I agree with you. Long reads lead to new insights and over time to better diagnoses by providing better understanding of large(r) scale aberrations, and as the tech gets better will be able to do so more easily. But is really not there yet. It’s mostly research and somehow it’s not really improving as much as hoped, I get the feeling.

Can you trust it though? It'd be trivially easy to do a 1x read, maybe 2x, and then fake the other 28 reads. And it'd be hard to catch someone doing this without doing another 30x read from someone you trust. There's famously a lot of cheating in medical research, it would be odd if everyone stopped the moment they left academia (there have been scandals with forensic labs cheating too, now that I think about it).

They save money by cheap labour and batching large quantities for analysis. For the consumer this means long wait times and potentially expired DNA samples.

I tried two samples with Nebula, waited 11 months total. Both samples failed. Got a refund on the service but spent 50usd in postage for the sample kit.

You align it to a reference genome.

Its like you have an intact 6th edition of a textbook, and you have several copies of the 7th edition sorted randomly with no page numbers. Programs like BLAST will build an index based on the contents of 6 and then each page of 7 can be compared against the index and you'll learn that for a given page of 7 it aligns best at character 123456 of 6 or whatever.

Do that for each page in your pile and you get a chart where on the X axis is the character index of 6 and on the Y axis is the number of pages of 7 which were aligned there. The peaks and valleys in that graph can tell you about the inductive strength of your assumption that a given read is aligned correctly to the reference genome (plus you score it based on mismatches, insertions and gaps).

So if many of the same pages were chosen for a given locus, yet the sequence differs, then you have reason to trust that there's an authentic difference between your sample and the reference in that location.

There's a lot of chemical tricks you can do to induce meaningful non-uniformity in this graph. See ChIP-Seq for instance, where peaks indicate methyl markers which typically correspond with a gene that was enabled for transcription when the sample was taken.

If you don't have a reference genome then you can run the sample on a gel to separate the sequences of different length, that'll group by chromosome. From there you've got a much more computationally challenging problem, but as long as you can ensure that it's cut at random locations before reads are taken you can use overlaps to figure out the sequence, because unlike the textbook page example, the page boundaries are not gonna line up (but the chromosome ends are):

    Mary had a little
    was white as snow
    lamb whose fleece was
    Marry had
    had a little lamb
    a little lamb
    was white
    white as snow

If you're working with circular chromosomes (bacteria and some viruses) you can't reason based on ends but as long as you have enough data there's still gonna be just one way to make a loop out of your reads. (Imagine the above example, but with the song that never ends. You could still manage to build a loop out of it despite not having an end to work from.)

They exploit the fact that so much of our DNA is the same. They basically have the book with no typos, or rather with only the typos they've decided to call canonical.

So given a short sentence excerpt, even with a few errors thrown in, partial string matching is usually able to figure out where in the book it was likely from. Sometimes there may be more possibilities, but then you can look at overlaps and count how many times a particular variant appears in one context vs. another.

One problem is, DNA contains a lot of copies and repetitive stretches, as if the book had "all work and no play makes jack a dull boy" repeated end to end for a couple of pages. Then it can hard to place where the variant actually is. Longer reads helps with this.

There are two ways: Assembly by mapping and de Novo assembly.

If you already have a human genome file, you can take each DNA piece and map it to its closest match in the genome. If you can cover the whole genome this way, you are done.

The alternative way is to exploit overlaps between DNA fragments. If two 1000 bp pieces overlap with 900 basepairs, that's probably because they come from two 1000 regions of your genome that overlap by 900 baswpairs. You can then merge the pieces. By iteratively merging millions of fragments you can reconstruct the original genome.

Both these approaches are surprisingly and delightfully deep computational problems that have been researched for decades.

This is very easily googled. There are new algorithmic advances for new kinds of sequencing data but this is the key (from the 70s)

https://en.wikipedia.org/wiki/Burrows–Wheeler_transform

If you broke a string into overlapping blocks you could easily reconstruct it. The key here is that blocks form a sliding window on the string

If blocks were nonoverlapping then yeah the problem is much harder, akin to fitting pieces of a puzzle. I bet a language model still could do it though

The basic assumption is that most of the genome is essentially random. If you take a short substring from an arbitrary location, it will likely define the location uniquely. Then there are some regions with varying degrees of repetitiveness that require increasingly arcane heuristics to deal with.

There are two basic approaches: reference-based and de novo assembly. In reference-based assembly, you already have a reference genome that should be similar to the sequenced genome. You map the reads to the reference and then call variants to determine how the sequenced genome is different from the reference. In de novo assembly, you don't have a reference or you choose to ignore it, so you assemble the genome from the reads without any reference to guide (and bias) you.

Read mapping starts with using a text index to find seeds: fixed-length or variable-length exact matches between the read and the reference. Then, depending on seed length and read length, you may use the seeds directly or try to combine them into groups that likely correspond to the same alignment. With short reads, it may be enough to cluster the seeds based on distances in the reference. With long reads, you do colinear chaining instead. You find subsets of seeds that are in the same order both in the read and the reference, with plausible distances in both.

Then you take the most promising groups of seeds and align the rest of the read to the reference for each of them. And report the best alignment. You also need to estimate the mapping quality: the likelihood that the reported alignment is the correct one. That involves comparing the reported alignment to the other alignments you found, as well as estimating the likelihood that you missed other relevant alignments due to the heuristics you used.

In variant calling, you pile the alignments over the reference. If most reads have the same edit (variant) at the same location, it is likely present in the sequenced genome. (Or ~half the reads for heterozygous variants in a diploid genome.) But things get complicated due to larger (structural) variants, sequencing errors, incorrecly aligned reads, and whatever else. Variant calling was traditionally done with combinatorial or statistical algorithms, but these days it's best to understand it as an image classification task.

De novo assembly starts with brute force: you align all reads against each other and try to find long enough approximate overlaps between them. You build a graph, where the reads are the nodes and each good enough overlap becomes an edge. Then you try to simplify the graph, for example by collapsing segments, where all/most reads support the same alignment, into a single node, and removing rarely used edges. And then you try to find sufficiently unambiguous paths in the graph and interpret them as parts of the sequenced genome.

There are also some pre-/postprocessing steps that can improve the quality of de novo assembly. You can do some error correction before assembly. If the average coverage of the sequenced genome is 30x but you see a certain substring only once or twice, it is likely a sequencing error that can be corrected. Or you can polish the assembly afterwards. If you assembled the genome from long reads (with a higher error rate) for better contiguity, and you also have short reads (with a lower error rate), you can do something similar to reference-based assembly, with the preliminary assembly as the reference, to fix some of the errors.

Do you have a write up somewhere? If not, it would be amazing if you wrote one!

I was planning on doing a similar thing (also with saliva) once I finished moving in and had a bit more time after conferences. (But, of course, I’d have to go through and actually figure out all of the mechanics and so on.)

I was interested to read this because some time ago I had my genome sequenced by Nebula. If you look at the lawsuit you can see that what Nebula did was use off-the-shelf third-party analytics products on their website, including recording analytics pings when users buy a kit, and pings when users use the Nebula website to browse Nebula's high-level analysis of their traits (leaking that the user has those traits to the analytics provider.)

This behavior represents a contemptible lack of respect for users' privacy, but it's important to distinguish it from Nebula selling access to users' genomes.

https://www.classaction.org/media/portillov-nebula-genomics-...

> There are also concerns about the quality of sequencing and false positives in all DTC genomics testing.

Even when the raw results are accurate there is a cottage industry of consultants and snake-oil sellers pushing bad science based on genetic testing results.

Outside of a few rare mutations, most people find their genetic testing results underwhelming or hard to interpret. Many of the SNPs come with mild correlations like “1.3X more likely to get this rare condition” which is extremely alarming to people who don’t understand that 1.3 times a very small number is still a very small number.

The worst are the consultants and websites that take your files and claim to interpret everything about your life or illness based on a couple SNPs. Usually it’s the famous MTHFR variants, most of which have no actual impact on your life because they’re so common. Yet there are numerous Facebook groups and subreddits telling you to spend $100 on some automated website or consultant who will tell you that your MTHFR and COMT SNPs explain everything about you and your ills, along with which supplements you need to take (through their personal branded supplement web shop or affiliate links, of course).

Yeah, the only way I would ever do DNA sequencing is anonymously...

Both do. I got mine through Dante, my wife through Nebula.

Dante includes a BAM

The article author probably bought the starter kit a while ago. It might explain why the pore count was low. It's a biological product so it degrades over time.

These are by no means a new product. I think the early prototypes for these possibly predate the microUSB plug.

The brochures always showed it next to a completely non-sterile laptop, but it never made sense. It's fundamentally a bio lab equipment, just small. You probably should be wiping the package with disinfectant, use DNA-cides as needed, or follow whatever bioscience people consider the basic common sense hygiene standards.

Now you can buy a portable thermocycler with 13k rpm centrifuge all-in-one with gel electrophoresis - BentoLab for £1299 in UK. Carry case £54. (They sell a Dipstick DNA Extraction kit for £38.50, pipettes, and mastermix...)

A portable lab with a thermocycler, 13k rpm centrifuge, and gel electrophoresis https://bento.bio/bento-lab/

> Intentionally not bothering to go into why, but above average intelligence.

Speaking as a geneticist, it's a shame that this is forbidden knowledge

Oh this is marvelous. I'm going to send you an email at the one in your profile, though I won't be upset if you share here. I suppose there's some Barnum Effect risk with this stuff (and of course singular variants don't immediately mean everything as our GCs have pointed out before), so I'll just answer everything as I can here and maybe you and others will find it interesting.

Licorice - no I don't like it

Lower body weight - until 3 years ago, now 84.5 kg / 183 cm

Anxiety - haha, I suppose that's true

Acne - yes

Dizziness upon standing - yes

Salt cravings - yes

Difficulty with sleep / Insomnia - used to be the case, solved in the last few years, strongest in teenage

Pots/Aldosteronism - not that I know of, just tested and sitting 60 bpm, stand up highest is 77 bpm with a continuous monitor on

Vit D - funny, blood tests which I took for the first time two years ago showed 12 ng / mL (low)

Mg - didn't test, but supplements did not change anything when I tried them in isolation so can't be too bad

Spinach - yes

TMJ - no issue here

Myopia - yes with astigmatism (-4.75 spherical -2.5 cylindrical)

Sour gummy candy - not much of a fan

Caffeine/Melatonin - Yes. Caffeine I always get half-caf. Melatonin I take 200-300 ug when I use it.

Acetaminophen - Can't tell, I suppose

Handedness - Right hand dominant, no ambidextrousness

Geeky - Described as so

Synesthesia - probably not, if weak very weak. I used to think I did, but I think that's because when I learned about it as a kid I really wanted to.

Strategy Board Game - you betcha

Hair Loss - Male Pattern Baldness in teenage years haha!

Alzheimer's - how interesting, I am curious

Smoking - Oops, smoked two years in college. Quit hard.

ADHD - I can't imagine this could be likely, but I suppose I had the excitability, impulsiveness, and talking over people things, but it hasn't really caused any real lasting trouble in my life so I can't label it a disorder really. I have previously received a prescription for this condition as an adult, but I did not take the medication for any appreciable amount of time.

Salt - this is very entertaining to my wife, because yes I do often add salt post-cooking to my portion of the meal and frequently complain about undersalting

Sweet Tooth - Yes (heavily dominating my behaviour), however, blood sugar is normal any time I check it 90 - 100 mg / dL . I could wear a continuous monitor and see what it says.

Now, for the intelligence thing. The various Jonathan-Anomaly-related companies these days are definitely trying to move the Overton window on this front. Herasight is the most well known, but I know of a few that are coming up as well. Of course, I'd like to believe this is true, but I suppose the one massive caveat is that (if you run me through peddy, you'll see) I'm South Asian and I know that South Asians have poor presence in most mainstream genomic datasets - a problem I am hoping to either fix or see fixed in my lifetime.

Your standard disclaimer acknowledged.

Follows from a deeper belief system that the expansion of knowledge is valuable and that humanity can learn even through things (even if they harm me) so long as I am public enough about it. https://wiki.roshangeorge.dev/w/Observation_Dharma

> What’s stopping a similar crisis that 23andMe customers faced where their genetic data along with their identifying information getting sold to the highest bidder if you ever become insolvent?

Nucleus employee here. Nucleus is a medical provider that is providing a medical service and is regulated by medical laws, which extend even through bankruptcy or acquisition. Whereas 23andMe was essentially an entertainment company and was regulated as such, which is what enabled that unfortunate situation to occur.

23andMe’s data isn’t a whole genome. It’s a lot less useful diagnostically, and that’s why it isn’t that valuable.

The author got less than 1x coverage for their efforts. To get the kind of coverage required for reliable base-calls, you need significantly higher coverage, and therefore a significantly higher spend

> That's a prevalent misconception even in the scientific community. Sure, each read has 1% incorrect bases (0.01). But each segment of DNA is read many times over. More or less 0.01^(many times) ≈ 0 incorrect bases.

That's true in targeted sequencing, but when you try to sequence a whole genome, this is unlikely.

The really difficult part of sequencing is after the Vcf. Before that is trivial, you can plug your fastq raw data in some Nextflow pipelines and in a couple of days of computing get plenty of "results" to be busy exploring for years.

Did you learn this from Genomind? Or how did you get from genomes to useful information re: meds?