Dynamic transcriptome profiling dataset of vaccinia virus obtained from long-read sequencing techniques.

Béla Dénes,Zsolt Boldogkői,István Prazsák,Michael Snyder,Attila Szűcs,Dóra Tombácz

doi:10.1093/gigascience/giy139

Béla Dénes, Zsolt Boldogkői + Show 4 more

Open Access

https://doi.org/10.1093/gigascience/giy139

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Journal: GigaScience	Publication Date: Nov 23, 2018
Citations: 29	License type: CC BY 4.0

Affiliation: University of Szeged, Stanford University

Abstract

BackgroundPoxviruses are large DNA viruses that infect humans and animals. Vaccinia virus (VACV) has been applied as a live vaccine for immunization against smallpox, which was eradicated by 1980 as a result of worldwide vaccination. VACV is the prototype of poxviruses in the investigation of the molecular pathogenesis of the virus. Short-read sequencing methods have revolutionized transcriptomics; however, they are not efficient in distinguishing between the RNA isoforms and transcript overlaps. Long-read sequencing (LRS) is much better suited to solve these problems and also allow direct RNA sequencing. Despite the scientific relevance of VACV, no LRS data have been generated for the viral transcriptome to date.FindingsFor the deep characterization of the VACV RNA profile, various LRS platforms and library preparation approaches were applied. The raw reads were mapped to the VACV reference genome and also to the host (Chlorocebus sabaeus) genome. In this study, we applied the Pacific Biosciences RSII and Sequel platforms, which altogether resulted in 937,531 mapped reads of inserts (1.42 Gb), while we obtained 2,160,348 aligned reads (1.75 Gb) from the different library preparation methods using the MinION device from Oxford Nanopore Technologies.ConclusionsBy applying cutting-edge technologies, we were able to generate a large dataset that can serve as a valuable resource for the investigation of the dynamic VACV transcriptome, the virus-host interactions, and RNA base modifications. These data can provide useful information for novel gene annotations in the VACV genome. Our dataset can also be used to analyze the currently available LRS platforms, library preparation methods, and bioinformatics pipelines.

Highlights

In the manuscript "Dynamic Transcriptome Profiling Dataset of Vaccinia Virus Obtained from Long-read Sequencing Techniques" by Tombácz et al, the authors describe a dataset produced by Pacbio and Nanopore sequencing of VACV, with multiple approaches taken in both sample collection and sequencing library preparation in order to profile several features of the transcriptome
The dataset is of obvious importance to the field and contains many interesting features, but there are some concerns which would need to be addressed before this manuscript is ready for publication
We recommend pulling out error rates, and representing instead as violin plots, or at least box plots, and include a column for "total reads" and either the count of mapped VACV reads, or percent reads mapped to VACV

Summary

Introduction

. In the manuscript "Dynamic Transcriptome Profiling Dataset of Vaccinia Virus Obtained from Long-read Sequencing Techniques" by Tombácz et al, the authors describe a dataset produced by Pacbio and Nanopore sequencing of VACV, with multiple approaches taken in both sample collection and sequencing library preparation in order to profile several features of the transcriptome. It would be beneficial to include a brief statement defining "dynamic" and "static" sample collection strategies in your data description to clarify how you are using these terms. Why aren't the RSII and direct RNA runs included in Table 7 and Figure 6 host mappings?

Results

Conclusion