Validating microarray data using rt real time pcr
High-density filter arrays identified 444 of 588 genes as either negative or expressed with less than twofold difference, and the other 144 genes as expressed uniquely or with more than twofold difference between the two subclones.Real-time RT-PCR used Light Cycler-based SYBR Green I dye detection and melting curve analysis to validate the relative change in gene expression.We show how the method can significantly increase validation success rates.In conclusion, in this study, we have successfully added a new automated step to determine the contributory sources of noise that determine successful or unsuccessful downstream biological validation.To accelerate this laborious task, the Super Array RT²Profiler PCR Array combines SYBR Green–based real-time RT-q PCR technology with a multi-gene array plate format to simultaneously analyze a panel of genes related to a specific disease or biological pathway.Each assay on the PCR array plate has been experimentally validated to insure gene-specific amplification.Reverse transcription quantitative real-time PCR (RT-q PCR) is a sensitive technique for quantifying gene expression, but its success depends on the stability of the reference gene(s) used for data normalization.Only a few studies on validation of reference genes have been conducted in fruit trees and none in banana yet.
The network displays the significant sources of noise in an experiment, and scores the likelihood of validation for every gene.Real-time RT-PCR confirmed the change in expression of 17 of 24 (71%) genes identified by high-density filter arrays.Genes with strong hybridization signals and at least twofold difference were likely to be validated by real-time RT-PCR.In one laboratory, individual PCR assays produced a standard deviation of 0.24 cycles and a coefficient of variance of 0.92% in technical replicates.The correlation coefficient for Ct values between replicate runs was 0.997 and for fold changes (ΔΔCt) across thermocyclers was 0.976.
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In the present work, 20 candidate reference genes were selected, and their expression stability in 144 banana samples were evaluated and analyzed using two algorithms, ge Norm and Norm Finder.