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Real Time RT-PCR

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Updated: Jun 9, 2025

Two-Step Reverse Transcription Droplet Digital PCR Protocols for SARS-CoV-2 Detection and Quantification

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Published on: March 31, 2021

Using intrahost single nucleotide variant data to predict SARS-CoV-2 detection cycle threshold values.

Lea Duesterwald^1,2, Marcus Nguyen^2,3,4, Paul Christensen^5,6

¹College of Engineering, Cornell University, Ithaca, NY, United States of America.

|October 30, 2024

Summary

This summary is machine-generated.

Predicting COVID-19 viral load is now possible using genomic data. This study demonstrates that sequence data can forecast PCR cycle threshold (Ct) values, aiding pandemic control efforts.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

Genomics
Virology
Computational Biology

Background:

COVID-19 pandemic driven by increasingly transmissible variants.
Lack of predictive tools for clinically significant viral features.
PCR cycle threshold (Ct) values correlate with viral load and disease spread.

Purpose of the Study:

To develop predictive models for viral load using genomic data.
To assess the feasibility of predicting PCR Ct values from SARS-CoV-2 sequence data.
To explore the utility of intrahost single nucleotide variant (iSNV) data for epidemiological insights.

Main Methods:

Utilized a dataset of 36,335 high-quality SARS-CoV-2 genomes.
Developed XGBoost models based on nucleotide (A, T, G, C) and indel frequencies.
Models were trained on iSNV data relative to the Wuhan-Hu-1 reference genome.

Main Results:

Achieved a predictive R-squared value of 0.604 and RMSE of 5.247.
Demonstrated modest predictive power for PCR Ct values.
Model performance was stable across external datasets and robust to patient factors and instrumentation.

Conclusions:

Genomic sequence data can be used to predict clinically relevant viral characteristics like Ct values.
Developed models show potential for disease prevention and control strategies.
Highlights the value of leveraging publicly available genomic data for public health.