7 ms·

Software breakthrough radically boosts the speed of nanopore DNA sequencers

134 points by olvy0 6y ago

chmaynard 6y ago
Link to the GitHub repository: https://github.com/skovaka/UNCALLED https://github.com/skovaka/UNCALLED
deleted 6y ago
[deleted]
jgmmo 6y ago
Note - this is NOT about whole genome sequencing.
- xiphias2 6y ago
  Can it be used instead of PCR Covid test for example?
  - PunchTornado 6y ago
    we already have covidseq by illumina which does this at a pretty big scale and provides survaillence and diagnostics in one test. the issue is that PCR still is the cheapest and fastest way to get the results.
  - myhf 6y ago
    This would be me more applicable to looking for variants of a known gene. For example, measuring mutation rates within known covid-positive samples.
    - ivirshup 6y ago
      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.
      - aroch 6y ago
        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.
        ivirshup 6y ago
        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?
breck 6y ago
I'd suggest the title be "...speed of nanopore DNA sequencers" as this tech doesn't apply to the majority of sequencers. (Not to say it's not exciting: I find nanopore sequencing to be the most exciting thing in the field)
- PunchTornado 6y ago
  to me nanopore seq is not that promising. it has been here for a long time now and the benefits didn't convince a lot of customers to adopt it. there are 2 main use cases now in precision medicine: rare disease and cancer. for both you need high precision reads, which nanopore doesn't provide.
  - anon946 6y ago
    Should the long read lengths allow error correction to work well if there is sufficient coverage?
    - fwip 6y ago
      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 people working on this problem. Increasing coverage will definitely help resolve these errors, but the coverage required may be such that it's more cost effective to use a more traditional sequencer. A homopolymer is when you have stretches of the same nucleotide, and the error is miscounting the number of them. e.g: GAAAC could be called as "GAAC" or "GAAAAC" or even "GAAAAAAAC".
      - dnautics 6y ago
        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.
alextheparrot 6y ago
If anyone is interested in DNA sequencing, I think the WENGAN paper [0] that came out the other day is pretty interesting too. They are using a hybrid input [1] in combination with their software package to reconstruct higher fidelity reference genomes. The big impact from that paper is really summarized by this: > WENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 80.64 Mb (NGA50: 59.59 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50: 57.88 Mb) [0] https://www.nature.com/articles/s41587-020-00747-w https://www.nature.com/articles/s41587-020-00747-w [1] Long and short reads, for example from Nanopore and Illumina, respectively
- wiz21c 6y ago
  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 ...)
  - xyhopguy 6y ago
    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 algorithms for each) you need to resolve a 1D sequence that holds all of those continguous subsequences for each chromosome. Genome assembly is often described as finding a hamiltonian cycle among the data. For short reads, we use debrujin graphs to avoid the hardness of a complete hamiltonian. for long reads, MinHash has become a popular heuristic. But this can be tricky, for instance, in species that isn't haploid -- you actually have two possible genomes that you need to correctly assemble. Sometimes the difference is a base or two, but sometimes its much longer stretches that can appear as large 'bubbles' in the assembly graph. The assembly problem can be harder or easier depending on the organism, for instance, Wheat is notoriously hard to assemble due to the fact that it has 3 mostly (but not completely) distinct genomes. The Norway Spruce is composed of ~80B basepairs, which will put strain on even the biggest machines. Oh and its mostly repeats. Repeats are _everywhere_, and they can be really long (sometimes on the order of millions of nucleotides). Also -- depending on the species youre assembling, the repeat content and the kind of repeats can change. You also can get different errors from the library preparation, again related to the species of origin. Using lab techniques is particularly helpful, as we can leverage molecular and genetic information. BACs (baceterial artificial choromosomes) can be used to break the genome into smaller continguous chunks and to act as 'anchors' for the larger assembly problem. Recombination rates of different SNPs can be used to infer spatially local segments of DNA, and optical tags can be added and imaged to provide physical anchors for certain sequences. Some organisms can be bred into pure lines, with limited heterozygosity. Expressed RNA transcripts can be used in a similar manner, as they are the concatenation of ordered exons. Combining these methods is typically the best way to get a good genome assembly. Some of the bubble resolution heuristics could probably be improved with deep learning, but getting the right data for that is probably more effective with traditional graph algorithms. Also -- you really only need a good genome assembly once, and sometimes it doesnt even need to be all that good. Genome assembly is a _really_ fascinating area of research, and honestly, the right direction is probably to represent genomes as something other than 1D sequence that more accurately reflects biology. Deep learning is still looking to make its mark on genomics, and unfortunately its not well suited for much of the discovery efforts such as genome assembly.
msandford 6y ago
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've heard it called). It feels like genome/genetic things have the same behavior but with a base of something like 5ish years.
- Real_S 6y ago
  The cost of sequencing is dropping much faster than Moore's law. https://www.nature.com/news/technology-the-1-000-genome-1.14901 https://www.nature.com/news/technology-the-1-000-genome-1.14...
  - biotinker 6y ago
    Not really. The cost of sequencing was dropping slowly for a while, then dropped much faster than Moore's Law between 2007 and 2012. It has since dropped much more slowly.
- HPsquared 6y ago
  Something a bit less than Moore's law... how about Lee's law?
- fatboy93 6y ago
  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.
ml_thoughts 6y ago
Link to biorxiv version: https://www.biorxiv.org/content/10.1101/2020.02.03.931923v1 https://www.biorxiv.org/content/10.1101/2020.02.03.931923v1 Summary because the title overstates significance a bit (of a cool paper): Most sequencing today is done using Illumina machines, which basically break DNA into small parts (on the order of ~hundred letters/bases), then use fluorescence/imaging to find sequence. This paper applies to a new technology, nanopore-based sequencing, which pulls much longer pieces of DNA (record length: 2.3 million base pairs, but most end up shorter, on the order of thousands of bases) through a microscopic molecular channel, providing real-time outputs of voltage that can be somewhat noisily mapped to nucleotide sequences (since different DNA sequences have different voltage outputs when run through a channel). This technology is very cool, and the fact that it's real-time opens up an interesting idea: you can potentially give real-time feedback to the sequencer as the run is operating about whether a given piece of DNA it's reading is interesting to you. If it's not, the channel can spit out the piece of DNA and start reading in a new one. So for example, let's say you want to sequence viral sequences present in a human tissue sample. Well, naively, if you just try to collect all the DNA in the sample, most of the DNA is going to be from the human genome (human genome is ~100,000x bigger than viral genomes and likely not all cells are infected). This approach aims to map the DNA as it's being read to some reference (in this case, the human genome) and avoid re-sequencing pieces of DNA mapping to the reference (by getting the channel to spit out the piece of DNA it's reading and wait for a new sequence to come in). Your sequencing results are therefore enriched in the actual sequences of interest. In terms of the contributions of this paper, nanopore sequencing is still a growing area. This real-time aspect has been a key motivation for some time: this paper improves the feasibility of the approach by algorithmic improvements to mapping between real-time voltage readings and reference sequences. This has to be fast to be effective, since DNA is read pretty quickly and in parallel across many channels, and previous methods apparently weren't fast enough to provide real advantages. The caveats are that this only applies to cases where you don't want to read the majority of DNA present (an important use case but not universal), nanopore sequencing still has issues with high error rates which makes it a bit less attractive than Illumina sequencing, and the amount of DNA you can read through nanopore is still less than what you can do with Illumina. So it's a cool step on the way to a future where we can do some really exciting "interactive" real-time sequencing work but it's still a part of a developing technology suite.
fwip 6y ago
This appears to be a potential alternative to selective amplification. https://www.wikiwand.com/en/Polymerase_chain_reaction#/Selective_DNA_isolation https://www.wikiwand.com/en/Polymerase_chain_reaction#/Selec... Instead of pre-processing the DNA by preferentially amplifying the DNA segments of interest (so that 99%+ of what you sequence is stuff you want), this software/hardware combo sequences the first N bases of each molecule, and then moves on to the next molecule if it doesn't match any expected sequences. If you're sequencing molecules over >200bp, being able to eject the molecule in the first ~10bp could be a very big speedup. Our lab already has sub-1 day processing times for this type of analysis, but I believe the traditional sequencer machine we use (an Illumina MiSeq) is likely more expensive and more cumbersome than the portable solution described here.
doonesbury 6y ago
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 amplified in a bacterial host such as Escherichia coli. " Two questions: 1) OK, we cut up the DNA and replicate it. How in the heck do we assemble the pieces together to restore the original ordering? I've got three pieces A,B,C. How do I know it was originally ABC or CBA or BAC etc? I can correctly sequence a piece ... but what I want is the sequence of all base-pairs in their original order. How on earth do they do that? 2) DNA spends most of its time wrapped up around histones. How do does one get it off the histones for access to sequencing? 3) Less interesting: unless we sequence the whole chromosome researches must pick off a piece ... how do we know which piece to pick off and how do we know where it's crammed around the histones?
- lbotos 6y ago
  I'm not an expert, but I think the PCR page will help: https://en.wikipedia.org/wiki/Polymerase_chain_reaction https://en.wikipedia.org/wiki/Polymerase_chain_reaction basically for PCR (enzyme based) it's sort of "unzipped" down the middle and the base pairs match a specific way which is how it's duplicated. The rest I can't answer but I hope this was as fun a read as it was for me the first time. It really is insane to think about.
  - epgui 6y ago
    Biochemist here! The DNA polymerase reaction can only take place when the gene is unwound from the histone in the first place. Therefore the PCR wiki page will not provide you with the answer... The key is the "DNA purification" prep step where you digest the histones with proteases. See here: https://en.wikipedia.org/wiki/DNA_extraction https://en.wikipedia.org/wiki/DNA_extraction
    - dnautics 6y ago

134 points by olvy0 6y ago

chmaynard 6y ago

Link to the GitHub repository: https://github.com/skovaka/UNCALLED https://github.com/skovaka/UNCALLED

deleted 6y ago

[deleted]

jgmmo 6y ago

Note - this is NOT about whole genome sequencing.

xiphias2 6y ago
Can it be used instead of PCR Covid test for example?
- PunchTornado 6y ago
  we already have covidseq by illumina which does this at a pretty big scale and provides survaillence and diagnostics in one test. the issue is that PCR still is the cheapest and fastest way to get the results.
- myhf 6y ago
  This would be me more applicable to looking for variants of a known gene. For example, measuring mutation rates within known covid-positive samples.
  - ivirshup 6y ago
    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.
    - aroch 6y ago
      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.
      - ivirshup 6y ago
        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?

breck 6y ago

I'd suggest the title be "...speed of nanopore DNA sequencers" as this tech doesn't apply to the majority of sequencers. (Not to say it's not exciting: I find nanopore sequencing to be the most exciting thing in the field)

PunchTornado 6y ago
to me nanopore seq is not that promising. it has been here for a long time now and the benefits didn't convince a lot of customers to adopt it. there are 2 main use cases now in precision medicine: rare disease and cancer. for both you need high precision reads, which nanopore doesn't provide.
- anon946 6y ago
  Should the long read lengths allow error correction to work well if there is sufficient coverage?
  - fwip 6y ago
    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 people working on this problem. Increasing coverage will definitely help resolve these errors, but the coverage required may be such that it's more cost effective to use a more traditional sequencer. A homopolymer is when you have stretches of the same nucleotide, and the error is miscounting the number of them. e.g: GAAAC could be called as "GAAC" or "GAAAAC" or even "GAAAAAAAC".
    - dnautics 6y ago
      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.

alextheparrot 6y ago

If anyone is interested in DNA sequencing, I think the WENGAN paper [0] that came out the other day is pretty interesting too. They are using a hybrid input [1] in combination with their software package to reconstruct higher fidelity reference genomes. The big impact from that paper is really summarized by this: > WENGAN assembly of the haploid CHM13 sample achieved a contig NG50 of 80.64 Mb (NGA50: 59.59 Mb), which surpasses the contiguity of the current human reference genome (GRCh38 contig NG50: 57.88 Mb) [0] https://www.nature.com/articles/s41587-020-00747-w https://www.nature.com/articles/s41587-020-00747-w [1] Long and short reads, for example from Nanopore and Illumina, respectively

wiz21c 6y ago
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 ...)
- xyhopguy 6y ago
  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 algorithms for each) you need to resolve a 1D sequence that holds all of those continguous subsequences for each chromosome. Genome assembly is often described as finding a hamiltonian cycle among the data. For short reads, we use debrujin graphs to avoid the hardness of a complete hamiltonian. for long reads, MinHash has become a popular heuristic. But this can be tricky, for instance, in species that isn't haploid -- you actually have two possible genomes that you need to correctly assemble. Sometimes the difference is a base or two, but sometimes its much longer stretches that can appear as large 'bubbles' in the assembly graph. The assembly problem can be harder or easier depending on the organism, for instance, Wheat is notoriously hard to assemble due to the fact that it has 3 mostly (but not completely) distinct genomes. The Norway Spruce is composed of ~80B basepairs, which will put strain on even the biggest machines. Oh and its mostly repeats. Repeats are _everywhere_, and they can be really long (sometimes on the order of millions of nucleotides). Also -- depending on the species youre assembling, the repeat content and the kind of repeats can change. You also can get different errors from the library preparation, again related to the species of origin. Using lab techniques is particularly helpful, as we can leverage molecular and genetic information. BACs (baceterial artificial choromosomes) can be used to break the genome into smaller continguous chunks and to act as 'anchors' for the larger assembly problem. Recombination rates of different SNPs can be used to infer spatially local segments of DNA, and optical tags can be added and imaged to provide physical anchors for certain sequences. Some organisms can be bred into pure lines, with limited heterozygosity. Expressed RNA transcripts can be used in a similar manner, as they are the concatenation of ordered exons. Combining these methods is typically the best way to get a good genome assembly. Some of the bubble resolution heuristics could probably be improved with deep learning, but getting the right data for that is probably more effective with traditional graph algorithms. Also -- you really only need a good genome assembly once, and sometimes it doesnt even need to be all that good. Genome assembly is a _really_ fascinating area of research, and honestly, the right direction is probably to represent genomes as something other than 1D sequence that more accurately reflects biology. Deep learning is still looking to make its mark on genomics, and unfortunately its not well suited for much of the discovery efforts such as genome assembly.

msandford 6y ago

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've heard it called). It feels like genome/genetic things have the same behavior but with a base of something like 5ish years.

Real_S 6y ago
The cost of sequencing is dropping much faster than Moore's law. https://www.nature.com/news/technology-the-1-000-genome-1.14901 https://www.nature.com/news/technology-the-1-000-genome-1.14...
- biotinker 6y ago
  Not really. The cost of sequencing was dropping slowly for a while, then dropped much faster than Moore's Law between 2007 and 2012. It has since dropped much more slowly.
HPsquared 6y ago
Something a bit less than Moore's law... how about Lee's law?
fatboy93 6y ago
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.

ml_thoughts 6y ago

Link to biorxiv version: https://www.biorxiv.org/content/10.1101/2020.02.03.931923v1 https://www.biorxiv.org/content/10.1101/2020.02.03.931923v1 Summary because the title overstates significance a bit (of a cool paper): Most sequencing today is done using Illumina machines, which basically break DNA into small parts (on the order of ~hundred letters/bases), then use fluorescence/imaging to find sequence. This paper applies to a new technology, nanopore-based sequencing, which pulls much longer pieces of DNA (record length: 2.3 million base pairs, but most end up shorter, on the order of thousands of bases) through a microscopic molecular channel, providing real-time outputs of voltage that can be somewhat noisily mapped to nucleotide sequences (since different DNA sequences have different voltage outputs when run through a channel). This technology is very cool, and the fact that it's real-time opens up an interesting idea: you can potentially give real-time feedback to the sequencer as the run is operating about whether a given piece of DNA it's reading is interesting to you. If it's not, the channel can spit out the piece of DNA and start reading in a new one. So for example, let's say you want to sequence viral sequences present in a human tissue sample. Well, naively, if you just try to collect all the DNA in the sample, most of the DNA is going to be from the human genome (human genome is ~100,000x bigger than viral genomes and likely not all cells are infected). This approach aims to map the DNA as it's being read to some reference (in this case, the human genome) and avoid re-sequencing pieces of DNA mapping to the reference (by getting the channel to spit out the piece of DNA it's reading and wait for a new sequence to come in). Your sequencing results are therefore enriched in the actual sequences of interest. In terms of the contributions of this paper, nanopore sequencing is still a growing area. This real-time aspect has been a key motivation for some time: this paper improves the feasibility of the approach by algorithmic improvements to mapping between real-time voltage readings and reference sequences. This has to be fast to be effective, since DNA is read pretty quickly and in parallel across many channels, and previous methods apparently weren't fast enough to provide real advantages. The caveats are that this only applies to cases where you don't want to read the majority of DNA present (an important use case but not universal), nanopore sequencing still has issues with high error rates which makes it a bit less attractive than Illumina sequencing, and the amount of DNA you can read through nanopore is still less than what you can do with Illumina. So it's a cool step on the way to a future where we can do some really exciting "interactive" real-time sequencing work but it's still a part of a developing technology suite.

fwip 6y ago

This appears to be a potential alternative to selective amplification. https://www.wikiwand.com/en/Polymerase_chain_reaction#/Selective_DNA_isolation https://www.wikiwand.com/en/Polymerase_chain_reaction#/Selec... Instead of pre-processing the DNA by preferentially amplifying the DNA segments of interest (so that 99%+ of what you sequence is stuff you want), this software/hardware combo sequences the first N bases of each molecule, and then moves on to the next molecule if it doesn't match any expected sequences. If you're sequencing molecules over >200bp, being able to eject the molecule in the first ~10bp could be a very big speedup. Our lab already has sub-1 day processing times for this type of analysis, but I believe the traditional sequencer machine we use (an Illumina MiSeq) is likely more expensive and more cumbersome than the portable solution described here.

doonesbury 6y ago

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 amplified in a bacterial host such as Escherichia coli. " Two questions: 1) OK, we cut up the DNA and replicate it. How in the heck do we assemble the pieces together to restore the original ordering? I've got three pieces A,B,C. How do I know it was originally ABC or CBA or BAC etc? I can correctly sequence a piece ... but what I want is the sequence of all base-pairs in their original order. How on earth do they do that? 2) DNA spends most of its time wrapped up around histones. How do does one get it off the histones for access to sequencing? 3) Less interesting: unless we sequence the whole chromosome researches must pick off a piece ... how do we know which piece to pick off and how do we know where it's crammed around the histones?

lbotos 6y ago
I'm not an expert, but I think the PCR page will help: https://en.wikipedia.org/wiki/Polymerase_chain_reaction https://en.wikipedia.org/wiki/Polymerase_chain_reaction basically for PCR (enzyme based) it's sort of "unzipped" down the middle and the base pairs match a specific way which is how it's duplicated. The rest I can't answer but I hope this was as fun a read as it was for me the first time. It really is insane to think about.
- epgui 6y ago
  Biochemist here! The DNA polymerase reaction can only take place when the gene is unwound from the histone in the first place. Therefore the PCR wiki page will not provide you with the answer... The key is the "DNA purification" prep step where you digest the histones with proteases. See here: https://en.wikipedia.org/wiki/DNA_extraction https://en.wikipedia.org/wiki/DNA_extraction
  - dnautics 6y ago