Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 668

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization

Dheda, K., Huggett, J.F., Chang, J.S., Kim, L.U., Bustin, S.A., Johnson, M.A., Rook, G.A.W., Zumla, A.·2005·Analytical Biochemistry

Abstract

hasbecome a routine technique for gene expression analysis,allowing accurate high-throughput RNA quantiWcationover a wide dynamic range at a relatively low cost.Although the technique has become well establishedover the past few years, data normalization remainsproblematic and is subject to frequent criticism. Normal-ization is essential to control for experimental errorbetween samples that can be introduced at a number ofstages throughout the procedure. Several di Verent strate-gies can be applied to the normalization of the expres-sion of an RNA of interest (for a review, see [1]). Themost common strategy is to use a reference gene as aninternal standard that is assumed to remain constantbetween experimental groups. It is now clear, however,that in most experimental situations the use of conven-tional reference genes, such as glyceraldehyde-3-phos-phate dehydrogenase (GAPDH) and -actin, isinappropriate due to their variability [1,2]. If the refer-ence gene chosen has a large expression error, the noiseof the assay is increased and detection of small changesbecomes unfeasible. Far worse, if expression of the refer-ence gene is altered by the experimental condition or thedisease under study, the results obtained may be entirelyincorrect. Therefore, it is essential to validate potentialreference genes to establish whether they are appropriatefor a speciWc experimental purpose. We have shown pre-viously that human acidic ribosomal protein (HuPO)was the least variable reference gene investigated in ourmodel of pulmonary tuberculosis (PTB) [3].Despite the fact that many validation exercises havebeen undertaken conWrming the variability of convention-ally used reference genes [1], it has been argued that over-all study conclusions remain unchanged because thisvariability is often similar in the control and interventiongroups [4]. Furthermore, reports that address the net e Vectof reference gene variability on experimental conclusionsare sparse and do not provide the Wnal proof that refer-ence gene variability may have a variable inXuence onstudy conclusions. To address the impact of a validationexercise on intergroup comparisons, we normalized diVer-ent sets of data with diVering reference genes, includingconventionally chosen genes. This report demonstratesthat experimental results are highly dependent on the ref-erence gene chosen. Moreover, whether statistically sig-niWcant diVerences between study groups were present orabsent depended on which reference gene was used.In the study, 21 patients with proven PTB and 21matched healthy volunteers were recruited. E Vector T-cellIFN- ELISPOT responses to TB-speciWc antigens wereused to conWrm that control subjects were not latentlyinfected with

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ab.2005.05.022

Cites

8 of 8 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 8 references

Cited by

See all 668

Metadata

Authors
Dheda, K., Huggett, J. F., Chang, J. S., Kim, L. U., Bustin, S. A., Johnson, M. A., Rook, G. A. W., & Zumla, A.
Venue
Analytical Biochemistry
Publisher
Elsevier BV
Published
2005-09
Volume
344
Issue
1
Pages
141-143
ISSN
0003-2697 (ISSN-L)
OpenAlex
W2057464020
Cited by
668
References
8
FWCI
8.07
Type
journal article
Language
en
Open access
closed

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: The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. https://app.cochat.ai/papers/10.1016/j.ab.2005.05.022
The paper
Dheda, K., Huggett, J. F., Chang, J. S., Kim, L. U., Bustin, S. A., Johnson, M. A., Rook, G. A. W., & Zumla, A. (2005). The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Analytical Biochemistry. https://doi.org/10.1016/j.ab.2005.05.022

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.