Validating microarray data with real time rt pcr

However, in more recent years, both techniques are highly reproducible, and several reports say the technical replicates of the two methods have a correlation higher than 0.99.

If you start with the same RNAs, the results are essentially the same.

When I was young, video tapes had two formats, VHS or beta.

For computer data storage, there were cassette tapes and floppy disks.

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.

In order to find whether the results obtained from RNA-seq or microarray are accurate, quantitative real-time PCR (q-RT-PCR) is most commonly used.

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.

Then you examined the probes for this gene on microarray, and you found that probes only cover shared exons among the splice variants.

When log-transformed data for RNA-seq and microarray are plotted, however, they don’t look uniformly distributed around the trend line (see figure below). (2014), This is due to the difference in dynamic range.

Dynamic range of RNA-seq is dependent on the depth of sequencing while microarray has more or less a fixed dynamic range.

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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