Sequencing your DNA with a USB dongle and open source code (opens in new tab)

In Spain, for example, we have a private system but it is extremely inefficient in some areas (and very good in others). Of course, you can have private insurance, but you still have to pay your social security. Curiously, the only ones who can decide which system they want are the public servants...

You cannot really avoid the fundamental constraints - anywhere in the world, there are only so many doctors and so much money available for treatments. IDK if USA has a shortage of doctors, but plenty of European countries do. A country like Romania just cannot give its doctors big enough wages to stop them from seeking employment elsewhere, where they will get five to ten times as much (UK, Germany, Switzerland). As a result, local hospitals are seriously understaffed.

Where I live, having personal connections to good doctors gives you an advantage - you will be examined and treated faster. Then there is outright nepotism.

The outgroups are different than in America, but there are always people for whom the system sucks.

Gattaca shows eugenics has been so vilified that the audience will root for a character who selfishly commits fraud, risking lives and scientific progress for his own vanity.

The really scary fact is that there would be no need for a police state and segregation. The genetically enhanced would just completely dominate an open and fair competition.

https://youtube.com/c/thethoughtemporium

He’s got a ton of other interesting projects, the DIY gene therapy is just one that stands out because it seems so risky.

If someone steals my DNA I can't stop them. But I can at least avoid being swept up in large scale DNA scanning and tracking efforts.

Edit: I used to help Google fund researchers like Joe Derisi and others who develop technology to do this, and some of the people I worked with in my academic career are quite good at identifying serial killers from 30 year old DNA. If you're downvoting because you think I'm making this up, you're wrong. If you're downvoting because you don't think large-scale individual detection using genetic sampling of the environment is possible, you're wrong. If you're downvoting because you think you couldn't do a whole genome sequence of an individual using a sample collected in the wild, you're wrong. If you're downvoting because you think this is a terrible idea (morally, ethically), that's fine but I didn't say anything about my own moral or ethical beliefs about this.

It's simply factually correct to say that large-scale individual sample collection (at order tens of thousands, if not hundreds of thousands of individuals in a country the size of the US) is possible. All the technology is there to do this.

The results have been pretty astounding. I found markers that pointed to poor response to a specific blood thinner my grandfather was put on before he passed. Currently I'm researching the cluster of Bipolar / ADHD / SAD symptoms I experience that all seem to trace back to a certain genotype of circadian rhythm genes I have (thank you, Sci Hub). To boot, some of the studies I've come across have been done on Han Chinese populations that match my descendance.

Perhaps going too far down this rabbit hole poses a self-diagnosis risk, but the correlations to my family history and my own life experience working with doctors to diagnose and treat symptoms are pretty undeniable. And given that your run-of-the-mill psychiatrist is going to treat you off of a DSM checklist, I feel much more confident knowing there have been genomic studies to back things up, since my doctor isn't up to date on this research, and finding one that would be will be difficult and expensive. I've shared the papers with my doc and he's been supportive, sometimes I feel like I should be getting a discount on services rendered.

You also still, in that case, need a gelbox + ladder + loading dye + sybrsafe or whatever, so it’s still not nothing.

when i worked on https://github.com/iontorrent/tmap we thought it would be a good idea to do something like a “local alignment” (using https://en.wikipedia.org/wiki/Smith–Waterman_algorithm) after doing a lookup into a burrows wheeler transform on a substring of the “read.”

https://genomebiology.biomedcentral.com/articles/10.1186/s13...

I guess there are limits to ensemble methods if the underlying accuracy doesn't increase. I don't work on gene sequencing algorithms but from what I understand of ML ensemble techniques, there are certain assumptions regarding the underlying independence of the errors. The errors for nanopore should be uniform but I am not sure. Any molecular biologist here care to comment?

There are two components that drive sequencing error rate. 1) The chemistry behind the sequencing (for nanopore sequencing this is the "feeding DNA through a pore" bit) 2) the method to convert raw signal into DNA sequence (this is called "base calling").

The gold-standard in terms of error profile for sequencing is currently the Illumina short read platform. Illumina machines are really just microscopes (TIRF scopes for optics folks) that sequence DNA by visualizing incorporation of dye-labeled nucleotides into the sequenced molecule(s) (Imagine a really slow PCR [1]). Each base is labeled with a different color, then when a molecule has a match it makes a colored spot on the slide that the machine can read (see here for more info & details of newer chemistry that use fewer colors [2]). This whole process is mediated by DNA polymerase which itself has a very low error rate. Another important point is that DNA sequenced on the illumina platform (called a "library") tends to be from "amplified" template DNA, meaning the DNA will have been processed and potentially be missing chemical modifications on the bases that could be present in the organism. This works to Illumina's advantage, because when trying to answer the question of "what is the DNA sequence?" we want the ground-truth DNA, not the modification state.

In contrast, Nanopore sequencing works by feeding a long strand of DNA through a pore and measuring the change in electrical current through the pore (watch the cool video [3]). For the current set of nanopore flowcells, 8 bases of DNA sit in the pore at a time, meaning the current at each timestep is a product of 8 nucleotides in aggregate. This also means that the pore "sees" each base 8 times, but always in the context of an additional 7. In order to basecall from the raw signal, it's not as easy as saying "blue = A", instead, you have to deconvolve each base from a complex signal. As you might imagine, the folks at Oxford Nanopore & broader research community have turned to machine learning-based base callers to solve this problem, and they work quite well [4]. But they are not perfect. Deconvolving runs of the same base (e.g. "AAAAAAA") is difficult because without well-defined signal changes between bases, the caller has a hard time deciding how many bases it has seen, so a common error mode for nanopore sequencing is to create insertions/deletions at places in the genome with low nucleotide diversity. Another interesting reason is that most Nanopore library preps are often performed on unamplified DNA, and so in addition to normal A/T/G/C nucleotides, the template DNA can also contain bases with chemical modifications. For example, in bacteria, A's are often methylated, and in Humans, C can have all kinds of different modifications (5-methyl-cytosine, 5-hydroxymethyl-cytosine, etc. etc.) and each different modification affects the signal in the nanopore. Therefore, basecallers that weren't trained on modified bases will produce basecalling errors in the presence of base modifications.

For both Illumina and Nanopore basecallers, they assign a quality score to each base that indicates the probability that the basecaller produced an incorrect value. This is called a Q-score, which is defined as "Q = -10(log10(P-value))" (i.e. Q / 10 = the order of magnitude of the error probability) [5]. For example, a Q-score of 10 means an error rate of 1 in 10, but a Q-score of 50 means an error rate of 1 in 100,000. For Illumina sequencing, >95% of the reads have a Q-score > 30 (i.e. 1 in 1000 errors), while Nanopore reads tend to have lower average Q-scores (~Q20, i.e. 1 in 100 errors). For genetics, where 1 base difference can mean the difference between a severe disease allele vs a normal variant, 1 in 100 won't cut it.

The current gen Nanopore flowcell chemistry (R9.4.1) is what most people are talking about when they talk about Nanopore error rates, but they've just released a new pore type & made some basecaller upgrades that improve the accuracy to what they call "Q20+" and some claims of Q>30, and from the data I've seen, it's impressive, I just haven't got my hands on one yet to see for myself [6]. I think the comment saying "wait 5 years" is an overestimate, but if you want to genotype yourself today, I'd just pay someone for Illumina sequencing and process the fastq files yourself if you really want to do it as a learning exercise.

I've unintentionally written an essay, so I'll stop here, but real quick to your other point RE: rerunning the sample N times & using the repeats for error correction. This won't work the way you're thinking because a "sample" is actually a collection of DNA molecules that are sampled randomly by the sequencer. You have no way of knowing that the same read between runs was actually from the same molecule, so you can't error correct this way. Consequently, a totally different sequencing platform from Pacific Biosciences uses this strategy by doing some really cool chemistry, but I'll spare you the second essay (google "PacBio HiFi" or "circular consensus reads" if you're interested).

[1] https://en.wikipedia.org/wiki/Polymerase_chain_reaction

[2] https://www.ecseq.com/support/ngs/do-you-have-two-colors-or-...

[3] https://www.youtube.com/watch?v=RcP85JHLmnI

[4] This paper is a tad out of date, but Ryan Wick always writes extremely clear papers: https://genomebiology.biomedcentral.com/articles/10.1186/s13...

[5] https://www.illumina.com/documents/products/technotes/techno...

[6] https://nanoporetech.com/about-us/news/oxford-nanopore-tech-...

Edit: reformatted links for clarity.

Is this also true for nanopores in protein sequencing? This HN comment from a few weeks back [1] pointed out recent progress but perhaps the tech is still not quite there.

[1]: https://news.ycombinator.com/item?id=29481075

But this could enable things like finding relatives which is what I got out of the comment about 23andme. Instead of all the data being centralized, storage and comparison could be distributed

Not sure what you are concerned about. What would you expect a bad actor to do with your DNA sequences? I'm genuinely curious.

We could even do that, without knowing anything about DNA at all. Or predict tomorrows weather without satellites and computers.

I think you are a bit too enthusiastic about statistics, or too naive about complexity.

It's unlikely even if we improved computing hardware many orders of magnitude beyond all reasonable predictions, that the calculations would be able to simulate all the necessary details; most of our simulations now are based on many approximations due to hardware limitations.

As to the question of "what level of fidelity is required to turn a FASTQ of somebody's genome into an accurate model of the resulting human, with some sort of realistic environment also provided", that's so far beyond what is even remotely comprehensible it's not worth speculating about in terms of science fact; it's just fiction.