Hotspots of rapid genome evolution hold clues about human adaptation. We present a comparative analysis of nine whole-genome sequenced primates to identify high-confidence targets of positive selection. We find strong statistical evidence for positive selection in 331 protein-coding genes (3%), pinpointing 934 adaptively evolving codons (0.014%). Our new procedure is stringent and reveals substantial artefacts (20% of initial predictions) that have inflated previous estimates. The final 331 positively selected genes (PSG) are strongly enriched for innate and adaptive immunity, secreted and cell membrane proteins (e.g. pattern recognition, complement, cytokines, immune receptors, MHC, Siglecs). We also find evidence for positive selection in reproduction and chromosome segregation (e.g. centromere-associated CENPO, CENPT), apolipoproteins, smell/taste receptors and mitochondrial proteins. Focusing on the virus–host interaction, we retrieve most evolutionary conflicts known to influence antiviral activity (e.g. TRIM5, MAVS, SAMHD1, tetherin) and predict 70 novel cases through integration with virus–human interaction data. Protein structure analysis further identifies positive selection in the interaction interfaces between viruses and their cellular receptors (CD4-HIV; CD46-measles, adenoviruses; CD55-picornaviruses). Finally, primate PSG consistently show high sequence variation in human exomes, suggesting ongoing evolution. Our curated dataset of positive selection is a rich source for studying the genetics underlying human (antiviral) phenotypes. Procedures and data are available at https://github.com/robinvanderlee/positive-selection.

Insights into the evolution of ancestral complexes and pathways are generally achieved through careful and time-intensive manual analysis often using phylogenetic profiles of the constituent proteins. This manual analysis limits the possibility of including more protein-complex components, repeating the analyses for updated genome sets, or expanding the analyses to larger scales. Automated orthology inference should allow such large scale analyses, but substantial differences between orthologous groups generated by different approaches are observed.We evaluate orthology methods for their ability to recapitulate a number of observations that have been made with regards to genome evolution in eukaryotes. Specifically, we investigate phylogenetic profile similarity (co-occurrence of complexes), the Last Eukaryotic Common Ancestor's gene content, pervasiveness of gene loss, and the overlap with manually determined orthologous groups. Moreover, we compare the inferred orthologies to each other.We find that most orthology methods reconstruct a large Last Eukaryotic Common Ancestor, with substantial gene loss, and can predict interacting proteins reasonably well when applying phylogenetic co-occurrence. At the same time derived orthologous groups show imperfect overlap with manually curated orthologous groups. There is no strong indication of which orthology method performs better than another on individual or all of these aspects. Counterintuitively, despite the orthology methods behaving similarly regarding large scale evaluation, the obtained orthologous groups differ vastly from one another. Availability and implementationThe data and code underlying this article are available in github and/or upon reasonable request to the corresponding author: https://github.com/ESDeutekom/ComparingOrthologies. Summary •We compared multiple orthology inference methods by looking at how well they perform in recapitulating multiple observations made in eukaryotic genome evolution.• Co-occurrence of proteins is predicted fairly well by most methods and all show similar behaviour when looking at loss numbers and dynamics.• All the methods show imperfect overlap when compared to manually curated orthologous groups and when compared to orthologous groups of the other methods. •Differences are compared between methods by looking at how the inferred orthologies represent a high-quality set of manually curated orthologous groups. •We conclude that all methods behave similar when describing general patterns in eukaryotic genome evolution. However, there are large differences within the orthologies themselves, arising from how a method can differentiate between distant homology, recent duplications, or classifying orthologous groups.

6Protein complexes from the oxidative phosphorylation (OXPHOS) system are assembled with the help of 1 7 proteins called assembly factors. We here delineate the function of the inner mitochondrial membrane protein 1 8

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