A first look at Illumina’s new NextSeq 500

[GenoMax]

You could have a library that is partially bad?

[Brian Bushnell]

Have you looked at the images to see if there is some flowcell location correlation to the low-quality reads?

[rogerzzw]

I have no idea what might cause it...... consistently.

[rogerzzw]

@Brian Bushnell

Illumina said it is very normal by looking the flowcell and other metrics.

[GenoMax]

I have no idea what might cause it...... consistently.

Possibility from post #76.

[kentawan]

This upgrade really is a pain in the backside. I have no idea why Illumina couldn't make this back compatible and then use the RFID chips to determine whether to process the run as required.

I wonder what happens if someone comes wanting to compare to an older data set once we've switched to v2. They'll need to pay to re-run those old libraries on v2.

I agreed. We wrote a feedback email to their engineering team too. As you might have expected, it is a disappointing email. Lesson learnt, don't buy too many kits in one shot.

[kentawan]

Hi, Kentawan.

I am a beginner of NGS on Nextseq 500. I am primarily doing whole genome bisulfite sequencing with Nextseq 500. I saw your post on this forum and would like to learn from you.

1. based on what you said, you use V2 kit and got a very good cluster density. we used V2 but never get that high cluster density. Ours is about 140K. Can you share your experience on that? Appreciate!

2. We always had a big issue on Barcode reading, other metrics look very good. There are a high portion (35%) reads are reading as "GGGGGG", I think it is due to reading failure, but we do not know how to pin it down, we pooled two samples each time. Unfortunately, there was one time, we pooled with other 4 Chip-seq samples and still the same. Do you some idea how to fix it?

Thank you very much

Roger

Hi Roger,

We spiked in a good 20% freshly diluted PhiX library, as our library's first 6 bases are the same due to restriction enzyme processing.

As for your question 1, I'd like to know how do you do your library quantification? We used KAPA's library quantification kits. You might want to do it the old school way, clone the library into T-vector or something and Sanger sequence it to see if the P5 and P7 are sequence correct and in good shape. I suspect your P5 and P7s are not working properly.

as for question 2, again, would like to know if it's possible for you to Sanger sequence your library (let's say 50 clones would be good.). Illumina's P5 and P7 sequences are actually close to the annealing sites for the index primers. I suspect your P5 and P7 sequences and nearby sequences are off, hence the failure for the indexing primer to anneal and subsequently you get dark spots on the cluster (a.k.a. GGGGGG)

Hope this helps! Good luck and may the bases be with you!

[rogerzzw]

@Kentawan

Hi, Kentawan

we used Kapa quantification kit, too. and we applied very strict quantification. We quantified individual library concentration firstly and pooled them together based on the measured concentration. We did another quantification when we dilute the pool in 20pm to make sure it is 20pm indeed. I believe our quantification is good enough.
considering cloning check, I do not know if it is proper because we wanna do whole genome sequencing. But I do agree that P5 and P7 might not be good enough and it is possible due to index annealing. I just do not how to confirm it. Do you get some idea?

Thank you very much

Roger

[rogerzzw]

BTW, Kentawan

Another lab using the same kit and protocol do not have this kind issue at all, which makes me very confusion.

[cmbetts]

Using High output we are actually getting over 500 million reads per run. Unlike our GAII, and HighSeq, we actually have to pay very close attention to cluster density. The target cluster density for high quality samples is 1.75pM-2pM. Anything above and below will results in under/over clustering. So your samples need to be very exact with concentration.

These are solely made to be streamlined with the BaseSpace. Right now it only works with BaseSpace onsite, not in the cloud as they are having some majority broker issues that still are not resolved. Make sure you do your research in regards to output files and data in regards to basespace because it is not a visual machine. It gives you the output files and you must use 3rd party software on a different computer to view the results. Very annoying.

Overall very impressed with the NextSeq's, not so much BaseSapce.

Does anyone have any feedback on what density would be considered overclustered on a NextSeq using v2 chemistry? We just got data back from a collaborator with terrible error rates in read 2 with lots of random stretches of variable length polyGs. Comparing to the SAV files from another successful run with an identically constructed library by the same facility, the only obvious run metric that jumps out at me (besides the terrible read quality) is that the failed run had ~20% higher cluster density (240k/mm^2 70%PF vs 200k/mm^2 80%PF). I'm mostly used to looking at HiSeq and MiSeq data, so I'm not sure whether this is significant or not.

[TonyBrooks]

Does anyone have any feedback on what density would be considered overclustered on a NextSeq using v2 chemistry? We just got data back from a collaborator with terrible error rates in read 2 with lots of random stretches of variable length polyGs. Comparing to the SAV files from another successful run with an identically constructed library by the same facility, the only obvious run metric that jumps out at me (besides the terrible read quality) is that the failed run had ~20% higher cluster density (240k/mm^2 70%PF vs 200k/mm^2 80%PF). I'm mostly used to looking at HiSeq and MiSeq data, so I'm not sure whether this is significant or not.

We've run exomes that clustered at 259k/mm2. The data still looked fine to us (92% >Q30, >90% alignment rates). The quality does begin to tail off when over-clustered though. 75bp are generally fine at that density, but the >100bp begins to look really poor. We also use short paired reads for RNA-Seq (43bp paired end) and this tolerates over-clustering much better.

On another note, we regularly see poly-G reads, (fastqc shows around 2-3% of over-represented sequences) but curiously this tends to happen on read 2 only (failed resynthesis?)

[AlexT]

I am not sure how much we can use the cluster densities as a measure of run quality. We had good runs with low and high cluster densities, as well as very poor run with normal densities.
Compared to the MiSeq, our NextSeq is very fragile and the cluster densities go up and down without showing an obvious pattern. In case of the MiSeq we have very stable densities (but in this case usually prepared with Nextera).

Actually our highest clustered run performed very well and had the following specs:
clusters: 287-301k/mm^2
PF: 83,0-84,3
Q30: 87,9-90,1
also at 75bp

[cement_head]

I know this is an older thread, but now that more and more users of the NextSeq are out there - what is the concensus on the NextSeq data? Is it still problematic relative to MiSeq Data?

Thanks

[Brian Bushnell]

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

[GenoMax]

HiSeq 1T = HiSeq 2500 HO mode?

Bottom line: If one has a different sequencer accessible walk away from a NextSeq?

Are Q-scores still important (other than for de novo or diagnostic analyses)?

Do you know what version of bcl2fastq is being used for your data?