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.

clever online dating headlines examples - Validating microarray data with real time rt pcr

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.