MIQE Checklist for qPCR Publications
A Practical Guide to Publishing Reliable Real-Time PCR Data
Quantitative PCR (qPCR) is one of the most common techniques in molecular biology. It's quick, sensitive and easy to run, which is why you find it in nearly every life sciences lab. The catch is that it's also one of the easiest techniques to do badly.
Most experienced researchers have seen it happen. A beautiful set of amplification curves produces inconsistent results when repeated in another lab or by another researcher. Reference genes turn out not to be stable. Primer efficiencies are never measured. Melt curves are missing or have not been analysed. Suddenly, what looked like convincing biology becomes difficult to interpret.
In 2009, Professor Stephen Bustin and a panel of international experts published the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, with the aim of improving the quality and reproducibility of qPCR work. MIQE is now the benchmark for reporting qPCR experiments, and a growing number of journals either require it or strongly encourage it.
The good news is that MIQE doesn't make your experiments more complicated. It's mostly about good lab practice, careful experimental design and writing down what you've already done. If you build MIQE into your workflow from the start, preparing the methods and figures for a paper becomes much easier.
Below are the key areas every researcher should think about before submitting a manuscript.
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1. Start With High-Quality RNA
No amount of optimisation downstream can rescue poor starting material. "Crap in, crap out," as they say.
RNA quality has a major influence on qPCR results. Degraded RNA, genomic DNA contamination, and inconsistent extraction methods all add noise that gets mistaken for biology.
- Process samples consistently
- Keep RNA cold at every stage
- Minimise freeze-thaw cycles
- Include DNase treatment where appropriate
- Measure RNA quantity, quality and purity
Assess RNA integrity whenever you can, using an electrophoresis system or Bioanalyzer. A surprising number of researchers assume a Nanodrop trace tells them something about RNA integrity. It doesn't.
If RNA quality varies between samples, any conclusions based on relative gene expression become much less reliable.
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2. Validate Your Primers
A lot of researchers order primers, confirm that they amplify a product, and go straight to expression analysis. Plenty also put blind faith in commercial "validated" primers without reading the small print. Most are only bioinformatically validated, which usually means they have been BLAST-checked against the host genome and nothing more.
MIQE expects considerably more.
- A single product on melt curve analysis
- A single band of the expected size on an agarose gel
- No amplification in no-template controls
- Minimal primer-dimer formation
- Amplification efficiencies close to 100%
Poor primer design remains one of the most common causes of unreliable qPCR data.
Whenever possible, design primers that span exon-exon junctions to minimise amplification of contaminating genomic DNA.
For primer selection and validation, see our qPCR and PCR primer design guide. Design candidate primers with the qPCR primer designer, check orientation with the DNA reverse & complement tool, and preview expected melt behaviour with the qPCR melt curve predictor.
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3. Measure PCR Efficiency
One of the most commonly overlooked parts of MIQE is amplification efficiency.
The popular ΔΔCt method assumes that target and reference genes amplify with very similar efficiencies.
Never assume this.
Generate a standard curve using serial dilutions and calculate efficiency from the slope.
- 90-110% efficiency is generally acceptable
- Correlation coefficients (R²) should be close to 1.0
- Large differences between primer sets should be investigated before analysing samples
Measuring efficiency only takes an afternoon and can prevent major problems later.
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4. Choose Reference Genes Carefully
Perhaps the biggest mistake in published qPCR studies is assuming that traditional housekeeping genes are automatically stable.
Genes such as GAPDH, ACTB and 18S rRNA can vary substantially depending on cell type, treatment or disease model.
- Test several candidate reference genes
- Evaluate stability using tools such as geNorm, NormFinder or BestKeeper
- Use more than one validated reference gene whenever possible
Reference gene validation often makes the difference between robust expression data and misleading conclusions.
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5. Include Appropriate Controls
Controls demonstrate that your assay is behaving as expected.
- No-template controls (NTCs), which test for contamination with template material
- No reverse transcriptase controls (No-RT), which test for DNA contamination of RNA samples
- Positive controls where appropriate, to confirm the reaction actually works
- Technical replicates, which capture technical variability
- Biological replicates, which capture biological variability
Negative controls should remain negative. If they don't, investigate contamination before analysing your experimental samples.
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6. Report Exactly What You Did
One of the central principles of MIQE is transparency.
If another laboratory cannot repeat your experiment using the information provided, the methods section isn't complete.
- RNA extraction kit
- Reverse transcription protocol
- Primer sequences
- Amplicon length
- PCR master mix
- Instrument used
- Cycling conditions
- Reaction volumes
- Template amount
- Data analysis method
These details only add a few extra lines to the methods section, but they make your work far more reproducible.
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7. Don't Rely Solely on Ct Values
Ct values themselves are not biological results.
Expression analysis should include appropriate normalisation and statistical analysis.
- how Ct values were processed
- which reference genes were used
- how fold changes were calculated
- what statistical tests were applied
Many reviewers now specifically ask for these details.
For Ct processing, normalisation, fold change, and statistics, see our qPCR data analysis guide.
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8. Show Primer Performance
If reviewers have to ask whether your primers worked properly, important information is missing.
- Melt curves
- Standard curves
- Amplification efficiencies
- Representative amplification plots
- Amplicon verification
These can often be included as supplementary information without taking up space in the main paper.
See our melt curve analysis guide and qPCR melt curve predictor for interpreting melt curves and checking primer specificity.
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9. Keep Good Records From Day One
Trying to recreate experimental details six months after finishing a project is rarely successful.
Instead, record everything as you go.
- Primer sequences
- Batch numbers
- RNA concentrations
- cDNA synthesis dates
- Standard curve data
- Efficiency calculations
- Raw Ct values
By the time you start writing the manuscript, most of the MIQE checklist will already be complete.
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10. Think About MIQE Before You Begin
The easiest way to satisfy MIQE is not to treat it as a publication checklist.
Instead, use it as an experimental planning guide.
- Have the primers been validated?
- Have suitable reference genes been selected?
- Will RNA quality be assessed?
- Are enough biological replicates included?
- Is every step being recorded?
Answering these questions early avoids many of the issues that reviewers commonly identify during peer review.
A Practical MIQE Checklist
Before submitting your manuscript, check that you have:
- Validated primer specificity
- Measured PCR efficiency
- Verified RNA quality
- Used validated reference genes
- Included appropriate controls
- Reported complete experimental methods
- Explained data normalisation
- Included sufficient biological replicates
- Applied appropriate statistics
- Made the experiment reproducible
If you can confidently tick every box, you're already well on the way to producing a publication that both reviewers and readers can trust.
Final Thoughts
MIQE has a reputation for being a long reporting checklist, but that's a misread. What it's really about is confidence in qPCR data. It pushes researchers to show that their assays are specific, efficient and reproducible, rather than asking readers to take the results at face value.
In our experience, following MIQE from the start actually saves time. Troubleshooting gets easier, writing the paper gets easier, and reviewers have less to pick at. As more journals adopt MIQE requirements, building these habits into your routine is shifting from optional to essential.
Whether you're running your first qPCR experiment or your fiftieth manuscript, treating MIQE as part of everyday lab practice will help you produce data that holds up to scrutiny. That's ultimately what good science is about.
Key References
- Bustin SA, Benes V, Garson JA, et al. (2009). The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments. Clinical Chemistry, 55(4):611-622.
- Bustin SA, Benes V, Nolan T, Pfaffl MW. (2005). Quantitative real-time RT-PCR, a perspective. Journal of Molecular Endocrinology, 34(3):597-601.
- Bustin SA, Huggett JF. (2017). qPCR primer design revisited. Biomolecular Detection and Quantification, 14:19-28.
- Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich J. (2019). The Ultimate qPCR Experiment: Producing Publication Quality, Reproducible Data the First Time. Trends in Biotechnology, 37(7):761-774.