41–50 of 96 posts
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I expect us to use the long reads from ONT and PacBio to discover that structural variation is even more important and common than previously appreciated. If that's the case, you'd be silly to use the Illumina technology for anything more complex than population genetics, and this will probably trickle into many parts of the market as the medically-relevant parts of the field realize what the basic science is demonst…
The fact is, unfortunately, that Nanopore sequencing (and also, from what I’ve read, PacBio) has a dramatically higher error rate than Illumina sequencing-by-synthesis. In the near future, anyway, I would expect to see inaccurate PacBio/Nanopore long reads being used as scaffolds for accurate Illumina short reads (in fact, this is already happening). Illumina won’t be going anywhere any time soon.
This is true, but only for Insertions/Deletions. The substitution error rate is comparable to Hi-Seq.
So it's good for resequencing but without a reference or decent scaffolds, you're in the dark.
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Side note: your comment is one of the few times I've seen the code tag used for actual code on HN, rather than quotations or just indentation.
True, and unfortunately it does not render it properly, it‘s cut after 20 chars or so
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How many sequencers has Oxford Nanopore sold? It seems like it has perennially been a “in the next few years” technology.
I'm not sure how many they've actually sold but i do know that they have a loan/test program where you can pay a relatively small amount (around $2000 or less) to get set up with a MinION kit. The main recurring cost is the flow cell. I'm currently trying to figure out how to join together short multiplexed samples (150bp - 200bp) and get it sequenced on the MinION. Current short-read sequencers from Illumina or Ther…
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The was flag killed. I vouched for this because I had the same question. What does it mean to have sequenced the human genome? If different individuals have a different genome, what is the human genome?
Not only do different individuals have different genomes, each cell from the same person will have different genomes. It is more like an "average" human genome, one that no actual cell has ever contained.
Did you mean each cell will have different gene expression ?
I thought all the cells have the same nucleotide sequences barring maybe occasional mutations (like in cancer)
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https://youtu.be/fCd6B5HRaZ8 is the best visualization of how the most popular type of DNA sequencer works (that I've found). Imagine you have a string of length 3 billion made by randomly choosing from 4 characters. Like this dna = ''.join(random.choices('atgc', weights=[30.9, 29.4, 19.9, 19.8], k=3_234_830_000)) you get to randomly sample 1 billion[3, page 7] overlapping substrings of length 200[3, page 7] with .1%…
Side note: your comment is one of the few times I've seen the code tag used for actual code on HN, rather than quotations or just indentation.
This is inaccurate, though the fault is with the author, not Lander. When you have genomic repeats, the appropriate analogy is if you had multiple puzzle pieces that had the exact same edges on all sides.
Puzzle pieces that show the same blue sky but with different edges can still be uniquely assembled. But puzzle pieces with identical edges create non-unique but equally satisfactory assembly solutions, and this is the case with the problem of genomic assembly.
As usual, the journalist spends many paragraphs painting a picture of human conflict before actually getting on to the interesting claim. > The reason for these gaps is that DNA sequencing machines don’t read genomes like humans read books, from the first word to the last. Instead, they first randomly chop up copies of the 23 pairs of chromosomes, which total some 3 billion “letters,” so the machines aren’t overwhelm…
The other thing this article doesnt mention is the idea of coverage. Its not like a jigsaw puzzle with one copy of each tile-- every time a whole genome is sequenced it is done so 30 times over to ensure that the pieces are correct.
Finally, Read length varies from technology to technology. This one claims average 10k base pairs.
http://www.pacb.com/smrt-science/smrt-sequencing/read-length...
As usual, the journalist spends many paragraphs painting a picture of human conflict before actually getting on to the interesting claim. > The reason for these gaps is that DNA sequencing machines don’t read genomes like humans read books, from the first word to the last. Instead, they first randomly chop up copies of the 23 pairs of chromosomes, which total some 3 billion “letters,” so the machines aren’t overwhelm…
https://youtu.be/fCd6B5HRaZ8 is the best visualization of how the most popular type of DNA sequencer works (that I've found). Imagine you have a string of length 3 billion made by randomly choosing from 4 characters. Like this dna = ''.join(random.choices('atgc', weights=[30.9, 29.4, 19.9, 19.8], k=3_234_830_000)) you get to randomly sample 1 billion[3, page 7] overlapping substrings of length 200[3, page 7] with .1%…
It's strange that this article ends as an advertisement for PacBio sequencing (which can ~50k-60k base reads) but makes no mention of Oxford Nanopore (which has gotten megabase reads and keeps improving). Single molecule nanopore sequencing is on track to sequence across the centromeres of human chromosomes in the next few years.
The problem is Oxford Nanopore data has about a 30% insertion and deletion error rate. While PacBio is less than 1% indel and substitution error. Don't get me wrong, the Minion is amazing, but it can't compare to PacBio in terms of quality... yet.