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Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
Published on: July 2, 2016
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Transcriptome dataset of six human pathogen RNA viruses generated by nanopore sequencing
István Prazsák1, Zsolt Csabai1, Gábor Torma1
1Department of Medical Biology, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
Data in Brief
|July 5, 2022
Summary
Long-read sequencing provides new insights into viral transcriptomes, aiding in the study of emerging zoonotic RNA viruses and host responses. This research details nanopore sequencing data for six key human pathogen RNA viruses.
Area of Science:
- Virology
- Genomics
- Bioinformatics
Background:
- Emerging RNA viruses pose significant public health threats, as exemplified by influenza and SARS-CoV-2.
- Long-read sequencing (LRS) technologies offer advanced capabilities for analyzing complex transcriptomes.
- Understanding viral RNA complexity is crucial for developing effective countermeasures.
Purpose of the Study:
- To generate and analyze transcriptomic data for six significant human pathogen RNA viruses using nanopore sequencing.
- To investigate the host cell response to viral infection at the transcriptomic level.
- To provide a valuable resource for viral genomics, gene annotation, and virus-host interaction studies.
Main Methods:
- Nanopore sequencing was employed to generate transcriptomic data for Influenza A virus (H1N1), Zika virus, West Nile virus, Crimean-Congo hemorrhagic fever virus, Coxsackievirus B5, and Vesicular stomatitis Indiana virus.
- Raw sequencing reads underwent quality filtering (Q-score ≥ 7) during basecalling.
- High-quality reads were mapped to viral and host genomes, with read length distribution and data statistics analyzed using the ReadStat.4 Python script.
Main Results:
- High-quality nanopore sequencing data were generated for six RNA viruses and their host responses.
- The data enable detailed profiling of viral transcriptomic landscapes.
- Analysis facilitates novel gene annotations and insights into virus-host interactions.
Conclusions:
- The generated datasets are a valuable resource for comparative transcriptomic analysis of RNA viruses.
- This study supports the investigation of RNA base modifications and the comparison of various sequencing and bioinformatics approaches.
- The findings contribute to a deeper understanding of viral RNA biology and host-pathogen dynamics.
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