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I was wondering if anyone had an empirical evidence in the comparison between the TruSeq and Nexterra protocols? We have been considering switching over to the Epicentre products exclusively but lack any definitive evidence of performance. Does anyone have an argument either way?
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First, Epicentre now is owned by Illumina, so no difference there.
Second, "Nextera" is a mixture of several component methodologies:
(1) "Tagmentation" (in vitro transposase action) is used instead of sonication or other methods of mechanical fragmentation.
(2) Dual indexes (one in each adapter) are deployed rather than the single indexes of TruSeq.
(3) At least for NexteraXT, "normalization" beads are used equalize all the libraries to be included in a single pool.
(4) The lower concentration of resulting library pools is compensated for by using a heat denaturation, rather than a base denaturation prior to clustering. This may be possible because the products come off the normalization beads as ssDNA.
We have not yet run any Nextera libraries. But the process does look streamlined -- especially for handling library construction for large numbers of samples (eg 96).
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Phillip -
Also....Nextera is now sold by Illumina, not Epicentre. Epicentre stopped selling the Nextera version 1 kits On Dec 31, 2011, and the Version 2 is now sold exclusively through Illumina.Comment
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Thanks but I was referring to sequencing performance? For example, sample coverage, anymore or less 5' or 3' bias, coverage of exons vs introns, coding vs non-coding genes?Comment
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you will need to contact Illumina regarding these parameters; I understand that Illumina has improved performance specs.Comment
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shearing differences
I'm told (but have no direct experience) that the "tagmentation" step, which uses enzymatic fragmentation via an engineered transposase, has some non-randomness that could potentially result in reduced coverage in certain areas. The transposase has some sequence specificity that you'd have to look up. But the sample prep sure is easy!Comment
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As I understand it, that's correct...minor G-bias.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.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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