Rapid and Accurate Estimation of Genetic Relatedness Between Millions of Viral Genome Pairs Using MANIAC

Abstract

Average Nucleotide Identity (ANI) is a widely used metric to estimate genetic relatedness, especially in microbial species delineation. While ANI calculation has been well optimised for bacteria and closely related viral genomes, accurate estimation of ANI below 80%, particularly in large reference datasets, has been challenging due to a lack of accurate and scalable methods. To bridge this gap, here we introduce MANIAC, an efficient computational pipeline optimised for estimating ANI and alignment fraction (AF) in viral genomes with divergence around ANI of 70%. Using a rigorous simulation framework, we demonstrate MANIACs accuracy and scalability compared to existing approaches, even to datasets of hundreds-of-thousands of viral genomes. Applying MANIAC to a curated dataset of complete bacterial dsDNA viruses revealed a multimodal ANI distribution, with a distinct gap around 80%, akin to the bacterial ANI gap ([~]90%) but shifted, likely due to viral-specific evolutionary processes such as recombination dynamics and mosaicism. We then evaluated ANI and AF as predictors of genus-level taxonomy using a logistic regression model. We found that this model has strong predictive power (PR-AUC=0.981), but that it works much better for virulent (PR-AUC=0.997) than temperate (PR-AUC=0.847) bacterial viruses. This highlights the complexity of taxonomic classification in temperate phages, known for their extensive mosaicism, and cautions against over-reliance on ANI in such cases. MANIAC can be accessed under https://github.com/bioinf-mcb/MANIAC. ImportanceWe introduce a novel computational pipeline called MANIAC, designed to accurately assess Average Nucleotide Identity (ANI) and alignment fraction (AF) between diverse viral genomes, scalable to datasets of over 100k genomes. Through the use of computer simulations and real data analyses, we show that MANIAC could ac- curately estimate genetic relatedness between pairs of viral genomes around 60-70% ANI. We applied MANIAC to investigate the question of ANI discontinuity in bacterial dsDNA viruses, finding evidence for an ANI gap, akin to the one seen in bacteria but around ANI of 80%. We then assessed the ability of ANI and AF to predict taxonomic genus boundaries, finding its strong predictive power in virulent, but not in temperate phages. Our results suggest that bacterial dsDNA viruses may exhibit an ANI threshold (on average around 80%) above which recombination helps maintain population cohesiveness, as previously argued in bacteria.