Systematic detection of co-infection and intra-host recombination in more than 2 million global SARS-CoV-2 samples.

Systematic monitoring of SARS-CoV-2 co-infections between different lineages and assessing the risk of intra-host recombinant emergence are crucial for forecasting viral evolution. Here we present a comprehensive analysis of more than 2 million SARS-CoV-2 raw read datasets submitted to the European COVID-19 Data Portal to identify co-infections and intra-host recombination. Co-infection was observed in 0.35% of the investigated cases. Two independent procedures were implemented to detect intra-host recombination. We show that sensitivity is predominantly determined by the density of lineage-defining mutations along the genome, thus we used an expanded list of mutually exclusive defining mutations of specific variant combinations to increase statistical power. We call attention to multiple challenges rendering recombinant detection difficult and provide guidelines for the reduction of false positives arising from chimeric sequences produced during PCR amplification. Additionally, we identify three recombination hotspots of Delta – Omicron BA.1 intra-host recombinants. SARS-CoV-2 coinfections may lead to recombination events which could be important in the emergence of new variants. Here, the authors develop an automated bioinformatics pipeline to identify coinfections in genomic data and test it on >2 million publicly available raw read data sets collected globally. [ABSTRACT FROM AUTHOR]