Abstract
Proteinortho is a widely used tool to predict (co)-orthologous groups of genes for any set of species. It finds application in comparative and functional genomics, phylogenomics, and evolutionary reconstructions. With a rapidly increasing number of available genomes, the demand for large-scale predictions is also growing. In this contribution, we evaluate and implement major algorithmic improvements that significantly enhance the speed of the analysis without reducing precision. Graph-based detection of (co-)orthologs is typically based on a reciprocal best alignment heuristic that requires an all vs. all comparison of proteins from all species under study. The initial identification of similar proteins is accelerated by introducing an alternative search tool along with a revised search strategy-the pseudo-reciprocal best alignment heuristic-that reduces the number of required sequence comparisons by one-half. The clustering algorithm was reworked to efficiently decompose very large clusters and accelerate processing. Proteinortho6 reduces the overall processing time by an order of magnitude compared to its predecessor while maintaining its small memory footprint and good predictive quality.