Small RNA Library Sequencing on NextSeq

[jteeee2]

Does anyone have experience sequencing small RNA libraries on the NextSeq? We are consistently getting low cluster densities (140-150K/mm2) even with a 10% PhiX spike-in to increase diversity. The libraries were made with the NEBNext Small RNA Kit.

There is clearly low diversity in the libraries by looking @ the post-run specs, so I thought that increasing the PhiX while leaving loading concentration the same would help the issue (Quality scores have been great with %Q30 above 80%). What I actually found was that increasing the PhiX any more than 10% dropped cluster density even further.

I would truly appreciate any thoughts on this. Thanks!

[ipeikon]

Updates

Any updates on this? we are having similar issues!

[jteeee2]

We never really figured out what was going on with my previously mentioned observations. What I can say is that we have seen much better clustering of our small RNA libraries on the NextSeq over the past 6 months. We quantify by Qubit/BioA and load @ 1.8-2.2 pM.

[Pavo]

On the same matter

Hi,
We consider using nextseq instead of hiseq for sequencing small RNAs in C. elegans, but the rumor has it that the results tend to have serious problems with:
A. strong biases for the first nucleotides (or even more specifically - G as the 1st nuc)
B. hard time sequencing repetitive reads.
Have you experienced any of these problems?
Thanks in advance,
Pavo

[Brian Bushnell]

I have not noticed problem A (it is something I may look into... but it sounds like a conflated library-creation issue) and have no experience with problem B. But I will add that NextSeq has substantially lower quality than HiSeq, in my experience, so I suggest you carefully consider whether the decreased sequencing cost on a NextSeq is justified. Normal RNA-seq quantification is quite tolerant of error. Small RNAs... well, microRNAs are so short that you normally want to map them allowing zero mismatches. So, any errors are bad, and biased errors are extremely bad. But since they are so short, errors decrease the signal-to-noise ratio much more than they would for longer sequences, even if you allow zero mismatches, because with a 17-mer (for example) you can easily get coincidental perfect matches from reads with errors.

Unless you do not care about very short RNAs (under 50bp or so), I suggest you use the highest-quality platform available, which is MiSeq or HiSeq 2500. You can improve things a lot by merging overlapping reads, too, so aim for read lengths that substantially overlap (by at least 50% of the read length, or ideally 100%, if you want to minimize errors) in the size range of interest.