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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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Related Experiment Video

Updated: May 23, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Secure and scalable gene expression quantification with pQuant.

Seungwan Hong^1,2, Conor R Walker^1,2, Yoolim A Choi^1,2

¹Department of Biomedical Informatics, Columbia University, New York, NY, 10032, USA.

Nature Communications

|March 11, 2025

Summary

This summary is machine-generated.

pQuant is a new algorithm for privacy-preserving gene expression quantification using homomorphic encryption. It ensures sensitive RNA-seq data remains secure during computation on cloud servers.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Next-generation sequencing (NGS) and RNA-sequencing (RNA-seq) generate vast amounts of data.
Processing RNA-seq data can expose private genetic information.
Existing computational methods lack robust privacy guarantees for sensitive genomic data.

Purpose of the Study:

To introduce pQuant, a novel algorithm for privacy-preserving gene expression quantification.
To enable secure analysis of RNA-seq data on public and cloud computing environments.
To ensure sensitive individual genotypes are not exposed during data analysis.

Main Methods:

Development of pQuant, an algorithm utilizing homomorphic encryption.
Implementation of computations directly on encrypted RNA-seq data.
Evaluation of pQuant's accuracy and scalability against established non-secure algorithms.

Main Results:

pQuant achieves accuracy comparable to state-of-the-art tools like STAR and kallisto.
The algorithm performs computations on encrypted data, maintaining data privacy.
pQuant demonstrates high scalability with runtime and memory independent of read count.
Supports parallel processing for enhanced efficiency across varying gene numbers.

Conclusions:

pQuant offers a secure and accurate solution for gene expression quantification from RNA-seq data.
The algorithm effectively protects private genotypes during computational analysis.
pQuant is a scalable and efficient tool suitable for cloud-based genomic data processing.