|
[introduction]
geNorm is a popular algorithm to determine the most stable reference (housekeeping) genes from a set of tested candidate reference genes in a given sample panel. From this, a gene expression normalization factor can be calculated for each sample based on the geometric mean of a user-defined number of reference genes. The algorithm also determines the optimal number of reference genes needed for accurate normalization.
Between 2002 and 2010, the Microsoft Excel version of geNorm has been downloaded more than 15,000 times worldwide. Between 2010 and 2023, an improved geNorm module was integrated in Biogazelle's qbase+ software for quantitative PCR data analysis in Windows, Mac and Linux. After Biogazelle's acquisition by CellCarta, a free Windows-only version is made available.
The underlying principles and formulas are described in Vandesompele et al., Genome Biology, 2002, 'Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes'. The full article can be read at http://genomebiology.biomedcentral.com/articles/10.1186/gb-2002-3-7-research0034
In a 2013 blog by Anna Perman, the story is told behind the geNorm algorithm and how the accompanying article almost got not published at all.
In a follow-up paper we developed the global mean normalization method (also available in qbase+) that is especially useful for normalization of data coming from a large and unbiased set of genes, e.g. microRNA gene expression profiling: Mestdagh et al., Genome Biology, 2009, 'A novel and universal method for microRNA RT-qPCR data normalization'. The full article can be read at https://genomebiology.biomedcentral.com/articles/10.1186/gb-2009-10-6-r64
[geNorm citations]
According to Google Scholar, more than 22,000 papers have cited the geNorm method (with an average of 100 citations per month). The article is part of the collection "Twenty years with Genome Biology", a list of some of Genome Biology's 'most accessed and interesting articles' over the last two decades.
[Improved geNorm in qbase+ software]
The more than 20 year old geNorm for Microsoft Excel is no longer available for several reasons (no longer compatible with later versions of Excel, slow, difficult to use).
The benefits of the new module in qbase+ are:
- fully automatic and expert result report
- handles missing data
- single best reference gene identification (in contrast to older implementation where best gene pair was identified)
- available for Windows, Mac and Linux
- much faster
The manual is available here.
[How to get geNorm?]
geNorm was part of Biogazelle's qbase+ software for quantitative PCR data-analysis. Since Biogazelle's acquisition by CellCarta end of 2021, a Windows version of the software can be downloaded here. The software is free but comes without support.
There are also several (older) implementations in R, Python, SAS, or are web-based:
- https://www.gear-genomics.com/rdml-tools/analyze.html (web)
- https://www.ciidirsinaloa.com.mx/RefFinder-master/ (web)
- https://www.bioconductor.org/packages/release/bioc/html/NormqPCR.html (R)
- https://pypi.org/project/eleven/ (Python)
- https://doi.org/10.1371/journal.pone.0029788 (SAS)
- https://pypi.org/project/rna-genorm/ (Python)
[user feedback]
"We have been using geNorm when examining cell wall synthesis enzymes in barley tissues and are happy with the results. Thanks for generating such a useful tool and making it universally available."
Neil Shirley, University of Adelaide, Australia
"I am extremely impressed with this approach to normalization."
Chris Tse, Sagres Discovery, USA
"I can no longer analyze my PCR runs without using geNorm!"
Marie-Jeanne Pillaire, CNRS, France
last update on 2024-08-21