How to use the RefGenes tool in 4 simple steps

RefGenes is an online tool from the GENEVESTIGATOR platform, located within the Gene Search toolset. Start it by clicking on the START button on this website. RefGenes will be launched with an example gene from mouse, so you will need to choose your organism and conditions of interest.


Choose a biological context similar to your RT-qPCR experiment

The goal of RefGenes is to identify genes that are stable in a chosen biological context. In this step, you create a selection of microarrays from samples that were collected in conditions similar to those in your RT-qPCRexperiment, e.g. a similar tissue type.
  • Click on "New" in the Sample Selection section
  • Choose organism and platform of interest (usually default platform is fine)
  • Click on "Select particular conditions"
  • In the dialog that appears, select "Anatomy" and choose a tissue type (or multiple tissue types) similar to the one(s) in your RT-qPCR experiment
Generally, we recommend to choose at least 50 microarrays from at least three independent experiments/studies.



Enter your target gene of interest

This step is not essential, but it helps you to see whether your target gene is strongly or weakly expressed in the conditions of interest selected in STEP1. This then allows you to search for candidate reference genes in a similar range of expression. Initially, you enter gene identifiers (e.g. UniGene or Entrez IDs) for transcripts you intend to measure in your RT-qPCR experiment. The software will let you know if these genes exist in at least one platform in GENEVESTIGATOR.



Search for the most stable genes in this biological context

Simply click on the "Run" button in the RefGenes tool. RefGenes will show the top 20 most stable genes across the selection of microarrays from STEP 2. Your target gene(s) of interest entered in STEP1 are displayed in the upper section, while candidate reference genes are shown in the lower section.


We recommend to choose three reference genes, one in a slightly lower range of expression, one in a similar range, and one in a slightly higher range of expression than target genes (compare correponding boxplots). If needed, broaden the search range by changing the numbers in the fields for "Range".


Validate candidate reference genes against a compendium of experimental conditions

Finally, candidate reference genes proposed by RefGenes should be checked against various other conditions.
  • Create a new selection of genes containing the newly found candidate reference genes (top right)


  • Check how they respond to various conditions (Perturbations tool in the Condition Search toolset).


This step will help you to obtain not only the most stable candidates for a particular context, but also those that are non-responsive to individual conditions that may occur within your RT-qPCR experiment.


Video Tutorial

For a more detailed step-by-step introduction, please view our video about RefGenes.

Video Tutorials


Data from GENEVESTIGATOR is normalized using RMA on an experiment level, and an additional inter-experiment correction is done by scaling the mean of the 90% quantile value of a given experiment to a target value. Details about this normalization are provided in the GENEVESTIGATOR documentation.


This approach has been validated several times in the literature and has been analyzed in detail in our own publication.

RefGenes publication:

Hruz T, Wyss M, Docquier M, Pfaffl MW, Masanetz S, Borghi L, Verbrugge P, Kalaydjieva L, Bleuler S, Laule O, Descombes P, Gruissem W and P Zimmermann (2011) RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization. BMC Genomics 2011, 12:156. [Abstract] [PDF]

Other studies that identified and validated reference genes from microarray data (earlier studies, without the use of RefGenes):

Czechowski T, Stiit M, Atlmann T, Udvardi M, Scheible W: Genome-Wide Identification and Testing of Superior Reference Genes for Transcript Normalization in Arabidopsis. Plant Physiol 2005, 139:5-17.

Saviozzi S, Cordero F, Lo Iacono M, Novello S, Scagliotti G, Calogero R: Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer. BMC Cancer 2006, 6:200.

Hamalainen H, Tubman J, Vikman S, Kyrola T, Ylikoski E, Warrington J, Lahesmaa R: Identification and validation of endogenous reference genes for expression profiling of T helper cell differentiation by quantitative real-time RT-PCR. Anal Biochem 2001, 299(1):63-70.

Gabrielsson BG, Olofsson LE, Sjogren A, Jernas M, Elander A, Lonn M, Rudemo M, Carlsson LM: Evaluation of reference genes for studies of gene expression in human adipose tissue. Obes Res 2005, 13:649-652.

Stamova BS, Apperson M, Walker WL, Tian Y, Xu H, Adamczy P, Zhan X, Liu DZ, Ander BP, Liao IH, Gregg JP, Turner RJ, Jickling G, Lit L, Sharp FR: Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood. BMC Med Genomics 2009, 2:49.

Kwon MJ, Oh E, Lee S, Roh MR, Kim SE, Lee Y, Choi YL, In YH, Park T, Koh SS, Shin YK: Identification of novel reference genes using multiplatform expression data and their validation for quantitative gene expression analysis. PLoS ONE 2009, 4:e6162.

Gur-Dedeoglu B, Konu O, Bozkurt B, Ergul G, Seckin S, Yulug IG: Identification of endogenous reference genes for qRT-PCR analysis in normal matched breast tumor tissues. Oncol Res 2009, 17:353-365.

Popovici V, Goldstein DR, Antonov J, Jaggi R, Delorenzi M, Wirapati P: Selecting control genes for RT- QPCR using public microarray data. BMC Bio formatics 2009, 10:42.

Pilbrow AP, Ellmers LJ, Black MA, Moravec CS, Sweet WE, Troughton RW, Richards AM, Frampton CM, Cameron VA: Genomic selection of reference genes for real-time PCR in human myocardium. BMC Med Genomics 2008, 1:64.

Frericks M, Esser C: A toolbox of novel murine house-keeping genes identified by meta-analysis of large scale gene expression profiles. Biochim Biophys Acta 2008, 1779:830-837.

Lee S, Jo M, Lee J, Koh S, Kim S: Identification of novel universal housekeeping genes by statistical analysis of microarray data. Biochem Mol Biol 2007, 40(2):226-231.