ArticlePlant molecular biology2010
Genome-wide identification and evaluation of novel internal control genes for Q-PCR based transcript normalization in wheat.
Article in Plant molecular biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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70 citing papers in PubMed, 135 citations in OpenAlex.
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- Q allele overexpression and alternative splicing can improve wheat yield by increasing thousand-kernel weight and grain number per spike.BMC plant biology · 2026Article
- Fine tuning wheat heading time through genome editing of transcription factor binding sites in Ppd-1 gene promoter.Scientific reports · 2025Article
- Validated reference genes for normalization of RT-qPCR in developing organs of wheat to study developmentally/spatio-temporally expressed family genes.Scientific reports · 2025Article
- Selection and Validation of qRT-PCR Internal Reference Genes to Study Flower Color Formation inInternational journal of molecular sciences · 2024Article
- Selection and validation of reliable reference genes for quantitative real-time PCR in Barnyard millet (Echinochloa spp.) under varied abiotic stress conditions.Scientific reports · 2023Article
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- Selection and Validation of the Most Suitable Reference Genes for Quantitative Real-Time PCR Normalization inPlants (Basel, Switzerland) · 2022Article
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- The PGS1 basic helix-loop-helix protein regulates Fl3 to impact seed growth and grain yield in cereals.Plant biotechnology journal · 2022Article
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- Selection of the reference genes for quantitative gene expression by RT-qPCR in the desert plant Stipagrostis pennata.Scientific reports · 2021Article
- Screening of stable internal reference gene of Quinoa under hormone treatment and abiotic stress.Physiology and molecular biology of plants : an international journal of functional plant biology · 2021Article
- Genome-wide screening of novel RT-qPCR reference genes for study of GLRaV-3 infection in wine grapes and refinement of an RNA isolation protocol for grape berries.Plant methods · 2021Article
- Overexpression of a wheat α-amylase type 2 impact on starch metabolism and abscisic acid sensitivity during grain germination.The Plant journal : for cell and molecular biology · 2021Article
- Co-Infections byPlants (Basel, Switzerland) · 2021Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To accurately quantify gene expression using quantitative PCR amplification, it is vital that one or more ideal internal control genes are used to normalize the samples to be compared. Ideally, the expression level of those internal control genes should vary as little as possible between tissues, developmental stages and environmental conditions. In this study, 32 candidate genes for internal control were obtained from the analysis of nine independent experiments which included 333 Affymetrix GeneChip Wheat Genome arrays. Expression levels of the selected genes were then evaluated by quantitative real-time PCR with cDNA samples from different tissues, stages of development and environmental conditions. Finally, fifteen novel internal control genes were selected and their respective expression profiles were compared using NormFinder, geNorm, Pearson correlation coefficients and the twofold-change method. The novel internal control genes from this study were compared with thirteen traditional ones for their expression stability. It was observed that seven of the novel internal control genes were better than the traditional ones in expression stability under all the tested cDNA samples. Among the traditional internal control genes, the elongation factor 1-alpha exhibited strong expression stability, whereas the 18S rRNA, Alpha-tubulin, Actin and GAPDH genes had very poor expression stability in the range of wheat samples tested. Therefore, the use of the novel internal control genes for normalization should improve the accuracy and validity of gene expression analysis.
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