qPCR analysis,
without the spreadsheets.
Upload the RDML file your instrument exports. Efficiency-corrected fold changes, statistics and a publication figure come back together. Swap a reference gene, drop a bad well or add a plate, then run it again. No formulas to rebuild.
Free plan · No credit card · Data hosted in the EU
Or search 1094 curated primer pairs, free and without an account →
Method peer-reviewed in Bio-Protocol (Müller & Tiret, 2026). Read the paper →
Co-authored by Ampliova’s founder, and published open access so you can check every step.
What you get
From raw Cq to publication figure in a few minutes.
Time, not technique
The last spreadsheet you build for a qPCR figure.
Manual analysis means exporting Cq values by hand and rebuilding efficiency formulas from scratch. Then you hope the error doesn't surface at review. One upload replaces the whole chain.
No R and no Excel. Not even Python.
Upload a file and configure your batch, then download your results. It all happens right in the browser.
Expression and ChIP.
Efficiency-corrected (LinRegPCR) or classic ΔΔCq, plus ChIP-qPCR as percent of input. Reference-gene stability ranking included.
Every result traceable.
Full audit trail from raw Cq to fold change, and a Methods paragraph drafted from your batch settings.
Try it on your own plate. The first two batches are free.
Start free →Someone has already validated your primer pair.
Every pair is read out of a published paper, mapped exon by exon against the Ensembl transcript, screened for off-target hits, and reviewed by hand before it appears. No submissions go straight in.
Free to browse, no account needed. Open the database → Published a pair that works? Send us the DOI →
Your instrument and LinRegPCR already do their half. We replace the spreadsheet in between.
Getting an efficiency out of a curve takes one free program. Turning twelve of them into a fold change you would put in a paper is where the afternoon goes.
Already on your bench
Your instrument's software
Runs the plate, calls Cq, writes an RDML file. Nothing here asks you to change instrument, or to export anything twice.
Gives you Cq values
Free and peer-reviewed
LinRegPCR
The per-well efficiency method of Ruijter et al. (2009), in the implementation of Untergasser et al. (2021). Ampliova runs it and cites it for you.
Gives you an efficiency per well
Everything after that
Ampliova
Ranking the reference genes, screening the outlier wells, linking plates, the statistics, the figure, and the trail from each bar back to its wells, in one batch.
Gives you a result you can defend
- 01ImportRDML in, raw curves kept, layout pre-filled from the file
- 02EfficiencyLinRegPCR per well, Grubbs on each gene's efficiencies
- 03NormalizeFour stability measures, geometric mean of the pair you keep
- 04FigureFold changes, a test fixed in advance, exports that trace back
Rather do it by hand? The spreadsheet workflow is published open access and it works. It cannot screen an outlier well, rank a reference gene, or tie two plates together.
How the calculation works →Your next batch can skip the spreadsheet.
Start free →Keep your workflow. Prism included.
If your lab draws its figures in Prism, keep doing it. The .pzfx export opens with one data table per gene, ready for your usual graphs.
- Prism (.pzfx)Opens in GraphPad Prism 9 and later, one data table per gene.All plans
- Excel (.xlsx)A summary sheet, the efficiencies, then one sheet per gene.All plans
- PDF reportTables, charts and the MIQE 2.0 checklist. Free-plan reports carry an Ampliova footer.All plans
- CSVOne flat file with every intermediate value.All plans
- JSONThe full audit trail, manual exclusions included.All plans
- Lab notebook (.zip)The PDF, Excel and JSON files in one archive for Labguru, Benchling, eLabFTW or RSpace.All plans
- FiguresSVG, PNG or TIFF at 150 to 1200 DPI, and vector PDF.Researcher and Lab
ChIP-qPCR batches export CSV and JSON for now.
If your machine exports RDML, you are ready.
RDML is the open exchange format for qPCR, and it carries the raw amplification curves. A plain Cq table leaves them behind, and efficiency correction has nothing to fit without them.
Applied Biosystems QuantStudioDesign & Analysis Software
File → Export → RDML
Keep "Include amplification data" checked. Without the raw curves, efficiencies cannot be computed.
Bio-Rad CFXCFX Maestro
File → Export → RDML
Tick "Include raw fluorescence data". CFX only writes wells that have a target assigned, so name anything you want analysed.
Roche LightCycler
Native RDML export
Exports RDML natively.
QIAGEN Rotor-Gene Q
Native RDML export
Exports RDML natively.
QIAGEN RT² Profiler arraysPathway-focused panels
Dedicated workflow
A dedicated workflow handles the array's own reference genes and controls. Read the RT² guide →
Not sure about your instrument? The full export instructions are in the documentation, or write to contact@ampliova.com and we will check a file for you.
One price a year, however many experiments you run.
Both paid plans cover as many runs and plates as you put through them, and Researcher is also sold by the month. Your first two batches are free.
Free
€0
- 2 batches, up to 2 plates each, counted once analyzed
- PDF, Excel, CSV, JSON and Prism exports
- No card needed
Researcher
€59/year
€99/year from year 3
Or €19/month, cancel anytime
- Unlimited experiments
- Figures in SVG, PNG, TIFF and vector PDF
- Methods paragraph drafted from your batch settings
★ Founders price. 40% off the first two years of an annual plan started by June 30, 2027, then the standard price.
Lab
€229/year
€389/year from year 3
5 people included
Up to 15 people: €29/year per extra person
- Everything in Researcher, for each member
- Batches shared across the lab
- One subscription and one invoice
★ Founders price. 40% off the first two years of an annual plan started by June 30, 2027, then the standard price.
Before you trust it with a paper.
Including the ones that argue against paying us.
Can I cite Ampliova in a paper?
Yes, and you should. Name Ampliova as the software used, cite Müller and Tiret (2026, Bio-Protocol) as the methodological reference, and cite the underlying methods you actually used: LinRegPCR (Ruijter et al. 2009, in the implementation of Untergasser et al. 2021), geometric-mean normalization (Vandesompele et al. 2002), and whichever stability measures you report.
The drafted Methods paragraph already carries the Ampliova and primary-reference citations.
What happens when the free plan runs out?
Your two free batches stay yours, and you can keep opening, editing, re-running and exporting them. A batch counts once it has been analyzed, so deleting it later doesn't give its place back.
Researcher costs €59/year with no limit on experiments, or €19/month. A lab of up to five people pays €229/year.
Need a quote for your purchasing office? Request one from your account, with the purchase order number and Chorus Pro code if your lab has them, and we'll send it to you.
What do I pay after the first two years?
Your subscription renews at the standard price: €99/year for Researcher, €389/year for Lab. The founders price covers the first two years of any annual plan started by June 30, 2027, counted from the day you subscribe. Monthly Researcher subscriptions don't get it.
LinRegPCR is free. Why pay for this?
If all you need is a per-well efficiency, do not. LinRegPCR is excellent, and Ampliova runs it rather than replacing it.
What you pay for is everything after it: ranking your reference genes against four stability measures instead of trusting GAPDH, screening outlier wells, pooling plates into one batch, running a test fixed in advance and reporting each effect with its confidence interval, and keeping a trail from each bar back to the wells behind it.
Does using Ampliova make my experiment MIQE-compliant?
No software can do that on its own, because compliance depends on your whole experiment, not on the analysis step.
What Ampliova does is follow MIQE 2.0 for the part it controls: mean efficiency per gene from LinRegPCR, normalization to at least two reference genes whose stability is measured, documented outlier exclusion, and an export that reports what the guideline asks you to report.
Efficiency-corrected or classic ΔΔCq?
Both are available, and classic ΔΔCq is a legitimate choice when you know your efficiencies are close to 2.
The cost of assuming it is not fixed: it compounds with the ΔCq you are measuring. For a gene that really amplifies at E = 1.95, assuming E = 2.0 overstates the ratio by about 29% over a ΔCq of 10 cycles, and about 88% over 25.
Which instruments work?
Any instrument that exports RDML with the raw amplification data, which covers Bio-Rad CFX, Applied Biosystems QuantStudio, Roche LightCycler and QIAGEN Rotor-Gene Q. A plain Cq table is not enough: efficiency correction needs the curves.
QIAGEN RT² Profiler arrays have their own workflow, with the array's reference genes and controls handled for you.
Can I use it for ChIP-qPCR?
Yes. A ChIP batch reports percent of input, with each IP normalized against the input of its own amplicon and chromatin sample, and the dilution factor applied once.
Fold enrichment over a mock or IgG control is deliberately not offered: Haring et al. (2007) showed that identical ChIP signals can differ tenfold in that measure depending on background alone.
Where is my data, and can I get it back?
Results and amplification curves are stored in Supabase's EU West region in Ireland, and the analysis engine runs in the EU as well, in the Netherlands. Plate-layout suggestions send sample and gene names only to Mistral AI in Paris, and can be turned off in Settings, by each user or for a whole lab.
You can export your results and delete your account and all its data at any time from Settings. Deletion completes within 30 days.
See what your own plate looks like.
Free plan: 2 batches, no card needed · Researcher €59/year, unlimited experiments · Lab €229/year for up to 5 people · ★ Founders price. Start an annual plan by June 30, 2027 and your first two years cost 40% less. After that, your plan renews at the standard price.
Hosted in the EU (Ireland) · Encrypted in transit & at rest (AES-256) · Never used to train AI models · Delete everything, anytime · Export your data, no lock-in · Full privacy policy