sgDI-tector: defective interfering viral genome bioinformatics for detection of coronavirus subgenomic RNAs

Abstract

Coronavirus RNA-dependent RNA polymerases produce subgenomic RNAs (sgRNAs) that encode viral structural and accessory proteins. User-friendly bioinformatic tools to detect and quantify sgRNA production are urgently needed to study the growing number of next-generation sequencing (NGS) data of SARS-CoV-2. We introduced sgDI-tector to identify and quantify sgRNA in SARS-CoV-2 NGS data. sgDI-tector allowed detection of sgRNA without initial knowledge of the transcription-regulatory sequences. We produced NGS data and successfully detected the nested set of sgRNAs with the ranking M>ORF3a>N>ORF6>ORF7a>ORF8>S>E>ORF7b. We also compared the level of sgRNA production with other types of viral RNA products such as defective interfering viral genomes.

sgRNAs: A SARS-CoV-2 emerging issue

“…For this purpose, RNA-Seq and amplicon-based NGS sequencing are the mainly used approaches. In the former approach, the data analysis is performed using software designed for RNA splicing identification ( di Gioacchino et al, 2022 ), such as STAR (Spliced Transcripts Alignment to a Reference)-based approaches. STAR is an RNA-Seq aligner, designed for spliced alignments, i.e.…”