[Brian Bushnell]

Good work Brian! I am skeptical about your claim that NextSeq has less crossover than HiSeq or Miseq however.

We did fairly extensive analysis on this, and the results were never strongly reproducible from one run to the next on a given platform, but this one result (NextSeq outperforming HiSeq/MiSeq in crosstalk) was very consistent. Bearing in mind that we only had a single NextSeq machine at the time.

Would you please provide some data to back this up.

I may try to dig it up if I have some time; there is no comprehensive single report with all of it so it would be a lot of work.

And if that is the case maybe it's simply because demultiplexing is being done by CASAVA v2 on NextSeq and CASAVA v1 on HiSeq and Miseq. What if you did demultiplexing yourself, taking into account Quality scores (which I assume is not typically done).

I did do the demultiplexing manually, which is why (for example) mergebarcodes.sh and filterbarcodes.sh are in the BBTools package; I wrote them just for this experiment Even aggressive filtering on barcode quality was unable to substantially impact the relative differences between the platforms.

[ymc]

Unfortunately, Illumina's taken a turn for the worse again. I just analyzed some recent data from the NextSeq, HiSeq2500, and HiSeq 1T platforms of the same library. The NextSeq data is dramatically worse than last time I looked at it. Error rates are several times higher, there's a major A/T base frequency divergence in read 2, and the quality scores are inflated again at ~6 points higher than the actual quality. More disturbingly, the HiSeq quality scores are completely inaccurate now, as well, though the actual measured quality is still very high - average Q33 for read 1 and Q29 for read 2 for HiSeq2500, versus Q24 for read 1 and Q18 for read 2 on the NextSeq (those numbers are as measured by counting the match/mismatch rates from mapping, so essentially, NextSeq has roughly 10X the error rate of HiSeq). But the measured discrepancy between claimed and measured quality scores for the HiSeq2500 and HiSeq 1T are BOTH worse than the NextSeq, despite the NextSeq having binned quality scores, and as you can see there are large regions of quality scores simply missing from the HiSeq2500, such as Q3-Q11, Q17-Q21, and Q29. There are clearly major problems with Illumina's current base-calling software, as quality score assignment has drastically regressed since last time I measured it.

You can see the graphs in this Excel sheet that I've linked. "Raw" is the raw data, "Recal" is after recalibration (which changes the quality scores but nothing else). "NS" is NextSeq, "2500" is HiSeq2500, and "1T" is HiSeq 1T which unfortunately was only run at 2x101bp instead of 2x151bp on the other 2 platforms.

https://drive.google.com/file/d/0B3l...ew?usp=sharing

Are there any updates for the current state of nextseq? Is it still this bad or return to the good quality when v2 came out?

[bini]

Next Seq Error

Hi,

I recently had an error message during a run " Camera 5 lane 3 bottom surface disabled: Failed to detect Clusters" " Camera 6 lane 3 bottom surface disabled: Failed to detect Clusters"and so on for multiple cameras. Does this mean there is something wrong with my cameras?

Please advise if you have come across similar error. Thanks

[GenoMax]

Hi,

I recently had an error message during a run " Camera 5 lane 3 bottom surface disabled: Failed to detect Clusters" " Camera 6 lane 3 bottom surface disabled: Failed to detect Clusters"and so on for multiple cameras. Does this mean there is something wrong with my cameras?

Please advise if you have come across similar error. Thanks

Those errors are most likely due to over-clustering or absence of clusters (if at the beginning of the run). Did the run fail? With camera errors it is always good to consult Illumina tech support to rule out any hardware errors.

[JKHatt]

Just to add our 2 cents.

We have a MiSeq running since 2013, and after some hickups we're now stable with it and reasonably happy.

We just recently installed a NextSeq500 and our first tests are not great. Q30 is >80%, but there are many low quality bases (constantly Q=14 "/"), and the worst part is that instead of being towards the end, they seem a bit randomly distributed. When comparing PhiX in a 2x150bp NextSeq with a 2x250bp MiSeq, after alignment I see a 0.2-0.3% error rate with MiSeq and 0.9-1% error rate with NextSeq (1M sampled reads). In the "randomly" distributed Q=14 bases I seem to notice more A to T transitions, but I didn't have time to gather more systematic statistics... If I do quality trim on the MiSeq I can easily get higher quality data, with the NextSeq since its randomly distributed is harder...

We've complained to the Illumina people, let's see what they say...

Hello,
Were you/anyone else ever able to resolve this issue with random Q14 scores throughout the length of the read? We currently see the same in our data.
Our core recently mothballed our HiSeq2500 due to outrageously high service contract costs. We have converted to a NextSeq500 running NextSeq control software v2.2.0.4, RTA v2.4.11. We are running Blue Pippin selected multiplexed Nextera XT libraries (we are an environmental microbiology lab so we can have very low nucleic acid yields so we stick with XT) for 300 cycle PE High output runs . I'm told we can get around this random low quality issue for metagenome assembly as long as the quality scores of the other bases are true and we sequence deep enough but we are also interested in SNP analysis on some of our other projects and this would be problematic (I don't do these analyses myself-I'm on the front end of library making and sequencing). Our MiSeq is also a bit aged and misbehaving so currently on-site we only have access to instruments running two-dye chemistry. One problem that we seem to see consistently on our NextSeq besides the data quality (now for 3 runs) is that the green channel intensity is consistently low. Illumina tells me that this is not good and could be the camera. My core tells me that this issue only occurs on my runs (they seem to blame the Nextera XT). Any help/insight would be appreciated.

[Juan Antonio]

That is very interesting, thanks for posting.

[Melissa]

Great discussion and thanks for sharing the plots. In short, Nextseq generates higher error rate in variant calling. This is often overlooked when buying the machine. It poses great challenges for bioinformaticians.