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If we get a pre adaptor, size selected library with an average size of 170 bp then after the adaptors (which are 41 and 53 bp) I would expect to see 170+41+53=264bp but what we actually see is more like 247bp. Does this seem normal? Perhaps the post adaptor size is not as expected because one of the adaptors is single stranded at it's 5' end?
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What method are you using to size the library? Pre or post enrichment PCR?
In any case I would not sweat the difference between 264 and 247 bp. Most likely the size is not exactly right. Keep in mind that if you are using some sort of electrophoresis based method that differences between the sequence composition of your sample and the size standard would likely be sufficient to explain a 7% difference. But other factors (salt concentration difference between the standard and sample) are also going to throw off your size estimation.
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Phillip -
Thanks for your reply, we are using the Agilent Bioanalyser with the DNA 1000 kit and 'yes' we are aware of the variation that is inherent. It has since been pointed out to me that the bioanalyser only reads double stranded DNA and the Standard adaptor has over 30 bases of single strand so combining this with the variation seems to account for what we see.
thanks again PhillipComment
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Hi JPC,Originally posted by JPC View Post
Not sure what was intended by the person that told you this.
The bioanalyser DNA chips do "read" single stranded molecules. If by "read" you mean "visualize". You can check this yourself by running the ssRNA ladder for the RNA chips on a DNA chip. You will see peaks. Please see:
for details. Counter-intuitively ssDNA segments would tend to actually slow the migration of molecules on the DNA agilient chips I have used.
Actually, had you not already done the enrichment PCR step prior to your running your chip? If so, then the amplicon should be completely dsDNA.
But I guess you library is fine in any case...
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PhillipComment
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Love 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.07-01-2026, 11:43 AM
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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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