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Originally posted by TonyBrooks
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Thanks. Looks like I should use the HS kit since my DNA is only about 5 ng/ul -
it's pointed out in another thread somewhere but if you are switching between the 2 kits you need to do some comparison tests of your samples using each kit. BR tends to give a higher reading by a high percentage vs the HS in my experience.Originally posted by lailaizhang View Post
I once ran out of the BR dsDNA assay which I had been using to measure the concentration of my RNAseq libraries for cluster concentration calculations and instead used a HS assay in a pinch, did the calculations the same and clustered at "the same" concentration and all my libraries over clustered. I then did a side by side of the libraries with the BR and HS and there was on average a 29% difference between the values given, with the BR being higher. All the libraries were falling in the 40ng/ul range on the HS so they were well with in the BR kit range
I had good consistent clustering results with the BR assay when using it, and only had a problem with it when i switched to HS without making adjustments for the different values given. (<-- unknown to me at the time, or an oversight) I will stick with HS from now on for anything under 100ng/ul, I do feel like it is more accurate with in that range vs the BRLast edited by subxero; 10-01-2015, 04:23 AM.Comment
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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.Today, 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... -
Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
...06-02-2026, 10:05 AM
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