Paper
28 September 2016 The utility of optical detection system (qPCR) and bioinformatics methods in reference gene expression analysis
Agnieszka Skarzyńska, Magdalena Pawełkowicz, Wojciech Pląder, Zbigniew Przybecki
Author Affiliations +
Proceedings Volume 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016; 1003130 (2016) https://doi.org/10.1117/12.2249147
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 2016, Wilga, Poland
Abstract
Real-time quantitative polymerase chain reaction is consider as the most reliable method for gene expression studies. However, the expression of target gene could be misinterpreted due to improper normalization. Therefore, the crucial step for analysing of qPCR data is selection of suitable reference genes, which should be validated experimentally. In order to choice the gene with stable expression in the designed experiment, we performed reference gene expression analysis. In this study genes described in the literature and novel genes predicted as control genes, based on the in silico analysis of transcriptome data were used. Analysis with geNorm and NormFinder algorithms allow to create the ranking of candidate genes and indicate the best reference for flower morphogenesis study. According to the results, genes CACS and CYCL were characterised the most stable expression, but the least suitable genes were TUA and EF.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Agnieszka Skarzyńska, Magdalena Pawełkowicz, Wojciech Pląder, and Zbigniew Przybecki "The utility of optical detection system (qPCR) and bioinformatics methods in reference gene expression analysis", Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 1003130 (28 September 2016); https://doi.org/10.1117/12.2249147
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Cited by 3 scholarly publications.
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KEYWORDS
Polymers

Statistical analysis

Genetic algorithms

Biological research

Bioinformatics

Proteins

Tissues

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