What is NormiRazor?
NormiRazor is a web-based tool that screens for experiment-specific reference genes. It uses well known algorithms: BestKeeper (Pfaffl et al., 2004), NormFinder (Andersen et al., 2004) and geNorm (Vandesompele et al., 2002), but contrary to predecessors it analyses not only single genes, but also combinations of 2 or 3 of them.
The idea of combination-based references for qPCR experiments originated from the need to find reliable normalizers for miRNA in biofluids that would allow for translation of scientific discoveries into practical miRNA biomarker assays. It was described by our team at the beginning of 2020 (Pagacz et al., 2020), where we showed that such references are more stable than single miRNAs.
The new algorithm was initially implemented in Python in order to prove the concept of combination-based references. However, its computational demands prompted us to find a more efficient solution that we could share to scientific community. The result of our efforts is NormiRazor with fast CUDA computational kernel and user friendly web interface.
How to use NormiRazor?
First, you need to register here. We register the users so that each of them has access only to their own data and results. We ask you to provide email address as an identifier of your account. NormiRazor will automatically send you activation link and later notifications when your analyses are finished.
When your account is activated you can upload your data, following instructions provided at the bottom of upload page. All your analyses will be accessible in History tab. If an analysis is successfully finished, you will be able also to download you dataset normalized with respect to one of best references identified by NormiRazor.
You don't need to install anything on your computer, because NormiRazor runs on our server.
If you want to try NormiRazor, you can download one of test datasets below and run the analysis.
|GCT file||CLS file||Description|
|Neuroblastoma dataset GCT||Neuroblastoma dataset CLS||
qPCR miRNA profiling of 95 neuroblastoma samples.
Data from Gene Expression Omnibus (GEO) at accession GSE121513.
CLS file groups samples by MYCN status: 1 - normal, 2 - amplified.
|Cognitive impairment dataset GCT||Cognitive impairment dataset CLS||
qPCR miRNA profiling in plasma of 23 patients with mild cognitive impairment (1 in CLS file) and 30 normal controls (0 in CLS).
Data from GEO dataset GSE90828.
|Pulmonary hypertension dataset GCT||Pulmonary hypertension dataset CLS||qPCR miRNA profiling in 4 treatment-naïve idiopathic pulmonary arterial hypertension patients (IPAH in CLS file) and 4 controls (Control in CLS). Data from GEO dataset GSE68314.|
How to cite NormiRazor?
A combination-based references for qPCR experiments are described in:
The technical description of NormiRazor and the results of benchmarks are available in:
- Andersen CL, Ledet-Jensen J, Orntoft TF. (2004) Normalization of Real-Time quantitative reverse transcription- PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, 64, 5245–5250.
- Pagacz K, Kucharski P, Smyczynska U, Grabia S, Chowdhury D & Fendler W. (2020) A systemic approach to screening high-throughput RT-qPCR data for a suitable set of reference circulating miRNAs. BMC Genomics, 21, 111.
- Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. (2004) Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper - Excel-based tool using pair-wise correlations. Biotechnology letters, 26(6), 509-15.
- Vandesompele J, De Preter K, Pattyn I, Poppe B, Van Roy N, De Paepe A, Speleman R. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, 3, 0034.1–0034.11.