geNorm is the most popular algorithm to find stable reference genes from a set of tested candidate reference genes in a given experimental condition. From this a gene expression normalization factor can be calculated for each sample, based on the geometric mean of the selected reference genes.

The qbase+ implementation of geNorm provides five great benefits compared to the predecessor in Microsoft Excel:

  1. fully automated calculations
  2. handling of missing data
  3. expert reporting for in depth understanding of your results
  4. allows ranking of candidate reference genes up to the single most stable gene
  5. Twenty times faster

Additional resources:

Normalization strategies

The sustainable research approach by Jan Hellemans & Jo Vandesompele resulted in a revolutionary set of normalization methods. These methods have been implemented in qbase+ to accommodate a wide range of experiments with specific needs:

  • multiple reference gene normalization which takes multiple stably expressed reference genes for reliable normalization of most experiments into account
  • global mean normalization examines all targets for normalization and is useful for experiments in which a large number of unbiased genes are measured (e.g. whole genome miRNA profiling)
  • user-defined normalization factors which enable normalization based on a user provided value

Additional resources:

  • A recorded webinar on global mean normalization
  • miRNA expression profiling - from reference genes to global mean normalization (D'haene et al., 2012, Methods Mol Biol)
  • Blog post on how to find stably expressed microRNAs

Quality control

It's essential to evaluate the quality of post-qPCR data and to discard data points that don't meet the predefined criteria prior to drawing conclusions. qbase+ is an crucial tool within this process, offering several types of quality control:

  • evaluation of amplification efficiencies from dilution series
  • control on PCR replicate variation
  • assessment of positive and negative control samples
  • determination of reference gene expression stability
  • evaluation of deviating sample normalization factors
  • quality control on inter-run calibration
  • sample quality control (average expression level, fraction of genes expressed)


The integrated statistical analysis wizard eliminates the need for indepth knowledge about biostatistics. Biologists performing qPCR analysis will be able to generate and interpret statistical results without help from statisticians.

Additional resources:

Inter-run calibration

Whenever samples need to be compared that were measured in different runs, one should be cautious of a potential bias. Inter-run calibration is a calculation procedure to detect and remove (often underestimated) inter-run variation.

The use of qbase+ for this type of data processing is highly recommended:

  • avoids calculation errors
  • uses correct error propagation
  • enables inter-run calibration using more than one inter-run calibrator, which makes it more accurate and allows quality control
  • performs inter-run calibration after normalization, allowing to re-synthesize cDNA from the inter-run calibrator RNA samples

CNV analysis

Quality control, normalization, copy number calling and visualization in a single program:

  • more than one reference sample can be used for more accurate copy number calling
  • reference samples with varying copy numbers can be used
  • user-defined threshold for upper and lower boundary for normal copy number calling
  • copy numbers visualized on a per sample basis
  • conditional bar coloring allows for easy detection of gene copy number alterations


The aim of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines (Bustin et al., Clinical Chemistry, 2009) is to provide authors, reviewers and editors with specifications for the minimum information that must be reported for a qPCR experiment. MIQE ensures its relevance, accuracy, correct interpretation and repeatability.

Applying  the rigorous MIQE compliant  procedures becomes easy with qbase+. It guides you to results of the highest quality. The analyzed and annotated experiments can easily be exported to RDML files which can be used as supplemental data for publication.

Open format

No matter what qPCR instrument or computer operating system you're using. Whatever type of experiment you're performing. qbase+ offers elegant solutions:

  • works on Windows (Windows 10 or above), Mac (OS X 10.8.3 - 10.14) Catalina or higher is NOT supported and Linux
  • accepts export files with Cq values from most real-time PCR instruments:
    • Applied Biosystems: 5700, 7000, 7300, 7500, 7900, StepOne, StepOnePlus, ViiA7, OpenArray
    • Bio-Rad: iCycler, iQ5, MyiQ, Opticon, Opticon2, MiniOpticon, Chromo4, CFX96, CFX384
    • Corbett Research: Rotor-Gene 2000, Rotor-Gene 3000, Rotor-Gene 6000
    • Eppendorf: Mastercycler ep realplex
    • Fluidigm: BioMark
    • Illumina: Eco
    • Qiagen: Rotor-Gene Q
    • Roche: LightCycler Carousel, LightCycler 480, LightCycler 1536, LightCycler 96
    • Stratagene / Agilent: MX3000P, MX3005P, MX4000P
    • Wafergen: SmartCycler
    • 3 general, instrument independent formats
  • allows easy data export for convenient data exchange, storage, and publication