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Article Abstract

During the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), positive-sense genomic RNA and subgenomic RNAs (sgRNAs) are synthesized by a discontinuous process of transcription characterized by a template switch, regulated by transcription-regulating sequences (TRS). Although poorly known about makeup and dynamics of sgRNAs population and function of its constituents, next-generation sequencing approaches with the help of bioinformatics tools have made a significant contribution to expand the knowledge of sgRNAs in SARS-CoV-2. For this scope to date, Periscope, LeTRS, sgDI-tector, and CORONATATOR have been developed. However, limited number of studies are available to compare the performance of such tools. To this purpose, we compared Periscope, LeTRS, and sgDI-tector in the identification of canonical (c-) and noncanonical (nc-) sgRNA species in the data obtained with the Illumina ARTIC sequencing protocol applied to SARS-CoV-2-infected Caco-2 cells, sampled at different time points. The three software showed a high concordance rate in the identification and in the quantification of c-sgRNA, whereas more differences were observed in nc-sgRNA. Overall, LeTRS and sgDI-tector result to be adequate alternatives to Periscope to analyze Fastq data from sequencing platforms other than Nanopore.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10520259PMC
http://dx.doi.org/10.26508/lsa.202302017DOI Listing

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Article Synopsis
  • The study focuses on the replication of SARS-CoV-2, specifically how genomic and subgenomic RNAs are synthesized through a complicated transcription process that involves template switching.
  • Next-generation sequencing and bioinformatics tools have been utilized to better understand the population and functions of subgenomic RNAs, leading to the development of tools like Periscope, LeTRS, sgDI-tector, and CORONATATOR.
  • The comparison of Periscope, LeTRS, and sgDI-tector revealed that all three tools performed well for identifying canonical subgenomic RNAs but showed variability in recognizing noncanonical ones, with LeTRS and sgDI-tector being strong alternatives for analyzing data from different sequencing platforms.
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