ArticleScientific reports2019
Selecting reference genes in RT-qPCR based on equivalence tests: a network based approach.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- Determination of the most suitable reference gene for transcription analysis of synovial tissue from osteoarthritis patients.Scientific reports · 2026Article
- Selection of Reference Genes for Normalization of Gene Expression After Exposure of Human Endothelial and Epithelial Cells to Hypoxia.International journal of molecular sciences · 2025Article
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- DA.Frontiers in cell and developmental biology · 2025Article
- Selection and Validation of Appropriate Reference Genes for qRT-PCR Analysis ofFood science & nutrition · 2025Article
- Brainstem transcriptomic changes in male Wistar rats after acute stress, comparing the use of duplex specific nuclease (DSN).Scientific reports · 2024Article
- RNA-seq validation: software for selection of reference and variable candidate genes for RT-qPCR.BMC genomics · 2024Article
- Effect of Trichomonacide 6-Nitro-1International journal of molecular sciences · 2024Article
- Decreased telomere length in a subgroup of young individuals with bipolar disorders: replication in the FACE-BD cohort and association with the shelterin component POT1.Translational psychiatry · 2024Article
- Canine urothelial carcinoma: a pilot study of microRNA detection in formalin-fixed, paraffin-embedded tissue samples and in normal urine.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2024Article
- Expression of housekeeping genes varies depending on mevalonate pathway inhibition in cancer cells.Heliyon · 2023Article
- A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets.Biomedicines · 2023Article
- Nuclear mechanoprotection: From tissue atlases as blueprints to distinctive regulation of nuclear lamins.APL bioengineering · 2022Review
- Validation of reference genes for the normalization of RT-qPCR gene expression inIranian journal of microbiology · 2022Article
- Innovative GenExpA software for selecting suitable reference genes for reliable normalization of gene expression in melanoma.Scientific reports · 2022Article
- Best Practices in qPCR and dPCR Validation in Regulated Bioanalytical Laboratories.The AAPS journal · 2022Article
- Evaluation of Reference Genes in the Polyploid ComplexPlants (Basel, Switzerland) · 2022Article
- Combining transcriptome analysis and GWAS for identification and validation of marker genes in thePeerJ · 2021Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Because quantitative reverse transcription PCR (RT-qPCR) gene expression data are compositional, amounts of quantified RNAs must be normalized using reference genes. However, the two most used methods to select reference genes (NormFinder and geNorm) ignore the compositional nature of RT-qPCR data, and often lead to different results making reliable reference genes selection difficult. We propose a method, based on all pairwise equivalence tests on ratio of gene expressions, to select genes that are stable enough to be used as reference genes among a set a candidate genes. This statistical procedure controls the error of selecting an inappropriate gene. Application to 30 candidate reference genes commonly used in human studies, assessed by RT-qPCR in RNA samples from lymphoblastoid cell lines of 14 control subjects and 26 patients with bipolar disorder, allowed to select 7 reference genes. This selection was consistent with geNorm's ranking, less with NormFinder's ranking. Our results provide an important fundamental basis for reference genes identification using sound statistics taking into account the compositional nature of RT-qPCR data. The method, implemented in the SARP.compo package for R (available on the CRAN), can be used more generally to prove that a set of genes shares a common expression pattern.
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