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Looks good.
If you want to see your RIN score you can check for errors in the error pane. It may be something like the "5S Region Anomaly Threshold" error. Then you can increase the 5S Region Anomaly Threshold from 0.5 to 1 and it should clear the error and give you a RIN score. But the RIN score will be unduly low due to the amount of small RNAs you have. So it is probably not worth the trouble.
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Phillip
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Plant RNA per se, will not necessarily give you poor RIN scores. We have a recent set of wheat RNAs submitted that all gave RINs > 9.5.
I think, other than actual degradation, the factor that will most lower the RIN score is a high concentration of small RNAs. That is typical of some isolation methods -- the Trizol (or other acid phenol) methods allow much higher yields of low molecular weight RNAs. For some applications that is actually good (well, obviously for small RNA projects). But for normal RNAseq experiments it can cause issues.
But, yes, it is not uncommon for plants to have a bunch of weird peaks (frequently plastid rRNA).
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Phillip
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by GATTACATLove this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
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Channel: Articles
07-01-2026, 11:43 AM -
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by SEQadmin2
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...-
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