2026.4·It's all relative

All releases · April 3, 2026 · Jan Hellemans

This is the release where Clarida extends the qPCR workflow to analyses. You could already define, design, and execute your experiment. Now you can analyze it too. Import your Cq data, flag problematic measurements, calculate amplification efficiency from standard curves, and walk all the way to normalized relative quantities without ever leaving the platform.

What I started 20 years ago with a simple Excel template for qPCR data analysis has now arrived in a much more capable package. Still built on the qBase quantification framework published in Genome Biology back in 2007, but now enhanced with upfront design functionality and an intuitive interface.

Happy analysis.
Jan

New features

Full qPCR analysis pipeline

Before this release, Clarida stopped at execution. With 2026.4, a new Analyze section guides you from import to inspection of analysis results. Smart defaults and auto-processing make for swift and intuitive analyses while manual mode and custom settings allow researchers to take full control.

Analyze sections showing the full pipeline: Import data, Annotation review, Cq processing with Measurement exclusion, Replicates, Amplification efficiency, and Normalization subsections, followed by Data analysis

Import Cq values from CSV, TXT, or Excel files. Save time by importing multiple files or a single file with several data sheets in one go. An annotation review catches mismatches between the imported data and your experiment design before you proceed.

Import data section with drag-and-drop file upload, a table linking three Excel files to runs with well counts and format details, and Annotation review showing match/mismatch counts per run

Measurement-level quality control lets you flag and exclude outliers. Flags from the execution stage and Cq value screening are shown to quickly focus on the measurements that require your attention. Beyond full manual control, auto-exclusion of outliers or bad replicates is there to quickly proceed with good data only.

Measurement exclusion table filtered to flagged measurements, showing Cq values with status icons and exclusion reasons like replicate dCq threshold exceeded, late Cq value, and undetermined results

Next up are a global review of replicate quality and an inspection of dilution curves for determination of amplification efficiency. The last step of data processing is the inspection of data normalization. We support multi-gene normalization and provide a geNorm based quality control on the stability of your reference genes to allow spotting and exclusion of bad normalizers.

Normalization section showing a reference gene stability table with M and CV values, include/exclude toggles, and a bar chart of normalization factors per sample with error bars

The pipeline culminates in efficiency-corrected, reference-gene-normalized relative quantities (NRQ) following the qBase framework. Change an upstream setting, an exclusion, an efficiency value or a reference gene selection and everything downstream recalculates within seconds. All results persist across sessions.

Multi-run experiments

Our March release brought auto-design to speed up your preparations for small experiments. In April we are adding multi-run support. By default we avoid spreading mixes across multiple-runs but, as always, you can change this and tweak the layout however you want.

Run Layout in Auto mode showing three runs with mix assignments, a 96-well plate visualization with color-coded assay blocks, and a well annotation table listing well positions, mixes, samples, and assays

Bringing larger experiments to the lab is yet another challenge. Clarida provides you with a list of materials needed and instructions for preparing mixes and filling plates. Some experiments can be handled in one go, others may require multiple sessions. We've got you covered with multi-session execution. Select the runs you want to prepare. We tell you about the material needed for this session only and recalculate mixes to only cover what is needed for the selected plates.

Execute section in multi-session mode showing a session table with two assigned runs, status badges, and the execution workflow steps: Collect material, Prepare mixes, Fill plate, and Start run

Run progress, mix state, and completion status all persist and sync automatically. Ideal to keep track of your more challenging experiments.

Multiplex assay support

You can now define multiplex assays directly in Assay Prep, with part-level editing for individual targets within a multiplex group. Downstream sections like efficiency calculation, normalization and quantification all respect the multiplex structure but simplify to just the assay level (hiding part details) for singleplex assays.

Assay prep showing expandable multiplex assays: SARS CoV-2 with three targets (N1, N2, IC) and Influenza with four targets (Influenza A, A (H5N1), B, RNase P), each with individual dye and type assignments

Quality of life

Experiment versioning · We create experiment snapshots whenever you finish a workflow stage or import run data so you can always restore to such a save point.
Display names · We now differentiate between the original sample or assay name and the display name you may want to use as a shorthand or alias within the context of a given experiment. Exports and UI consistently use these display names while preserving traceability to the original library or import names.
Reworked ELN export · The Excel export now produces one Execute sheet per session, with session-filtered sample lists, assay lists, per-run pipetting tables, and 96-well plate grid visualizations. If your experiment has multiple runs, each run gets its own clearly labeled sections.

Changes that may affect you

No changes to analysis algorithms or result calculations in this release. ✓