[seqsense]

I appreciate the concern of using a true binary representation of bases, namely the [0,0] one, but can you elaborate on what you mean by "increased sterics", "different electrostatics" and the basis for the belief that there is an "increased probability of mismatches"?

Different electrostatics - the structure of labeled nucleotides has changed and with that comes different electronic fields which can influence the physical behaviour of the bases especially with flat, aromatic dyes that can interact with each other, eg pi-stacking, and DNA in a number of ways, eg intercalation. This has introduced new, less understood biases and might significantly impact the chemistry of incorporation

Increased sterics - so in the nextseq kit T and C have single fluorescent labels so unless the types of dyes have changed from the original this shouldn't change their individual incorporation chemistry but may change in relation to the new G and A.

However, A now has two fluorescent dyes and G has no fluorescent dye. This changes their spacial volume significantly with the former now larger and the latter smaller.

In the absence of competitive incorporation ie no A present, G will pair with T. I don't believe that should be news to anyone.

Hence, in the situation created by Illumina having no label on G and two labels on A the competition between A and G for incorporation with T has now been skewed due to steric hindrance toward misincorporation of G with T. It's now more difficult for A to pair with T because it's bigger and easier for G to mispair with T because it's smaller.

These changes have only corrupted the most valuable part of the Illumina system. The sequencing chemistry has been compromised, in the true meaning of the word, so the system can be made cheaper by removal of two lasers and the knock on cost savings with less informatics required.

I feel they've made a fatal error here because they don't understand what they were given by those who did.

[austinso]

Hence, in the situation created by Illumina having no label on G and two labels on A the competition between A and G for incorporation with T has now been skewed due to steric hindrance toward misincorporation of G with T. It's now more difficult for A to pair with T because it's bigger and easier for G to mispair with T because it's smaller.

I'm not necessarily disagreeing with your extrapolations from biophysics (as you peppered with "might" and "may"), but evidence of this should be readily apparent in the data, then...

Just curious from a "should I get v1 or wait until v2" perspective is all...

[seqsense]

I'm not necessarily disagreeing with your extrapolations from biophysics (as you peppered with "might" and "may"), but evidence of this should be readily apparent in the data, then...

Just curious from a "should I get v1 or wait until v2" perspective is all...

There's no biophysics, it's just plain chemistry. It might fall out for resequencing but for de novo it's an issue because it's not predictable. There will be an increase in G:T pairing although invisible due to the lack of dye and it will occur toward the end of the ~150 base pair read length where the synthesised dsDNA terminally impedes incorporation due to structural complexity.

Besides, I wrote this in response to a comment that the nextseq results appear to be inferior to the original chemistry. I can't think of any other reasons why this should be other than the ones I have stated as they appear obvious to me to be the most likely causes.

[nucacidhunter]

I wonder if incorporation of G instead of A during sequencing with NextSeq as proposed by seqsence would explain the observation posted in this tread: http://seqanswers.com/forums/showthr...hlight=nextseq

poly-G in NextSeq
________________________________________
Hi,
I just received NextSeq paired-end results (45 bp 1st read and 40 bp second read) and I noticed (using FastQC) that about 1-2% of the second read is poly-G. I known that G has no "colour" so it probably means that these spots are not detected in the paired run but what is the cause for that? Is it common to get this number of failing paired reads? Have someone ran into this before?
Thanks
By the way, the first read also contains poly-G but for very few reads.

Has anyone observed similar results?

[Brian Bushnell]

This isn't really the place to discuss this (though I did bring it up); perhaps it should be moved to the Illumina forum? But anyway, tomorrow I'll post some of my NextSeq graphs. They have a very badly skewed A/T ratio that gets worse toward the read end. I'm not sure why; I had assumed it was the base caller, but it could be the chemistry. The C/G ratio seems fine.

[Brian Bushnell]

I started a thread here containing an analysis of some of our NextSeq data, with HiSeq 2000 for comparison.

[ymc]

Hi Minion users, do you need to treat the samples with DNase to do viral sequencing? I learned it was necessary to do so with Illumina machines.

Thanks in advance for your reply

[lkral]

If anyone in the MAP can talk about this, how does one prepare samples forMinION cDNA reads? Is it possible to get the entire length of the transcript sequenced or does the cDNA have to be fragmented during the sample prep? Thanks.

[ymc]

Do you guys think ONT can replace Illumina in the quantitation space? I think Illumina's fixed length reads probably is more suitable for quantitation, right?

[robp]

Hi ymc,

Certainly in the short term, I don't think that long read technology will replace Illumnia-style technology for quantification. The problems tackled by long reads are different. It may be great (once we can get the accuracy up) for isoform resolution, but the sheer number of reads is currently too small to be useful for many forms of quantification.

[Brian Bushnell]

I agree that ONT is unlikely to supersede Illumina for quantification in the short term, but in the long term, it's possible. Illumina coverage does not reflect physical coverage, due mainly to its GC bias, but probably others. When you consider PacBio data - it's wonderfully smooth and unbiased, thus capable of accurately quantifying expression. Illumina is not capable of that on an absolute scale, though it should be accurate on a relative scale, between different samples considering the same gene isoform.

If Nanopore data is unbiased, the technology should allow you to fragment and sequence short reads (which are more applicable for quantification of unspliced genes) and get a superior result, compared to sequencing with known unpredictable biases. Currently ONT's error rate appears to be higher toward the beginning of a read, which would reduce the accuracy of short reads. But I think that single-molecule-sequencing is the way forward for absolute quantification or when dealing with alternatively-spliced genes; even if the reads have lower accuracy, as long as you can map them, you can greatly reduce bias and vastly increase your ability to identify isoforms, in one fell swoop.

Note that I am not presently allowed (by JGI) to give nonpositive ONT results. That said, I would like to say that their base-calling accuracy is advancing rapidly, and their read lengths are very impressive, greater than anything I've seen from PacBio. That alone has lead me to suggest using ONT for scaffolding, where it could allow great increases in genome contiguity, particularly in repetitive organisms..

[BBoy]

There's paper in press claiming that using ONT data in combination with Illumina improves assembly quality ten fold.

Reminds me of this. I think at the time it was targeted at PacBio, but the author seems to have changed their mind since then.

http://pathogenomics.bham.ac.uk/blog/2012/02/a-new-sequencing-technology-enters-the-ring-shtseqtm/

[Brian Bushnell]

That was pretty funny

[nickloman]

Reminds me of this. I think at the time it was targeted at PacBio, but the author seems to have changed their mind since then.

http://pathogenomics.bham.ac.uk/blog...ring-shtseqtm/

Actually Neil Hall wrote that, I was just hosting for him. And he changed his mind because he bought a PacBio

[ymc]

Dear all nanopore vets,

I just started looking into nanopore data. I set-up the PBcR pipeline and experimented with the sample data at the PBcR home page. It worked as expected.

The page mentioned that the assembly can be improved using nanopolish, so I downloaded Nick's fast5 and gave it try. Nanopolish took 25x time comparing to the PBcR pipeline. Is that normal? Can it be faster? I was running -P 6 for parallel and -t 6 for nanopolish on my 6-core machine.

I also noticed that two of the nanopolish threads crashed with the same assertion error:
nanopolish: src/hmm/nanopolish_profile_hmm.cpp:143: std::vector<AlignmentState> profile_hmm_align(const string&, const HMMInputData&): Assertion 'get(vm, row, col) != -(__builtin_inff())' failed.

When I tried to run nanopolish_merge.py, I got the following errors:
ERROR_MISSING ctg7180000000001 287
ERROR_MISSING ctg7180000000001 288
ERROR_MISSING ctg7180000000001 289
ERROR_MISSING ctg7180000000001 353
ERROR_MISSING ctg7180000000001 354
ERROR_MISSING ctg7180000000001 355
ERROR_MISSING ctg7180000000001 356
ERROR_MISSING ctg7180000000001 357
ERROR_MISSING ctg7180000000001 358
ERROR_MISSING ctg7180000000001 359

I think this was caused by crashing in two of the threads. Is it possible for me to rerun only crashed parts instead of the whole thing?

Thanks a lot in advance.