Validating microarray data with real time rt pcr dlsu validating test results
In this case, you need to figure out which form(s) of the gene is (are) actually differentially expressed.While this can be done with q-RT-PCR or northern blot analysis, it takes more time and effort to confirm it.
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I can guess there would be no significant difference in medium to high expression genes between RNA-seq and microarray, however, the lower and higher ends of gene expression are likely more accurate for RNA-seq due to its better dynamic range.
Let’s say you find a differentially expressed gene which is potentially very interesting in microarray.
Some m RNAs have only a few copies per cell, while the most abundant ones have 10,000 copies per cell.
Before talking about dynamic range, let’s talk about how similar data obtained by RNA-seq and microarray are.
In terms of transcriptome analysis, DNA microarray has dominated the last decade.
Recently, however, Next Gen sequencing (NGS) technology has provided a new path for gene expression analysis.In this post, I want to compare gene expression analysis using two platforms: RNA-seq and DNA microarray.When DNA microarray was technology first introduced, spot c DNA microarray was quite variable between arrays and it was necessary to run technical replicates as well as dye-swap experiments.The more microarray contains probes for possible variants, the fewer issues with identifying and quantifying specific variants.RNA-seq is also capable of detecting single nucleotide polymorphisms (SNPs).Illumina says the sensitivity of microarray (human) for the major vendor is 10 million mapped reads/sample on average, RNA-seq should provide a lot higher sensitivity than microarray.