found that QuartetS slightly, but consistently, outperformed the highly specific OMA method and that, while consuming only 0.5 % additional computational time, QuartetS predicted 50 % more orthologs with a 50 % lower false positive rate than the widely used BBH method. We conclude that, for large-scale phylogenetic and functional analysis, QuartetS and QuartetS-C should be preferred, respectively, in ap-plications where high accuracy and high throughput are required