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Volatile sulfur compounds gradually develop in Lentinula edodes after hot-air drying, and many genes are involved in the generation of these sulfur compounds. The expression stability of reference genes may vary in a particular experimental treatment when analyzing their expressions by quantitative real-time polymerase chain reaction (qRT-PCR). In this study, the expression profile of 17 candidate genes was assessed in L. edodes under treatment at 50 °C for 0, 1, 2, and 3 h, and the expression stability of each reference gene was analyzed by three statistical algorithms, including geNorm, NormFinder, and BestKeeper. Results indicated that the two optimal reference genes for mycelium and fruiting body were CAC and DAHP as well as CAC and NUP, respectively. Additionally, CAC and DAHP were found to be the two most stable reference genes across the mycelium and fruiting body set. Our results will provide a genetic foundation for further research on the metabolism genes of sulfur compounds in L. edodes.
Pubmed ID: 30602709 RIS Download
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Excel-based tool using pair-wise correlations for determination of stable housekeeping genes, differentially regulated target genes and sample integrity. It determines the best suited standards, out of ten candidates, and combines them into an index. The index can be compared with further ten target genes to decide, whether they are differentially expressed under an applied treatment. All data processing is based on crossing points.
View all literature mentionsSoftware for identifying the optimal normalization gene among a set of candidates. It ranks the set of candidate normalization genes according to their expression stability in a given sample set and given experimental design. It can analyze expression data obtained through any quantitative method e.g. real time RT-PCR and microarray based expression analysis. NormFinder.xla adds the NormFinder functionality directly to Excel. A version for R is also available.
View all literature mentionsSoftware to determine most stable reference (housekeeping) genes from set of tested candidate reference genes in given sample panel. From this, gene expression normalization factor can be calculated for each sample based geometric mean of user-defined number of reference genes.
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