Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) · 2014 · 16 citations · 18 references
Open access
EngineeringGeneticsTranscriptomics TechnologyGene CharacterizationMolecular GeneticsGenomicsReference GenesGene RecognitionGene Expression ProfilingTranscriptional RegulationQpcr DataMolecular DiagnosticsQpcr AnalysisPlant Gene ExpressionMolecular Biological MethodSequence AnalysisQuantitative Real-time PcrGene ExpressionBioinformaticsFunctional GenomicsProtein BiosynthesisBiologyQuantitative Real-time PcrSeed StorageMedicine
Quantitative real-time PCR (qPCR) has become a favourite method for quantification of mRNA transcripts. However, several optimisation steps must be performed to avoid misleading qPCR results. One of the steps is selection of reference genes for normalisation purpose and these genes should be stably expressed across the samples. In this study, isolation of partial-length cDNA encoding seven potential reference genes from Ganoderma boninense has been performed. These potential reference genes are α-tubulin, β-tubulin, β-actin, elongation factor 2 (eef2), glyceraldehyde 3-phosphate dehydrogenase (gapdh), 40S ribosomal (r40s) and ubiquitin C (ubc). The expression of these reference genes was studied in mycelia, white button and fruiting body tissues of G. boninense. The qPCR data were analysed using BestKeeper and geNorm algorithms and both softwares have identified β-tubulin, eEF2 and α-tubulin as the most stable reference genes and r40s and ubc as the least stable reference genes. Three reference genes with the lowest M value (eEF2, β-tubulin and α-tubulin) were recommended by the geNorm software to be used in the qPCR analysis for more accurate normalisation.
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Michael W. Pfaffl, Aleš Tichopád, Christian Prgomet et al. · Biotechnology Letters · 2004 · 5K citations
Housekeeping genes as internal standards: use and limits
Olivier Thellin, Willy Zorzi, Bernard Lakaye et al. · Journal of Biotechnology · 1999 · 1.6K citations · Full text