How to sequence your DNA for <$2k
maxlangenkamp.substack.com
maxlangenkamp.substack.com
Yes it requires chopping the genome opening small(er) pieces (than with Nanopore sequencing) and then reconstructing the genome based on a reference (and this has its issues). But Nanopore sequencing is still far from perfect due to its high error rate. Any clinical sequencing is still done using sequencing by synthesis (at which Illumina has gotten very good over the past decade).
Nanopore devices are truly cool, small and comparatively cheap though, and you can compensate for the error rate by just sequence everything multiple times. I’m not too familiar with the economics of this approach though.
With sbs technology you could probably sequence your whole genome 30 times (a normal “coverage”) for below 1000€/$ with a reputable company. I’ve seen 180$, but not sure if I’d trust that.
Usually, but sometimes the errors are correlated.
Overall I agree, short read sequencing is a lot more cost effective. Doing an Illumina whole genome sequence for cell line quality control (at my startup) costs $260 in total.
There is no reason for Nanopore to supplant sequencing-by-synthesis for short reads - that's largely solved and getting cheaper all the while.
The future clinical utility will be in medium- and large-scale variation. We don't understand this in the clinical setting nearly as well as we understand SNPs. So Nanopore is being used in the research setting and to diagnose individuals with very rare genetic disorders.
(edit)
> We are not “in the nanopore era of sequencing”. We are (still) firmly in the sequencing by synthesis era.
I also strongly disagree.
SBS is very reliable but it's common (if Toyota is the most popular car, does that mean we're in the Toyota internal combustion era? Or can Waymo still matter despite its small footprint?).
Novelty in sequencing is coming from ML approaches, RNA-DNA analysis, and combining long- and short-read technologies.
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.
It would be difficult to break a modest program into basic blocks and then reconstruct it. Same with paragraphs in a book.
How does this work with DNA?
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.
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.
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.)
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
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.
A recent paper on classifying acute leukemia via nanopore: https://www.nature.com/articles/s41588-025-02321-z/figures/8
The timelines are exaggarated but still it works and that’s what matters in diagnostics.
It really depends what your goals are. At the NAO we use Illumina with their biggest flow cell (25B) for wastewater because the things we're looking for (ex: respiratory viruses) are a small fraction of the total nucleic acids and we need the lowest cost per base pair. But when we sequence nasal swabs these viruses are a much higher fraction, and the longer reads and lower cost per run of Nanopore make it a better fit.
I'm a nobody, and I can drop a tube into a box in a local university, and get the results emailed to me by next morning for $15USD. This is due to a streamlined nanopore-based workflow.
> Another problem was our flow cell was malfunctioning from the start — only 623 out of 2048 pores were working.
Is this normal for the machine? Is there a better write up somewhere where they didn’t give up immediately after one attempt?
My Nanopore flow cell had nearly every pore working from the start. So I would say that is not normal. Maybe it was stored incorrectly.
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.)
No, it's not "normal," but it is fairly common. When I worked in NGS, nearly 1/4 of flow cells were duds. ONT used to have a policy where you could return the cell and get a new one if it failed its self-test.
In my experience, most inactive pores are due to a poorly prepared sample. I don't know why, but maybe it blocks or jams the pores.
When I analyzed Oxford nano pore data a few years ago, I found it to be very sensitive to skilled sample preparation. The data quality varied so much that I could tell which of my laborant co-workers (the experienced one or the new one) had prepared the sample by analyzing the data. So I expect that the authors garage sample prep maybe wasn't great.
Coincidentally, I had a colleague who worked on building a portable sequencing lab powered by a car battery. The purpose was to be able to identify viruses by DNA from a van in rural Central Africa or wherever. Last I talked to her, the technical bottleneck was sample prep - the computational part of the van lab wasn't too hard.
Nebula is facing a class action for apparently disclosing detailed genomic data to Meta, Microsoft & Google. The subreddit is also full of reports of people who never received their results years after sending their kits back. There are also concerns about the quality of sequencing and false positives in all DTC genomics testing. Given what happened with 23andme as well and all of this stuff, I'm wary of sending my genetic data to any private company.
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).
Even better would be to swap identity with someone else who wants to get sequenced...
1. There are already multiple database containing both your parents, you, and a linkage between you and them indicating parentage. So, prior knowledge: Alice and Bob are parents of Charlie.
2. If Charlie's parents have taken a DNA test, there already exists a database linking their DNA to their name. So, prior knowledge: Alice's DNA belongs to Alice, Bob's DNA belongs to Bob.
3. If Charlie takes a DNA test totally anonymously and perfectly untraceably, it will still show up as, child of Alice and Bob's DNA. So, knowledge now includes: Charlie's (anonymous) DNA is the son of Alice and Bob's DNA
4. From these pieces of information, it is trivial to de-anonymize Charlie's DNA, linking it to Charlie's identity: the only person it could belong to is the son of Alice and Bob, and the son of Alice and Bob is already known from point 1.
I think in my case I'm just not that concerned by the hypothetical because my parents haven't done sequencing/genetic screening and also aren't likely to. I guess the main question is how far out in my family tree I have to think about that. (Also has implications for my descendants, I suppose...)
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-...
I don't have any evidence they're selling anything but that lawsuit shows pretty sloppy behaviour for a company that should be thinking very deeply about privacy. I guess that's about what you said though :)
If you want to go even cheaper and depending of what you want, you can go for an exome instead of a WGS. And a lot of people are sequencing when they really want genotyping.
But I would not be surprised if someone is already getting $100 WGS.
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.
This can be done in the field (read near a lot of dirt). This does not require sterility at all. The main problems with this are keeping your prep clean (which is different from sterile; primarily involves not getting bubbles where they shouldn't be etc.) and temperature/salt handling.
> These are by no means a new product. I think the early prototypes for these possibly predate the microUSB plug. > 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.
The consumable product is what needs to be stored carefully. Its delivered DNA-free; no disinfectant is needed. It's actually hard for accidental DNA to be introduced at the sequencing step; that would usually reflect poor practices earlier on.
OP you'd get better results of you centrifuge your blood, extract the white blood cells and sequence those instead of whole blood. Thats a bit tricky with a lance and a tiny device though...
A portable lab with a thermocycler, 13k rpm centrifuge, and gel electrophoresis https://bento.bio/bento-lab/
If you're curious about my genome, here are my VCF files https://my.pgp-hms.org/profile/hu81A8CC
If you want to indulge your curiosity some more:
$ rg "20189511" /Users/george/tmp/genome/nebula_roshan_NG1AW8W7PU.mm2.sortdup.bqsr.hc.vcf
3499829:chr13 20189511 rs104894396 C T 252.77 . AC=1;AF=0.500;AN=2;BaseQRankSum=1.54;ClippingRankSum=0.00;DB;DP=25;ExcessHet=3.0103;FS=4.008;MLEAC=1;MLEAF=0.500;MQ=60.00;MQRankSum=0.00;QD=10.11;ReadPosRankSum=0.666;SOR=0.160 GT:AD:DP:GQ:PL 0/1:15,10:25:99:281,0,436
In practice, when my wife and I did carrier screening we didn't do it with Nebula, but carrier screening also confirmed that we had GJB2-related hearing loss genes in common. The embryos of our prospective children were also sequenced so that we could have a child without the condition.
Anyway, if you'd like a test file of a real human to play with, there's mine (from Nebula) for you to take a look at. If you use an LLM you can have some fun looking at this stuff (you can see I'm a man because there are chrY variants in there).
I also used Dante because I wanted to compare the results of their sequencing and variant calling. Unfortunately, they have a different way to tie the sequence back to the user (you take the code they have and keep it safe, nebula has you put the stuff in a labeled container so it's already mapped by them) and I was in a hurry with other stuff. They never responded to me with any assistance on the subject - not even to refuse the request to get the code for that address - so I have no idea how they work.
The nanopore stuff is very cool, but I heard (on Twitter) there were quality control issues with the devices. I'd love to try it some time later just to line it up with my daughter's genome.
Depends on the sum of all the genes in this area of course, but this one mutation is a big influence on the hpa-axis. I would ask if you have lower body weight, heightened anxiety, bad acne as a teenager, episodes of dizziness upon standing, salt cravings, and difficulties with sleep this would be the main driver, pretty standard nonclassic CAH. If you had ever thought you might have "pots", the more accurate would be hypoaldosteronism (but depends on renin genes).
Sense we were poking around here is some highlights
Decent chance of being left handed or ambidexterity given that you also have PCSK6 rs11855415 a;t at the same time (as it can help with the salt issues) and I look for when I see something like the above two.
Vitamin D risk given your GG CYP2R1 (dr probably checks that yearly anyway), risk of lower Mg because of this (cramps, muscle twitches?).
bvitamin wise b9, b12 could be on the lower side given MTRR AA rs1802059 (combined with MTHFR 31 GT 76 CT, MET 30 CG, COMT 99 AG, BHMT rs3733890 G;A). Probably like spinach. If you have TMJ regularly you need to find the right diet or bcomplex for you which will fix this as well as any hypermobility resulting from the collagen production issues. Higher chance of myopia, especially if you are gen Z.
TPH2 rs4570625 g;t jumps out on the serotonin path. Vit d can help here, some might say 5-htp when depressed, but fix the vit d first. Do you like sour gummy candy?
CYP1B1, I see 3 reductions, combined (and the above) I would ask if you have glaucoma in your family history, if so then stuff you can do.
CYP1A1 rs1048943 C;T and really CYP1A2 rs762551 A;A, so fast caffeine and melatonin issues. More insomnia.
CYP2E1, need less acetaminophen to do the same as others.
Intentionally not bothering to go into why, but above average intelligence.
Combine all of the above and decent chance you fall into the bucket of being taller (6'1"?), skinnier, hard time falling asleep and also likes sleeping in, higher libido, left handed, high visual skills, geeky. Possibly synesthesia (a weaker form). Would enjoy a strategy board game over trivial pursuit. Earlier hair loss. Higher risk of one form of Alzheimer's (there is stuff you can do today to reduce it). *Do not smoke*. Didn't dive into all of the ADHD genes, but if mild resolving the above Vit D, b vitamin deficiencies would influence that.
This was with 10 minutes of poking around not a comprehensive look. Mostly I just wanted to add a comment to the general reader that genetics variants are part of larger systems. You would want to do a deeper look, combine it with symptoms as well as lab work to determine the full impact of any change. For example the PCSK6 variant reduces the impact of the CYP11B1 variant. Further you could also easily have something else on the hpa-axis that completely negates the NCAH and never have any salt issues at all. Before spending time looking through each gene I would simply ask, hey do you love to put salt on every meal?
Another one I didn't dig into, but would just ask first is if you have a big sweet tooth. (ncah influenced hypoglycemia).
Feel free to give me a ping and I can walk you though this better.
There is a reason these always end with a disclaimer, talk to your doctor about making changes to your diet, etc, I am not a doctor just someone who learned biology/genetics as a hobby especially given how it can teach tricks to apply to software engineering and my ai/AGI work.
Speaking as a geneticist, it's a shame that this is forbidden knowledge
The real problem is that most historical papers look for single SNPs. But a gene could have dozens of variants that do the same thing or you could have a genetic path where a major variant in any gene on that path results in the same outcome. These papers overlook this.
So you have to step back and asking: What are the principles of intelligence? and how would I expect to see them in human biological or other biological systems? (And related, why does humans have intelligence "now"?)
For this crowd, if I take an LLM and make it bigger is it intelligent? Obviously a key component of intelligence is raw stuff. Someone with fattier myelin sheath's straight up has more/faster "brain stuff". You might say ChatGPT 4.5 is "smarter" than 3.5, but not intelligent. There are two other key attributes missing. For those following along with the arc-agi you might already have a hint what those are simply based on what is moving the needle forward. Now even with all three and you are close and simply need to provide an environment for self-replicating with a selection pressure and energy constraint. For one definition (others will have a different definition) I have had a primitive AGI for 13 months now and regularly put it to work on sub problems of mine.
This really took off when I was reading genetics files like books and noticed I was reading files that are very similar, but some were Mensa level folks and others were more just "smart". Didn't take too much longer to piece together the key paths and differences and even went tracing back through Neanderthals DNA (How cool is it these days that we can simply poke around Neanderthals DNA!).
So it isn't forbidden, but more like I know what to look for and people are super sensitive around saying someone is probably smarter or not from a genetic file so I usually don't comment because of the Gattaca problem.
P.S. If you have a bio/genetic background and are playing around with AI I would love to chat. There are so very few of us. DeepMind is thinking of some of this, but they are in the UK. (It would be fun to give a meta talk to them explaining why their smartest engineers are smart.)
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.
I used WGS from Nebula - but would like to back that up with Nanopore raw DNA reads targeted on specific genes where I need more accuracy and to investigate structural differences that Illumina or Nebula's MGI machines can't pick up... also for the additional methylation data.
It is quite hard to get yourself sequenced in EU in 2025.
The sub-$100 genomes could be in reach within the next 5 years, from what I have seen.
Considering how big a deal that was at the time, and how strong a differentiator it would be, it’s notable how absent from the homepage. It’s nice that Nucleus claims not to sell the data, but 23andMe had similar claims, it wasn’t strong enough to prevent genetic data from being transferred if they were to be acquired.
This has always been something I’ve been interested in but so far no company handles privacy concerns of data that’s so deeply fundamentally personal and private in a satisfactory way, and I’m especially apprehensive post-23andMe.
Taking a dice roll on Nucleus not just pulling another 23andMe seems not worth the ~$3000 saving you claim to be offering.
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.
So, yes, you can sequence your genome relatively cheaply using these technologies at home, but you won't be able to draw any conclusions from the results
For assembling a bacterial genome the consensus error rate is as low or in some cases better than Illumina.
Nanopore platform has its usecases that Illumina falls short on.
> So, yes, you can sequence your genome relatively cheaply using these technologies at home, but you won't be able to draw any conclusions from the results
Agreed, any at home sequencing should not be used to draw any conclusions.
That's true in targeted sequencing, but when you try to sequence a whole genome, this is unlikely.
Whole-genome shotgun sequencing is pretty cheap these days.
The person you are replying to doesn't give any specific numbers, but in my experience, you aim for 5-20x average coverage for population level studies, depending on the number of samples and what you are looking for, and 30x or higher for studies where individuals are important.
For context, coverage refers to the (average) number of resulting DNA sequences that cover a given position in the target genome. Though there is of course variation in local coverage, regardless of your average coverage, and that can result in individual base-calls being being more or less reliable
You need multiple flow cells or a higher capacity flow cell to get anything close to 1X on an unselected genome prep.
Shotgun sequencing isn’t probably what you meant to say - this is all enzymatic or, if it’s sonicated, gets size selected.
But if we are talking nanopore sequencing, then yes, you need multiple flowcells. Which is not a problem if you are not a private person attempting to sequence your own genome on the cheap
You can do nanopore PCR/cDNA workflows right up to the largest known mRNAs (13kb).
Edit:
I’m not sure if you’re saying that you can’t do a 5/20/30X genome on nanopore - that’s also not true. It only makes sense in particular research settings, of course.
https://pmc.ncbi.nlm.nih.gov/articles/PMC3849550/
You can sequence a human genome on a MinION - but you need to purchase 5 flow cells to reach 11X (if they are used correctly).. https://nanoporetech.com/news/news-human-genome-minion
Yes... and that is why I said that the presence of PCR does not imply short reads.
> You can sequence a human genome on a MinION - but you need to purchase 5 flow cells to reach 11X (if they are used correctly).. https://nanoporetech.com/news/news-human-genome-minion
THat's why I said that 5/20/30X coverage is possible in the appropriate research setting
I can't 100% prove it wasn't a legit mutation but our lab did several tests where we sequenced the same sample with both Illumina and Nanopore, and found Nanopore to be less than perfect even with exteme depth. Like, out depth was so high we routinely experienced overflow bugs in the assembly software because it stored the depth in a UInt16.
The issue with this approach is that you'll receive raw data that needs to be processed. Even after processing you'll need to do further analysis to answer your questions. After all this, I'd be suspicious of the results and seek a medical councellor to discuss and perform further tests.
I'd advise on thinking what questions you want answered. 'Sequencing your genome' sounds amazing but imo you're better off with seeking accredited tests with acrionable results.
It might even make you sicker through anxiety or other nocebo.
It's only really useful as a confirmation for a doctor's diagnosis, and those are done with slightly better data controls.
Any recommendations?
They are building Fully Homomorphic Encryption (FHE) and Multiparty Computation (MPC) tools for genetic data. Your data format may need to be modified. They currently focus on the SNP results from places like Ancestry.
Some HN posts from their CEO:
k
> 200 µL of blood (about ⅕ of a ml)
"About"? Anyway, thanks for the clarification.