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Software breakthrough radically boosts the speed of nanopore DNA sequencers

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Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#51

Post by fwip »

dnautics wrote: post #44
fwip wrote: post #16

Earlier quoted context omitted.

Nanopore/pyrosequencing technology is interesting in that the class of errors that it is most susceptible to (homopolymer inaccuracies ) nearly do not exist in more traditional base-by-base sequencing. These have proven harder to correct than simple substitution errors - this is both a fault of the bias of existing tooling, and also a difficult problem in general. Roche and other companies have had a lot of smart peo…

What? Have you ever looked at a sanger trace with homopolymer stretches? Depending on how blotchy it is, After about 7 you might not really be sure, and it definitely gets the n wrong occasionally even with nicely resolved peaks. I'm not defending nanopores here, frankly I'm not convinced about them yet.

Sorry, I didn't mean Sanger sequencing, was comparing to Illumina sequencing.

Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#52

Post by fatboy93 »

msandford wrote: post #12

Michael Schatz has been involved in a number of interesting computational biology projects. I worked in a bioinformatics lab ~10-12 years ago and I remember using his fast short read aligner to help speed things up. Back then the nanopore devices were still just rumors. Computers have Moore's law with a base or time constant of 12-24mo. Batteries seem to have the same with a base of about 10 years (slow Moore's law I…

Mike Schatz is awesome, and is genuinely one of the most amazing people.

I've met him on a couple of occasions and it was always amazing that he takes time off to listen to whatever ideas you've got and tries to debate them regarding their feasibility.

Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#53

Post by doonesbury »

epgui wrote: post #32
doonesbury wrote: post #30

I would be eternally grateful to a bio-chem person to explain two facets of DNA sequencing I've never understood. To motivate the question a short quote from Wikipedia: "For longer targets such as chromosomes, common approaches consist of cutting (with restriction enzymes) or shearing (with mechanical forces) large DNA fragments into shorter DNA fragments. The fragmented DNA may then be cloned into a DNA vector and a…

1. The key to this is understanding that the reads or sequences are long, and the slicing happens at somewhat random locations, such that your reads all overlap with each other (of course in reality it's more complicated than that, due to a bunch of long non-coding sequences and repeating sequences, but this will be mostly true for interesting parts of the genome). Therefore your mental model of A, B, C is not partic…

>understanding that the reads or sequences are long

So why slice and replicate slices? All that makes more noise. The wikipedia seems to suggest (or I infer) that one can't merely "just read of" the base pairs in QWERTYUIOP through some super cool process and be done with it. No, I have to split QWERTYUIOP into chunks and replicate the hell out of the resulting chunks which just re-asks my question. I mean how is QWERTYUIOPQWERTYUIOP not a reasonable outcome?

I'm still missing something fundamental. The more chunks there are, the more permutations, combinations there are in possible re-assembly up to all the power sets of base pairs. Granted, it may not be possible to make complex simple here. So that thank you for your time and effort.

Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#54

Post by ivirshup »

aroch wrote: post #26
ivirshup wrote: post #22

Earlier quoted context omitted.

Nanopore isn’t great for looking at generic mutation rates (many will be single nucleotide polymorphisms) due to the high error rate. It’s much better at looking for splicing patterns and epigenetic modifications. Splicing patterns could conceivably change due to mutation, but that’d be a pretty dramatic mutation.

This isn’t the case anymore. Pathogen surveillance labs use Nanopore for single-base resolution variant calls to determine antibiotic susceptibility and to “fingerprint” against known and previously sequences isolates.

Could you share some links on this? I’ve heard talks on using Nanopore for pathogen surveillance, but they were mostly about the ability to spit out reads once you knew what they were. Also, I sit (depending on restriction level) next to a nanopore lab, and they’re pretty consistent about nanopore not being good for single base resolution.

Maybe this could be a case with a lot of amplification, so you have many squiggles to infer a consensus from?

Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#55

Post by jakobnissen »

doonesbury wrote: post #53
epgui wrote: post #32

Earlier quoted context omitted.

1. The key to this is understanding that the reads or sequences are long, and the slicing happens at somewhat random locations, such that your reads all overlap with each other (of course in reality it's more complicated than that, due to a bunch of long non-coding sequences and repeating sequences, but this will be mostly true for interesting parts of the genome). Therefore your mental model of A, B, C is not partic…

>understanding that the reads or sequences are long So why slice and replicate slices? All that makes more noise. The wikipedia seems to suggest (or I infer) that one can't merely "just read of" the base pairs in QWERTYUIOP through some super cool process and be done with it. No, I have to split QWERTYUIOP into chunks and replicate the hell out of the resulting chunks which just re-asks my question. I mean how is QWE…

Indeed you are right, that would be much easier. DNA assembly is an insanely hard computational problem. The issue there is that it's difficult to actually build a sequencing machine that can sequence more than a few hundred base pairs before it stops.

Why that is hard depends on the approach the machine takes to sequencing. With the "sequence by synthesis" approach, the problem is that you need one or two chemical reactions per base, and any yield much lower than 100% will quickly degrade the product after a few hundred cycles.

Nanopore uses a different approach and can indeed produce very long reads, with the tail of the distribution being tens of thousands of base pairs. Not sure what the bottleneck for the length is there.

Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#56

Post by jakobnissen »

xyhopguy wrote: post #17
wiz21c wrote: post #8

Earlier quoted context omitted.

until with have AlphaGenome (they'just beaten some protein folding problem by a laarge margin). (I didn't look in the specifics of the problem so it may be completely wrong, but, you know, these days ...)

theyre not really similar problem, genome assembly is a problem that is most effectively solved in the lab and with traditional graph algorithms. protein folding fits very nicely into structured prediction and has a very quantitative way of measuring performance. Genome assembly is more qualitative, to say the least. given a bunch of reads of short(50-300bp) or long (1000-100,000 bp) lengths (we use different algorit…

What a great write-up. I used to work in metagenomics, where we would assemble environments with hundreds of bacterial genomes. We developed a deep-learning approach to binning the assembly, Vamb, which worked really well. In hindsight, our approach to deep learning was quite naive, so I can easily see more skilled application of DL completely outclass all existing approaches to binning. So while DL indeed would have a hard time with assembly, I'm not sure that's the case for genomics as such.

Re: Software breakthrough radically boosts the speed of nanopore DNA sequencers

#57

Post by epgui »

doonesbury wrote: post #53
epgui wrote: post #32

Earlier quoted context omitted.

1. The key to this is understanding that the reads or sequences are long, and the slicing happens at somewhat random locations, such that your reads all overlap with each other (of course in reality it's more complicated than that, due to a bunch of long non-coding sequences and repeating sequences, but this will be mostly true for interesting parts of the genome). Therefore your mental model of A, B, C is not partic…

>understanding that the reads or sequences are long So why slice and replicate slices? All that makes more noise. The wikipedia seems to suggest (or I infer) that one can't merely "just read of" the base pairs in QWERTYUIOP through some super cool process and be done with it. No, I have to split QWERTYUIOP into chunks and replicate the hell out of the resulting chunks which just re-asks my question. I mean how is QWE…

Again, the key is understanding that the reads or sequences are long, hahaha. For reads, the length of the sequences are on the order of 100-1000bp in length. This is not captured by the QWERTYUIOP example.

It's also important to understand that it's impossible to obtain an error-free sequence (I am ignoring nanopore sequencing, which works differently, for the purposes of this comment), and that the assembly of all these reads is a game of probabilities.

The DNA fragment sequences are even longer than the reads.

Nobody said this was easy! Hehe.

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