Rna Virus Sequencing
Rna Virus Sequencing - Related Videos
09:36
Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
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Cited by 50 •
2016
This protocol describes a rapid and broadly applicable method for unbiased RNA-sequencing of viral samples from human clinical isolates.
18:10
Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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Cited by 40 •
2011
The present article describes the steps required to isolate and characterize RNA polymerase fidelity variants of RNA viruses and how to use mutation frequency data to confirm fidelity changes in tissue culture.
03:36
Recovery of Zika Virus from In Vitro-Transcribed RNA
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2026
Source: Weger-Lucarelli, J., et al. Rescue and Characterization of Recombinant Virus from a New World Zika Virus Infectious Clone. J. Vis. Exp. (2017)This video demonstrates the recovery of infectious Zika virus using electroporation of in vitro transcribed RNA. The process shows how synthetic RNA initiates infection and leads to viral replication in cultured cells.
07:30
Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
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Cited by 19 •
2020
This method describes the steps to improve the quality and quantity of sequence data that can be obtained from formalin-fixed paraffin-embedded (FFPE) RNA samples. We describe the methodology to more accurately assess the quality of FFPE-RNA samples, prepare sequencing libraries, and analyze the data from FFPE-RNA samples.
01:29
Viruses with RNA Genomes
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2025
RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
FAQs
RNA virus sequencing relies on platforms that generally read DNA, so viral RNA is converted into complementary DNA, or cDNA. This creates a DNA-based template that can then be amplified or incorporated into a sequencing library. Without this conversion, the RNA genome would not enter the stated DNA-reading workflow.
Reference-based analysis aligns generated sequencing reads with an existing viral genome, allowing researchers to compare the sample with a known sequence. De novo assembly instead reconstructs sequence information without relying on a reference genome. Using either approach helps produce an interpretable viral sequence while supporting identification of related strains and genetic changes.
Comparing viral sequences reveals mutations and helps distinguish related strains. When sequences from multiple samples are examined, their similarities and differences can also reveal transmission patterns. In medicine, these findings support surveillance of emerging variants, evaluation of diagnostic targets, and broader interpretation of how viral populations change during outbreaks.
The workflow begins with viral RNA, followed by reverse transcription to produce complementary DNA. That material is then amplified or prepared as a sequencing library before sequencing generates reads. Finally, the reads are aligned to a reference genome or assembled de novo, producing sequence information that can be examined for species identity, mutations, and strain relationships.
During an outbreak, sequencing can identify the viral species involved, distinguish related strains, and reveal transmission patterns across samples. These results add genetic evidence to outbreak surveillance and help monitor emerging variants. The information can therefore support public health responses while also guiding research into the changing viral population.
Sequence data can help assess diagnostic targets by showing which viral regions are present and how they vary. The same information supports research on treatment, vaccine development, and emerging variants. Because sequencing also distinguishes viral strains, it connects laboratory genetic findings with medical surveillance and investigations of disease spread.