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Per the request here, it seems time to create this forum! I'm really excited to see where this data goes and when I can get my hands on a MinION!
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More like "get my hand on a MinION"Originally posted by ECO View Post
They're tiny!
That said, it's not entirely clear to me what users are allowed to discuss about results, though I will describe my methodology for evaluating it. I used both the 1D and 2D reads (converted to fastq), and mapped with this command line:
Then I pasted the histograms into Excel and examined their scatterplots. This command breaks reads over 1kbp into 1kbp pieces and maps them independently; you can set this higher (up to 6kbp) but the mapping rate drops as the shred length increases. The output is in the same order as the input, so you can determine mapped read length by counting the number of consecutive sam lines with the same read name (the pieces get a name suffix of _1, _2, etc) that map to consecutive genomic positions.Code:mapPacBio.sh -Xmx30g k=7 in=reads.fastq ref=reference.fa maxreadlen=1000 minlen=200 idtag ow int=f qin=33 mhist=mhist1.txt idhist=idhist1.txt ehist=ehist1.txt indelhist=indelhist1.txt lhist=lhist1.txt gchist=gchist1.txt qhist=qhist1.txt qahist=qahist1.txt bhist=bhist1.txt out=mapped1.sam minratio=0.15 ignorequality slow ordered maxindel1=40 maxindel2=400 nodisk bs=bs1.sh
If you run the resulting "bs1.sh" bash shellscript, and have samtools installed, it will turn the sam output into a sorted, indexed bam file ready for IGV.Last edited by Brian Bushnell; 09-25-2014, 05:44 PM. -
And can you state on why it is dropping? To many errors in the alignment? Breaking the reads into small fragments sounds like one step backwards to meOriginally posted by Brian Bushnell View Post
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So is this an one-off observation?
Can people chime in (with non-specific comments, if they can't talk about specifics) if their experience is in-line/unlike the paper above?Comment
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By the time people post results the data is already obsolete. The protocols and software change every week if not sooner.Comment
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@NextGenSeq: Are you implying that data from one week can't be trusted the next
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No it is improving every week.
If I were PacBio I would be worried.Comment
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The Nanopore reads I've seen have a sort of 'bistable' error model - lower for a while, then higher for a while, then lower for a while, etc. The higher-error mode is harder to map. Breaking the reads into pieces allows mapping the lower-error-mode pieces and discarding the higher-error-mode pieces; the shorter the piece, the more likely it will be entirely within a lower-error-mode region.Originally posted by WhatsOEver View PostAnd can you state on why it is dropping? To many errors in the alignment? Breaking the reads into small fragments sounds like one step backwards to meComment
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About a month back, one of my collaborators asked me to check about Oxford Nanopore, because she was planning to do a large sequencing project with Illumina+Pacbio, and wanted to know whether waiting for ONT would save her money. She heard good things from another colleague about the portability of Minions and was curious. I am not involved in the early access program and looked for any information available publicly. Based on what I found, I believe the company is advised by incompetent scientists, who are getting the company bad reputation.
My personal background - I have been working on nanotechnology since 1993, wrote the first (and highly cited) paper on calculating electrical current through small organic molecules in 1995 and worked with the NASA Nanotech group for several years in early 2000 before moving on to genomics. At NASA, one my closest collaborator worked on nanopore sequencing and another one worked on computational modeling current flow through the pore. However, I was never directly involved in either of those projects and the main reason being signal quality from the pores. So, the first thing I wanted to find out about ONT is the error rate, because the electrical signal from molecules moving at room temperature tends to get noisy. This is basic quantum (and statistical) physics, which no amount of technology can overcome.
The error rate is very important in deciding about assembly projects. It is definitely possible to do assembly from long erroneous reads, but you will need more reads and that means your costs go up. At the end of the day, my collaborator is interested in comparative costs between various technologies.
I tried to find a straight answer for over a month and could not. For example, Michael Schatz, who is involved in early access program, posted a figure showing 'assembly from nanopore' in twitter. When I asked him about the error rate, he gave some philosophical answer - 'I do not care, because assembly is possible, as long as there is more signal than noise'. WTF? Based on his slides from a recent conference (see here), he had the numbers, but decided to stonewall. Then I learned that the assembly was done with nanopore+ILMN (hybrid), whereas PacBio assemblies are done with PacBio only. Neither did I get a straight answer about error rate from Nick Loman - another scientist working closely with ONT CEO to release data. Those frustrations led me to write this blog post about the company -
The situation seems to have improved somewhat after the company allowed Nick Loman to release his data (check our blog for link), and Michael Schatz posted his slides with the kind of information one needs to make decisions -
Hopefully, others will take a look at the data and come up with an objective answer regarding what is possible and not possible. The technology has promises, but error rate is a critical concern.Comment
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That is possibly due to the molecule moving through the pore at different speed, and the HMM (Viterbi) calculation for base-calling being fixed at one mode and miscalling in the other mode.Originally posted by Brian Bushnell View Post
This thing is definitely a physicist's paradise and would give rise to interesting physics papers, similar to what we used to do on current transport during early 1990s.Comment
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There's paper in press claiming that using ONT data in combination with Illumina improves assembly quality ten fold.Originally posted by samanta View PostAbout a month back, one of my collaborators asked me to check about Oxford Nanopore, because she was planning to do a large sequencing project with Illumina+Pacbio, and wanted to know whether waiting for ONT would save her money. She heard good things from another colleague about the portability of Minions and was curious. I am not involved in the early access program and looked for any information available publicly. Based on what I found, I believe the company is advised by incompetent scientists, who are getting the company bad reputation.
My personal background - I have been working on nanotechnology since 1993, wrote the first (and highly cited) paper on calculating electrical current through small organic molecules in 1995 and worked with the NASA Nanotech group for several years in early 2000 before moving on to genomics. At NASA, one my closest collaborator worked on nanopore sequencing and another one worked on computational modeling current flow through the pore. However, I was never directly involved in either of those projects and the main reason being signal quality from the pores. So, the first thing I wanted to find out about ONT is the error rate, because the electrical signal from molecules moving at room temperature tends to get noisy. This is basic quantum (and statistical) physics, which no amount of technology can overcome.
The error rate is very important in deciding about assembly projects. It is definitely possible to do assembly from long erroneous reads, but you will need more reads and that means your costs go up. At the end of the day, my collaborator is interested in comparative costs between various technologies.
I tried to find a straight answer for over a month and could not. For example, Michael Schatz, who is involved in early access program, posted a figure showing 'assembly from nanopore' in twitter. When I asked him about the error rate, he gave some philosophical answer - 'I do not care, because assembly is possible, as long as there is more signal than noise'. WTF? Based on his slides from a recent conference (see here), he had the numbers, but decided to stonewall. Then I learned that the assembly was done with nanopore+ILMN (hybrid), whereas PacBio assemblies are done with PacBio only. Neither did I get a straight answer about error rate from Nick Loman - another scientist working closely with ONT CEO to release data. Those frustrations led me to write this blog post about the company -
The situation seems to have improved somewhat after the company allowed Nick Loman to release his data (check our blog for link), and Michael Schatz posted his slides with the kind of information one needs to make decisions -
Hopefully, others will take a look at the data and come up with an objective answer regarding what is possible and not possible. The technology has promises, but error rate is a critical concern.Comment
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ten fold compared to what?
Check page 13 of Michael Schatz's slides I posted here.
Illumina alone - N50=59Kb
Illumina + Nanopore - N50=362kbp
Illumina + Pacbio - N50=811kbp
So, my collaborator will lose by going from Pacbio to Nanopore. Moreover, the promise of carrying USB stick to the field does not hold, if she has to also carry a 90Kg Illumina machine.Comment
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The binary error mode is something I would expect from HMM basecalling. Not all levels have the same clear differentiation. At difficult regions, once you got a base wrong, all the following bases have to be consistent to be wrong also. So you get a string of very wrong calls, only to recover later back to consistent correct calls.Originally posted by samanta View Post
Systematic error is likely to be troubling.Comment
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Versus a 2 ton PacBio instrument?Originally posted by samanta View Postten fold compared to what?
Check page 13 of Michael Schatz's slides I posted here.
Illumina alone - N50=59Kb
Illumina + Nanopore - N50=362kbp
Illumina + Pacbio - N50=811kbp
So, my collaborator will lose by going from Pacbio to Nanopore. Moreover, the promise of carrying USB stick to the field does not hold, if she has to also carry a 90Kg Illumina machine.
Anyway read the paper when it comes out. I can't post further info about it.
There is data showing over 99% accuracy of ONT data aligned to reference genomes which is not yet publicly available.Comment
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I also think an algorithm for dealing with this would give rise to a very interesting CS paper. I'd be willing to bet that changes in molecular speed affect the resulting signal in detectable ways, and that modifying the underlying HMM to account for this is possible. ONP base-calling definitely seems like an interesting computational problem.Originally posted by samanta View PostComment
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Data variability is still an issue in sequencing technologies despite the advances in reproducibility and accuracy of these platforms. But the problem does not originate in the sequencing itself, but in the previous steps, before the sample reaches the sequencer.
The first step is collection, followed by preservation and sample preparation for analysis. Most scientists overlook those steps, but not being careful might just be skewing the experiment’s results.
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With the launch of new single-cell sequencing platforms in 2026, the field stands at an exciting inflection point. This article surveys the most impactful advances in the field and discusses how they’re reshaping research in cancer, immunology, and beyond.
Introduction
Single-cell sequencing technologies have undergone remarkable advances over the past decade, transitioning from low-throughput experimental approaches to highly scalable platforms capable of...05-22-2026, 06:42 AM
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