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Related Concept Videos

Nested-PCR to Detect a Specific Viral Genomic Sequence03:20

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This video describes nested polymerase chain reaction, a technique that consists of two sequential PCR amplification processes using two primer sets. The first set of primers is intended to anneal to sequences upstream of the second set, resulting in selective amplification of specific gene sequences. This PCR is more sensitive and specific than a normal PCR and is widely used as a detection technique for various...
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Related Experiment Video

Updated: Jan 20, 2026

Nested-PCR to Detect a Specific Viral Genomic Sequence
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Detecting viral sequences in NGS data.

Paul G Cantalupo1, James M Pipas1

  • 1Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA.

Current Opinion in Virology
|August 30, 2019
PubMed
Summary

Next-generation sequencing (NGS) aids virus detection and discovery in various samples. This review covers computational methods, challenges like contamination, and insights into viruses in cancer.

Area of Science:

  • Virology
  • Bioinformatics
  • Genomics

Background:

  • Next-generation sequencing (NGS) is crucial for identifying known and novel viruses in clinical and environmental samples.
  • Computational pipelines are essential for analyzing NGS data to detect and classify viral sequences.
  • Viruses play roles in various biological contexts, including human health and environmental ecosystems.

Purpose of the Study:

  • To review computational approaches for identifying viral sequences in NGS data.
  • To discuss strategies for mitigating false positives from contamination and artifacts.
  • To highlight the application of these methods in cancer research.

Main Methods:

  • Summary of diverse computational pipelines for viral sequence identification.

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Related Experiment Videos

Last Updated: Jan 20, 2026

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  • Discussion of techniques to address physical contamination and computational artifacts.
  • Review of case studies applying these methods to cancer datasets.

    • Main Results:

    • Established computational pipelines effectively identify and classify viral sequences.
    • Methods exist to minimize false virus identifications due to contamination or artifacts.
    • Analysis of cancer data reveals viruses as drivers and biomarkers for specific tumors.

    Conclusions:

    • NGS technologies coupled with robust computational pipelines are powerful tools for viral discovery.
    • Careful methodology is required to ensure accurate viral identification in complex samples.
    • Understanding virus-host interactions, particularly in cancer, is advancing through these sequencing approaches.