Evidence map›Paper›PMID 41898763›Full record

ReviewInternational journal of molecular sciences2026

Understanding the qPCR Standard Curve: From Assay Validation to Absolute Quantification and Variance PCR.

Mikael Kubista, Amin Forootan, Michael W Pfaffl, Stephen A Bustin, Jose M Andrade, Robert Sjöback, Björn Sjögreen, Anders Ståhlberg

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mikael KubistaInstitute of Biotechnology, Czech Academy of Science, 252 42 Vestec, Czech Republic.
Amin ForootanMultiD Analyses AB, GoCo Health Innovation City, 431 53 Mölndal, Sweden.
Michael W PfafflAnimal Physiology and Immunology, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany.ORCID 0000-0002-3192-1019
Stephen A BustinMedical Technology Research Centre, Faculty of Health, Education, Medicine and Social Care, Anglia Ruskin University, Chelmsford CM1 1SQ, UK.ORCID 0000-0003-1870-6098
Jose M AndradeGroup of Applied Analytical Chemistry, University of A Coruña, Campus da Zapateira, 15008 A Coruña, Spain.
Robert SjöbackPrecision BioAnalytics AB, GoCo Health Innovation City, 431 53 Mölndal, Sweden.ORCID 0009-0000-2906-3089
Björn SjögreenMultiD Analyses AB, GoCo Health Innovation City, 431 53 Mölndal, Sweden.
Anders StåhlbergSahlgrenska Center for Cancer Research, Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 413 90 Gothenburg, Sweden.ORCID 0000-0003-4243-0191

Funding

Region Västra Götaland; Swedish Cancer Society 25-4425RVO and MULTIOMICS_CZ (Programme Johannes Amos Comenius, Ministry of Education, Youth and Sports of the Czech Republic, ID Project CZ.02.01.01/00/23_020/0008540) - Co-funded by the European Union, 86652036Swedish Childhood Cancer Foundation MT2024-0004 and PR2025-006Swedish governmental funding of clinical research (ALF) 1006009Swedish Research Council 2021-01008Swedish Tillväxtverket 20370391, 20371194the Sjöberg Foundation 2025-1219the Swedish Agency for Economic and Regional Growth 20370391the Swedish Foundation for Strategic Research SM23-0033Vinnova 2024-03260
6 · The paper itself

Abstract

The quantitative polymerase chain reaction (PCR) standard curve is the central analytical tool for validating qPCR assays and can also be used to estimate target concentrations in test samples. This review explains how qPCR standard curves are constructed, validated, and analyzed for different purposes. We first examine an idealized standard curve generated using an exceptionally high number of replicates, far exceeding typical routine use. This approach clearly illustrates fundamental qPCR characteristics and provides an educational framework for defining and estimating PCR efficiency, limit of detection, and limit of quantification. Furthermore, we demonstrate that, in theory, variation in threshold crossing points across replicates can be used to estimate the number of target molecules in a sample. This method, which we term variance PCR, could complement digital PCR and potentially extend the dynamic range of absolute quantification. We also analyze a representative standard curve as typically processed in routine qPCR workflows. This includes validating its dynamic range, assessing the impact of outliers, estimating PCR efficiency and precision, and finally applying the curve to determine the concentration of test samples.

Indexed as

Real-Time Polymerase Chain ReactionHumansLimit of DetectionReference StandardsReproducibility of ResultsMIQEmolecular diagnosticsPCRqPCRreal-time PCRRT-PCRstandard curvevariation PCRvarPCR

Identifiers

PMID41898763
PMCPMC13026911

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.