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.
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.
> 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
I’m referring to the experiment done in the OP - the most I’ve read about from an minION flow cell is 8 Gb (and this is from cell line preps with tons of DNA, so the coverage isn’t great).
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.
What the person you replied to described read like short read sequencing with PCR amplification to me ("each segment of DNA is read many times over"), rather than nanopore sequencing. My reply to you was written based on that (possibly false) assumption.
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
There wasn’t enough information to tell (on my 1 minute scan) which nanopore kit was used, but the presence of PCR does not imply short reads.
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.
I worked with Nanopore data about four years ago, and I found that that's mostly true, but for some reason at some sites, there was systematic errors where more than half of reads were wrong.
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.
What was the DNA source? At the same time (4 years ago) there were issues with specific species -
Birds and some metagenome species were the worst if I remember correctly.
Over 90% of diabetes is type 2: it's the classic metabolic disease. Noting down the exact type adds complexity to be honest. The are other forms, such as type 1, MODY or even insipidus and explaining all of them is another topic and they are comparatively rare. It's a bit of a stretch to call out the journalists work ethic as lazy. That said once specifying "type 2" wouldn't hurt but it's not necessary either.
As a T1D, I strongly disagree. The confusion of the “types” profoundly impacts public opinion, which is significant downstream (funding, employment bias, etc.).
Unfortunately, they recently removed most of the books from that hall due to conservation efforts. I didn't really give the feel or atmosphere of an old library.
I'm very active on iNaturalist. The name suggestions made there by image recognition, at least for the genus I work with, is a complete and utter joke. >98% of name suggestions for that group are incorrect. As soon as a genus becomes diverse, it classifies everything as a single species instead of a genus. It does not have ability to recognize 'the unknown'. Most of my time on that website is wasted on clearing up gross incorrect name suggestions from AI that people accept without any checks of plausibility. Even with species that have >2000 confirmed observations, it still incorrectly suggests the name for obviously completely unrelated species. You didn't take the time to explain what you mean with 'hyperspectral' but I'm assuming it just introduces a new dataset where we start all over.
Seek by iNaturalist got a lot of very positive press here on HN for some reason, but it's crazy. I assume Seek is why iNaturalist classifies everything as a single species with no possibility of 'unknown' - as Seek views the world, the goal of classifying is to produce a species name, and accuracy isn't a concern.
I have a photo of an elephant seal that Seek informs me is actually a clouded monitor lizard.
I also have two photos of identical plants growing inches apart from each other that Seek informs me are unrelated.
Hyperspectral imaging is about collecting images in more than three bands of color (red, green, blue) as perceived by humans. Lots of things look very different in the IR and UV spectra, especially flowers and insects.
hah. tricked you! I rely on folks like you to help me ID the things that the app can obviously not ID correctly :) The best way to get the correct answer on the internet...
I get quite a few genus level suggestions when it can't determine to the species level. I'm not an expert taxonomist so I'm sure there are still many overly specific incorrect assignments.
Why is this "way better"? The article appears to discuss a blood tests to triage stroke which is unrelated to both disease treatment or identifying a root cause of the stroke.
I'm surprised how the conversation shifted from genetics to unethical germline editing of things that barely have clinical relevance. The rates of scurvy in the 21th century are low and definitely don't warrant super invasive editing germline cells (!), changes which become hereditary. Adding vitamin synethesis is super complex: it's not just a single SNP change but would require a whole new enzyme system. It's a very fine line from opening up a big old can of eugenics worms. Adding extra tRNAs is sometimes done in microbiology but any health benefits are extremely questionable. I've never before seen germline editing be a proposed 'solution' to malnutrition.
Tesla is refusing to sign a collective bargaining agreement which is basically the norm in Sweden. This has been going on for a couple of weeks and garnered a lot of attention in Swedish media and social media. Sweden has a strong tradition of unionization which clashes with 'big American capitalism'. Swedish subreddits have lately had frequent posts with the comment sections filled with comments that more or less in unison are saying 'go get them, grabs popcorn' in support of the actions taken against Tesla.
The strike has been ongoing for a couple of weeks but in the result of Tesla refusing to negotiate despite five years of attempts from the union. So it’s actually ongoing for +5 year
Ironically leeches are used to this day. At the most specialized hospital in Sweden they are sometimes used to treat wounds that have a hard time healing. IIRC they somehow promote revascularizing of the tissue.
This doesn't hold true for all cell types. There's a group in Sweden that has C14 dated the replication rate of some cell types. IIRC some cell types - some neurons and adipocytes - only replicate every 10 years or less. Their method has something to do with C14 from nuclear weapons: people who lived before they went off wouldn't have as much. And a lot of C14 would be from the uptake of nuke C14 since. IIRC pre-nuke people have adipocytes with C14 amounts compared to post-nuke people because those cells barely divide. That's what I picked up from a talk way back. I believe this is the same group.
https://news.ki.se/new-neurons-generated-in-the-hippocampus-...
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