The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A standard curve based method for relative real time PCR data processing
Open accessAbstract
Background: Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR. Results: We designed a procedure for data processing in relative real time PCR. The procedure completely avoids PCR efficiency assessment, minimizes operator involvement and provides a statistical assessment of intra-assay variation. The procedure includes the following steps. (I) Noise is filtered from raw fluorescence readings by smoothing, baseline subtraction and amplitude normalization. (II) The optimal threshold is selected automatically from regression parameters of the standard curve. (III) Crossing points (CPs) are derived directly from coordinates of points where the threshold line crosses fluorescence plots obtained after the noise filtering. (IV) The means and their variances are calculated for CPs in PCR replicas. (V) The final results are derived from the CPs' means. The CPs' variances are traced to results by the law of error propagation. A detailed description and analysis of this data processing is provided. The limitations associated with the use of parametric statistical methods and amplitude normalization are specifically analyzed and found fit to the routine laboratory practice. Different options are discussed for aggregation of data obtained from multiple reference genes. Conclusion: A standard curve based procedure for PCR data processing has been compiled and validated. It illustrates that standard curve design remains a reliable and simple alternative to the PCR-efficiency based calculations in relative real time PCR.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
Livak, K. J., & Schmittgen, T. D. (2001) · Methods · doi:10.1006/meth.2001.1262 · Cited by 180,860
Mutation of the mouse klotho gene leads to a syndrome resembling ageing
Kuro-o, M., Matsumura, Y., Aizawa, H., Kawaguchi, H., Suga, T., Utsugi, T., Ohyama, Y., Kurabayashi, M., Kaname, T., Kume, E., et al. (1997) · Nature · doi:10.1038/36285 · Cited by 3,837
Cited by
Novel Markers to Delineate Murine M1 and M2 Macrophages
Jablonski, K. A., Amici, S. A., Webb, L. M., Ruiz-Rosado, J. D. D., Popovich, P. G., Partida-Sanchez, S., & Guerau-de-Arellano, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0145342 · Cited by 978
Gutierrez, L., Mauriat, M., Guénin, S., Pelloux, J., Lefebvre, J., Louvet, R., Rusterucci, C., Moritz, T., Guerineau, F., Bellini, C., et al. (2008) · Plant Biotechnology Journal · doi:10.1111/j.1467-7652.2008.00346.x · Cited by 696
Svec, D., Tichopad, A., Novosadova, V., Pfaffl, M. W., & Kubista, M. (2015) · Biomolecular Detection and Quantification · doi:10.1016/j.bdq.2015.01.005 · Cited by 601
Control of the Inflammatory Macrophage Transcriptional Signature by miR-155
Jablonski, K. A., Gaudet, A. D., Amici, S. A., Popovich, P. G., & Guerau-de-Arellano, M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0159724 · Cited by 146
Adult zebrafish model of bacterial meningitis in Streptococcus agalactiae infection
Patterson, H., Saralahti, A., Parikka, M., Dramsi, S., Trieu-Cuot, P., Poyart, C., Rounioja, S., & Rämet, M. (2012) · Developmental & Comparative Immunology · doi:10.1016/j.dci.2012.07.007 · Cited by 83
Metadata
- Authors
- Larionov, A., Krause, A., & Miller, W.
- Venue
- BMC Bioinformatics
- Published
- 2005-03-21
- Volume
- 6
- Issue
- 1
- Pages
- 62-62
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W1506057870
- Cited by
- 946
- References
- 2
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A standard curve based method for relative real time PCR data processing. https://app.cochat.ai/papers/10.1186/1471-2105-6-62
- The paper
- Larionov, A., Krause, A., & Miller, W. (2005). A standard curve based method for relative real time PCR data processing. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-6-62
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.