Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Personalized risk prediction of financial toxicity in patients with cancer: an interpretable machine learning study.

JNCI cancer spectrum·2026

Same author

Day-30 IL1RL1, CXCL9 and REG3α are prognostic for survival after mismatched unrelated donor transplantation.

British journal of haematology·2026

Same author

Phase II Trial of Opaganib Addition in Metastatic Castration-Resistant Prostate Cancer After Disease Progression on Abiraterone or Enzalutamide.

Cancer medicine·2026

Same author

Secondary Eyelid Weight Surgery in Patients with Facial Paralysis.

ORL; journal for oto-rhino-laryngology and its related specialties·2026

Same author

Nanobodies against Clostridioides difficile CDTb provide a toolkit for potent toxin neutralization and highly sensitive quantitation.

The Journal of biological chemistry·2025

Same author

CLIP2: a novel functional player in AQP5 trafficking dynamics and implications for Sjögren's disease.

Cell communication and signaling : CCS·2025

Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026

Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026

Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 19, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

iQuantitator: a tool for protein expression inference using iTRAQ.

John H Schwacke¹, Elizabeth G Hill, Edward L Krug

¹Department of Biochemistry, Medical University of South Carolina, Charleston, South Carolina, USA. schwacke@musc.edu

BMC Bioinformatics

|October 20, 2009

Summary

This summary is machine-generated.

We developed a novel modeling approach and statistical methods to analyze multiple Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) experiments, improving protein expression analysis and interpretation.

More Related Videos

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

A Protocol for the Identification of Protein-protein Interactions Based on ¹⁵N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Published on: September 24, 2012

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

Published on: February 17, 2023

Related Experiment Videos

Last Updated: Jun 19, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

A Protocol for the Identification of Protein-protein Interactions Based on ¹⁵N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Published on: September 24, 2012

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

Published on: February 17, 2023

Area of Science:

Proteomics
Bioinformatics
Quantitative Biology

Background:

Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) is increasingly used for differential protein expression analysis.
Analyzing multiple iTRAQ experiments presents significant computational challenges.

Purpose of the Study:

To develop a unified modeling approach and statistical methods for analyzing multiple iTRAQ experiments.
To overcome computational challenges in parameter inference for unbalanced iTRAQ data.

Main Methods:

Developed a process-based modeling approach using hierarchical regression.
Employed batching of regression coefficients and Markov Chain Monte Carlo (MCMC) methods for parameter inference.
Implemented a software tool, iQuantitator, for analysis.

Main Results:

The modeling approach provides a unified analysis of data from multiple iTRAQ experiments.
The developed inference approach overcomes computational challenges in parameter inference.
Simulation and experimental results demonstrate the method's efficacy.

Conclusions:

iQuantitator's approach overcomes limitations in current methods for iTRAQ data analysis.
The tool supports diverse experimental designs and aids result interpretation.
Hypertext-linked documents facilitate exploration and understanding of findings.