henhaobio
Sign in

RNABioinformaticsPeer-reviewed journal article

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

Andrea Di GioacchinoiD, Rachel LegendreiD, Yannis Rahou, Valérie NajburgiD, Pierre CharneauiD, Benjamin D. GreenbaumiD, Frédéric TangyiD, Sylvie van der WerfiD, Simona CoccoiD, Anastassia V. KomarovaiD

PDFDOIPreprint

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.

Citations
9
FWCI
0.47
Field percentile
top 31%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISParker '21Nomburg '20Whitworth '23Cohen '21Lyu '20Wong '21
71% semantic matchBioinformatics
Matthew D. Parker, Benjamin B. Lindsey, Shay Leary+29 more·Genome Research·Apr 1, 2021· 5 citations