Applying defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs

Coronavirus RNA-dependent RNA polymerases produce subgenomic RNAs (sgRNAs) that encode viral structural and accessory proteins. The kinetics and efficiency of sgRNAs production during viral replication in different cell types or sgRNA transcription by individual viral strains or variants are yet to be studied to shed light on fundamental mechanisms necessary for viral replication. User-friendly bioinformatic tools to detect and quantify sgRNA production are urgently needed to study a growing number of next-generation sequencing (NGS) data of SARS-CoV-2. Starting from DI-tector, a bioinformatic tool for the detection of viral defective interfering genomes, here we introduced sgDI-tector to identify and quantify sgRNA in SARS-CoV-2 NGS data. This new tool allowed detection of sgRNA without initial knowledge of the transcription-regulatory sequences. As a proof of principle, we analyzed new data sets and successfully detected the nested set of sgRNAs produced with the ranking M>ORF3a>N>ORF6>ORF7a>ORF8>S>E>ORF7b. Our study also compared, for the first time for SARS-CoV-2, the level of sgRNA production with other types of viral RNA products such as defective interfering viral genomes. Overall design: three biological replicates of SARS-CoV-2 infected (MOI1)HEK293-ACE2 cells were performed.