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EXPRESSION & PROFILING

Is my candidate gene upregulated in treated vs. control samples?

From hypothesis to publication-ready fold-change data in a single workflow.

Run your first expression experiment

Your workflow in 5 steps

Define → Design → Execute → Analyze → Report

1

Define

Define your hypothesis and select target + reference genes

Start with the research question. Select your target genes and validated reference genes for normalization.

2

Design

Design a 96-well plate with biological and technical replicates

Drag-and-drop plate layout with built-in sample management. Controls and replicates designed once, reusable across projects.

3

Execute

Run your plate and import raw Cq data

Export cycler-ready protocols. Import results from any major qPCR instrument with metadata intact.

4

Analyze

Normalize to validated reference genes, calculate fold changes

Automated MIQE-compliant normalization. Reference gene validation, ΔΔCq calculation, and statistical analysis in one step.

5

Report

Export publication-ready figures and statistics

Generate figures and MIQE-compliant reports. Every result traces back to its raw data and analysis parameters.

What you're doing today

  • Exporting Cq values to Excel, building your own normalization templates
  • Copy-pasting between GraphPad for figures
  • Struggling with reviewer questions about normalization strategy
  • Losing reproducibility when lab members leave with their Excel templates

3+ tools juggled per expression experiment

45 min lost per dataset on normalization spreadsheets

1 in 4 papers fail MIQE normalization checks

Why Clarida

MIQE-compliant normalization

Built on published methods, not custom spreadsheets. Normalization that reviewers trust.

geNorm integration

Validated reference genes, not assumed ones. The gold standard for normalization built right in.

One workflow, every experiment

Same 5 steps whether it’s your first or fiftieth experiment. Consistency without the learning curve.

MIQE Guidelines - co-authored by our team

Who this is for

  • Every qPCR researcher doing treated vs. control comparisons
  • Academic labs, pharma preclinical teams, anyone doing differential expression
  • The most universal qPCR use case - if you’re doing qPCR, you’re probably doing this

Related use cases

Popular with academic labs, pharma R&D teams, and core facilities

VALIDATION & QC

Which reference genes are most stable across my tissue panel?

Learn more

VALIDATION & QC

Is there batch-to-batch variation in my cDNA synthesis?

Learn more
Not your use case? See all qPCR workflow types

Ready to get started?

Free to start. No credit card. Export your data anytime.

Run your first expression experimentStart analyzing for free