ArticleBMC genomics2012
ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers, 1 of them a synthesis that pooled it.
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Who cites it
151 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Selection of reference genes for quantitative PCR: identifying reference genes for airway epithelial cells exposed toAmerican journal of physiology. Lung cellular and molecular physiology · 2020Pooled it
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- MicroRNA profiling in plasma samples using qPCR arrays: Recommendations for correct analysis and interpretation.PloS one · 2018Trial
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- Systemic STING agonist therapy drives expression of interferon stimulated genes and downstream production of cytokines in dogs with solid tumors.Journal for immunotherapy of cancer · 2025Article
- Identification of a Novel Salivary Four-miRNA Signature for Non-Invasive Diagnosis of Oral Squamous Cell Carcinoma.International journal of molecular sciences · 2025Article
- An infusible extracellular matrix biomaterial improves survival in a model of severe systemic inflammation.NPJ Regenerative medicine · 2025Article
- Nanobioreactor detection of space-associated hematopoietic stem and progenitor cell aging.Cell stem cell · 2025Article
- RQdeltaCT: an open-source R package for relative quantification of gene expression using delta Ct methods.Scientific reports · 2025Article
91 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMeasuring gene transcription using real-time reverse transcription polymerase chain reaction (RT-qPCR) technology is a mainstay of molecular biology. Technologies now exist to measure the abundance of many transcripts in parallel. The selection of the optimal reference gene for the normalisation of this data is a recurring problem, and several algorithms have been developed in order to solve it. So far nothing in R exists to unite these methods, together with other functions to read in and normalise the data using the chosen reference gene(s).
resultsWe have developed two R/Bioconductor packages, ReadqPCR and NormqPCR, intended for a user with some experience with high-throughput data analysis using R, who wishes to use R to analyse RT-qPCR data. We illustrate their potential use in a workflow analysing a generic RT-qPCR experiment, and apply this to a real dataset. Packages are available from http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.htmland http://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html
conclusionsThese packages increase the repetoire of RT-qPCR analysis tools available to the R user and allow them to (amongst other things) read their data into R, hold it in an ExpressionSet compatible R object, choose appropriate reference genes, normalise the data and look for differential expression between samples.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.