Supporting data for "High Temporal-Resolution Nanopore Sequencing Dataset of SARS-CoV-2 and Host Cell RNAs"

Recent studies have disclosed the genome, transcriptome and epigenetic compositions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the effect of viral infection on gene expression of the host cells. It has been demonstrated that, besides the major canonical transcripts, the viral genome also codes for non-canonical RNA molecules. While the structural characterizations have revealed a detailed transcriptomic architecture of the virus, the kinetic studies provided poor and often misleading results on the dynamics of both the viral and host transcripts due to the low temporal resolution of the infection event and the low virus/cell ratio (MOI=0.1) applied for the infection. It has never been tested whether the alteration in the host gene expressions is caused by aging of the cells, or by the viral infection. <br>In this study, we used Oxford Nanopores direct cDNA and direct RNA sequencing methods for the generation of a high-coverage, high-temporal-resolution transcriptomic dataset of SARS-CoV-2 and of the primate host cells, using a high infection titer (MOI=5). Sixteen sampling time points ranging from 1 to 96 h with a varying time resolution and three biological replicates were used in the experiment. In addition, for each infected sample, corresponding non-infected samples were employed. The raw reads were mapped to the viral and to the host reference genomes, resulting in 49,661,499 mapped reads (54,62Gbs). The genome of the viral isolate was also sequenced and phylogenetically classified.<br>This dataset can serve as a valuable resource for profiling the SARS-CoV-2 transcriptome dynamics, the virus-host interactions and the RNA base modifications. Comparison of expression profiles of the host gene in the virally-infected and in non-infected cells at different time points allows to make a distinction between the effect of the aging of cells in culture and the viral infection. These data can provide useful information for potential novel gene annotations and can also be used for studying the currently available bioinformatics pipelines.