<p>FastQC (<a href="https://www.bioinformatics.babraham.ac.uk/projects/fastqc/" rel="nofollow noreferrer">https://www.bioinformatics.babraham.ac.uk/projects/fastqc/</a>) is a good start: it will show you observed/expected ratios for the most overrepresented sequences, and running it on a few datasets you know to be high quality should give you intuition for what looks good and what doesn't.</p>

<p>My labmate also produced something called preseq (<a href="http://smithlabresearch.org/software/preseq/" rel="nofollow noreferrer">http://smithlabresearch.org/software/preseq/</a>) that uses the library complexity of an initial sequencing experiment to predict how many more unique reads you would get with additional sequencing of the same library. It could give you some intuition for whether or not it is worth sequencing your library deeper to get the information you need.</p>

CC BY-SA 3.0
2018-03-08T17:56:22.650Z
2018-03-08T17:56:22.650Z
Ben D.
<p>I made some NEB ultra II whole-genome shotgun libraries with around ~200ng of template going into the indexing PCR step. The PCR was run for 13 cycles and I ended up having high library concentrations between 60-90 ng/µL. Should I be worried about complexity or PCR duplicates? How would all of you QC to make sure the library is complex enough and not overamplified too badly?</p>

<ngs><illumina><genome-sequencing>
How to QC overamplified shotgun library?
# How to QC overamplified shotgun library?

(Asked by: username_0 on 2018-03-08)

I made some NEB ultra II whole-genome shotgun libraries with around ~200ng of template going into the indexing PCR step. The PCR was run for 13 cycles and I ended up having high library concentrations between 60-90 ng/µL. Should I be worried about complexity or PCR duplicates? How would all of you QC to make sure the library is complex enough and not overamplified too badly?

**Tags:** ngs, illumina, genome-sequencing

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## 1 answers

### Answer by username_1 on 2018-03-08. Score: 1 | Accepted answer

FastQC (<https://www.bioinformatics.babraham.ac.uk/projects/fastqc/>) is a good start: it will show you observed/expected ratios for the most overrepresented sequences, and running it on a few datasets you know to be high quality should give you intuition for what looks good and what doesn't.

My labmate also produced something called preseq (<http://smithlabresearch.org/software/preseq/>) that uses the library complexity of an initial sequencing experiment to predict how many more unique reads you would get with additional sequencing of the same library. It could give you some intuition for whether or not it is worth sequencing your library deeper to get the information you need.


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