paper.bib

@article{Bustin:2009,
    author = {Bustin, Stephen A and Benes, Vladimir and Garson, Jeremy A and Hellemans, Jan and Huggett, Jim and Kubista, Mikael and Mueller, Reinhold and Nolan, Tania and Pfaffl, Michael W and Shipley, Gregory L and Vandesompele, Jo and Wittwer, Carl T},
    title = {The {MIQE} Guidelines: Minimum Information for Publication of Quantitative Real-Time {PCR} Experiments},
    journal = {Clinical Chemistry},
    volume = {55},
    number = {4},
    pages = {611-622},
    year = {2009},
    month = {04},
    abstract = "{Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results. }",
    issn = {0009-9147},
    doi = {10.1373/clinchem.2008.112797},
    url = {https://doi.org/10.1373/clinchem.2008.112797},
    eprint = {https://academic.oup.com/clinchem/article-pdf/55/4/611/32674422/bustin09-04-55-611.pdf},
}
@article{Ahmed:2018,
 title = {{pcr}: an {R} Package for Quality Assessment, Analysis and Testing of {qPCR} Data},
 author = {Ahmed, Mahmoud and Kim, Deok Ryong},
 year = 2018,
 month = mar,
 keywords = {qPCR, R package, Data analysis, Quality assessment},
 abstract = {
            
               Background
               Real-time quantitative PCR (qPCR) is a broadly used technique in the biomedical research. Currently, few different analysis models are used to determine the quality of data and to quantify the mRNA level across the experimental conditions.
            
            
               Methods
               We developed an R package to implement methods for quality assessment, analysis and testing qPCR data for statistical significance. Double Delta \textit{C}\textsubscript{\textit{T}} and standard curve models were implemented to quantify the relative expression of target genes from \textit{C}\textsubscript{\textit{T}} in standard qPCR control-group experiments. In addition, calculation of amplification efficiency and curves from serial dilution qPCR experiments are used to assess the quality of the data. Finally, two-group testing and linear models were used to test for significance of the difference in expression control groups and conditions of interest.
            
            
               Results
               Using two datasets from qPCR experiments, we applied different quality assessment, analysis and statistical testing in the pcr package and compared the results to the original published articles. The final relative expression values from the different models, as well as the intermediary outputs, were checked against the expected results in the original papers and were found to be accurate and reliable.
            
            
               Conclusion
               The pcr package provides an intuitive and unified interface for its main functions to allow biologist to perform all necessary steps of qPCR analysis and produce graphs in a uniform way.
            
         },
 volume = 6,
 pages = {e4473},
 journal = {PeerJ},
 issn = {2167-8359},
 url = {https://doi.org/10.7717/peerj.4473},
 doi = {10.7717/peerj.4473}
}
@Manual{Spiess:2018,
    title = {{qpcR}: Modelling and Analysis of Real-Time {PCR} Data},
    author = {Spiess, Andrej-Nikolai},
    year = {2018},
    note = {R package version 1.4-1},
    url = {https://CRAN.R-project.org/package=qpcR},
  }
  
@article{Perkins:2012,
   abstract = {Background: Measuring 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).Results: We 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. Conclusions: These 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. © 2012 Perkins et al.; licensee BioMed Central Ltd.},
   author = {James R. Perkins and John M. Dawes and Steve B. McMahon and David L.H. Bennett and Christine Orengo and Matthias Kohl},
   doi = {10.1186/1471-2164-13-296},
   issn = {14712164},
   issue = {1},
   journal = {BMC Genomics},
   keywords = {Animal Genetics and Genomics,Life Sciences,Microarrays,Microbial Genetics and Genomics,Plant Genetics and Genomics,Proteomics,general},
   month = {7},
   pages = {1-8},
   pmid = {22748112},
   publisher = {BioMed Central},
   title = {{ReadqPCR} and {NormqPCR}: {R} packages for the reading, quality checking and normalisation of {RT-qPCR} quantification cycle ({Cq}) data},
   volume = {13},
   url = {https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-13-296},
   year = {2012},
}
  
@article{Dvinge:2009,
    author = {Dvinge, Heidi and Bertone, Paul},
    title = {{HTqPCR}: high-throughput analysis and visualization of quantitative real-time {PCR} data in {R}},
    journal = {Bioinformatics},
    volume = {25},
    number = {24},
    pages = {3325-3326},
    year = {2009},
    month = {10},
    abstract = {Motivation: Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility. Results: We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates. Availability:HTqPCR and user documentation can be obtained through Bioconductor or at http://www.ebi.ac.uk/bertone/software. Contact:bertone@ebi.ac.uk},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btp578},
    url = {https://doi.org/10.1093/bioinformatics/btp578},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/25/24/3325/641798/btp578.pdf},
}