No human genome has ever been completely sequenced
statnews.com
statnews.com
> 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 overwhelmed. The resulting chunks contain from 1,000 letters (during the Human Genome Project) to a few hundred (in today’s more advanced sequencing machines). The chunks overlap. Computers match up the overlaps, assembling the chunks into the correct sequence.
> That’s between difficult and impossible to do if the chunks contain lots of repetitive segments, such as TTAATATTAATATTAATA, or TTAATA three times. “The problem is, when you have the same exact words, it’s hard to assemble,” said Lander, just as if jigsaw puzzle pieces show the same exact blue sky.
> In 2004, the genome project reported that there were 341 gaps in the sequence. Most of the gaps — 250 — are in the main part of each chromosome, where genes make the proteins that life runs on. These gaps are tiny. Only a few gaps — 33 at last count — lie in or near each chromosome’s centromere (where the two parts of a chromosome connect) and telomeres (the caps at the end of chromosomes), but these 33 are 10 times as long in total as the 250 gaps.
I recommend reading HN user eggie's comments on this from last week.
https://news.ycombinator.com/item?id=15482439 https://news.ycombinator.com/item?id=15483462
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))
[0] https://docs.python.org/3/library/random.html random.choices() was added in Python 3.6
[1] http://www.biology-pages.info/B/BasePairing.html source for `weights`
[2] https://en.wikipedia.org/wiki/Human_genome source for 3_234_830_000 (python ignores underscores in numbers)
[3] http://sci-hub.io/10.1111/j.1755-0998.2011.03024.x illumina is the most popular producer of genome sequencers
On a tablet, I highlight and pull down. It grabs it all and I can then read it. It is a very awful workaround that impacts the site's usability.
The alternative technologies, sometimes called third generation sequencing are nanopore based (PacBio and Oxford Nanopore technologies), they work by pulling long DNA molecules through tiny holes and read the sequence directly, more specifically, they read a couple of base pairs at a time and infer the sequence from the current between the sides of the pores. This is very error prone and the (around 1 in 10?) random errors are corrected by sequencing every part of the genomic area of interest multiple times.
To be clear, most sequencing experiments (in research and in the hospital) are designed to look at variations we can understand and thus the "words" (we call them reads) are aligned against "the" reference genome (as opposed to assembling de novo), which is the genome for as far as we understand it (the last official version is from 2013 but is updated regularly with new parts). Typically, a significant portion of the reads a sequencer produces cannot be mapped to the reference, probably as a results of unmapped regions indeed or perhaps deviations between the patient and the reference genome, this is of course even worse in patients with tumors with unstable genomes. This field still has a lot of challenges to tackle.
Nope, not randomly. Having a random error profile is the best case. In reality these machines suffer from systematic errors, especially the Illumina ones. That means that they always fail to read through certain pieces of DNA, or make the same error every time.
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...
I realize that the magnitude of this natural shuffling, even during meiosis, is nowhere near like what happens when sequencing DNA[2], but (assuming I'm correct) it's helpful to realize that this problem of deciphering the "correct" sequence reflects something intrinsic to the operation of the machinery; that this applies even when the relevant macro sequences are all genes that directly code for phenotypes as traditionally understood; and that gene sequencing works precisely because many (most?) genes are relatively tolerant to being spliced in at random locations in the strand. (Which is different than saying all locations--or even any two locations--produce identical results.)
[1] I refrain from saying "complete set of genes" because I imagine for some genes location at both the micro- and macro-scale is everything. AFAIU the term gene is sometimes better understood as more a description of the result of possibly non-local sequences which maintain reproductive affinity than an identification of a specifically adjoining sequence of base pairs.
[2] Or is it?
This is correct, and is a reason why genome informatics has been moving away from representing the genome as a single linear "reference" sequence of 3 billion nucleotides, and towards a graph-based model: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411762/.
There’s even evidence that this is true, for who knows their audience better than them? You, while making up a a portion of the audience, are not the main portion, or so it seems, unless they don’t understand their audience that well after all... although that seems pretty unlikely
So you have two problems. The first is, you never read anything longer than say 1k bp ever. The second is say your nuclease cuts at GAATTC. You will have great difficulty counting runs of that sequence.
this is the technique https://en.wikipedia.org/wiki/Polyacrylamide_gel_electrophor...
The other takeaway I had, as someone who came to this with a CS and stats background, is that chemistry sucks. None of it is deterministic, and when people say some chemistry does X, what they really mean is it generally does that. Most of the time. Subject to conditions and a reasonable amount of stochasticity. We're built on a pile of random muck hacked together.
And yes, the chemistry that biology takes advantage of means embracing uncertainty in the world. It's all thermodynamic chances, with lots of non-linearity added on top of it all.
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.
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.
ONT will be much more useful for assembling reference genomes (which can then be polished with short reads), characterizing large scale structural variants, and real time applications like "what pathogen am I infected with?".
If you're doing a standard RNA-seq experiment, short 100bp to 200bp Illumina reads are just dandy for what you need. Most of the time, at least; you could, for example, be interested in different splice variants for a gene family; in that case longer continuous reads are valuable. But then it's no longer a standard RNA-seq.
PacBio and pore-type sequencing is most valuable for polishing reference genome assemblies. For instance, this is a huge issue for crop plants because they have huge, repetitive genomes and are often polyploid. So there's a lot of interest in creating high quality assemblies not just for e.g. corn, but the major research inbreds (B73, Mo17, etc.) as well as private breeding lines. Some of the most valuable crop QTLs (R genes, microRNAs, transposon fragments, etc.) are just 'junk' sequence that plays a regulatory or defense role, so it's super important.
In general, the Illumina data is very high quality and very inexpensive, and works beautifully provided you have a high-quality assembly to work with, and what you're interested in isn't very repetitive. It has many good applications, so I don't see much risk at this point.
If, at some point, longer reads can be done with high fidelity and with similar cost, then there would be an issue. I don't think that's going to happen anytime soon, but I'm sure Illumina is looking at that long-term.
Don't get me wrong, the Minion is amazing, but it can't compare to PacBio in terms of quality... yet.
Substitution error rate was very low, but you should evaluate INDELs specifically, rather than just identity like you did on your git, because it is the major drawback of this technology.
The non-random nature of their error profile is also a problem.
Also, which library prep are you using? I think the ligation kit has historically gotten better yield (not sure if that's true any more).
I'm also extremely put off by how they handle support -- that is via a forum where they do their very best to barely answer anything
I used the flowcell version previous to the current one and got ~2gb of data. I used the ligation kit on genomic extraction of ~10 individuals (crustacean).
When I was a movie theatre projectionist, I had a similar problem. Movies have to be assembled onto platters, from a handful of reels which contain 10-20 minute lengths of film. There's about a mile of film per hour, and to assemble it quickly you have a motorized platter and a table with its own motor with dials to control the speed of one or the other. How it should work is you take a center ring - a circle with a gap to wind film through and metal spokes that sit in the platter - and you spin the film on. You put the reels one by one onto this, stopping with each one to cut the footer of one and the header of the next to splice them together with tape.
Well, that's how it's supposed to work. Sometimes, the film tightens up, when moving the film from one platter to another. The center ring spokes might not fit, or might create additional stress.
Well, I went full speed ahead anyway, and when a tiny bit of slack resulted in a sudden jerk on the platter, the center ring popped off, and about an hour of film flew over my hand and into the wall behind me. What happened next is almost impossible to describe. A circular disk of film and metal hitting a solid wall briefly became a vertical column as the tension-less film was forced to go any direction except forward, and the result then splayed itself out on the floor in front of me in a tangled mess a mile long.
Perhaps like an early sequencing machine, my first attempt at recovering this was to look at a piece of the film and determine what part of the movie it was from, and to begin throwing away the parts I thought were from trailers.
They weren't. It was the wrong movie.
I despaired, the film was a mile long and tangled into unbelievable knots. I had to cut it to untangle it. But how?
And it hit me, I knew where a handful of colored markers were, and I began randomly pulling segments of film out from the mess, placing masking tape across a frame, and making 3 lines in 3 different colors. I made dozens of these little loops, and that was how I put it together. The process: cutting, labeling, and placing the segments into new reels made my shift from that night run into the next day's matinee.
This process was painstaking, and that's where my question leads: can we label the ends when we cut up the DNA, like I did with my film? Are we stuck watching chromosomes splash against the wall and shatter into indecipherable pieces?
from TED: https://youtu.be/WFCvkkDSfIU?t=3m39s
A question I've never seen addressed: is one justification for junk DNA to create space for beneficial mutations?
Obviously some mutations are actively harmful, but the reason most mutations are destructive is that they break something already-useful into something inert. That's not a risk with non-coding (or irrelevantly-coding) DNA, which means that in return for spending energy on copying junk DNA, we get space where even barely-useful changes will be net positive.
Is this obvious to experts? Completely stupid for reasons I don't know? Even a possibility worth discussing?
See wikipedia [1] for an overview.
[1] https://en.wikipedia.org/wiki/Gene_duplication#As_an_evoluti...
Yes, gene/genome duplications can help create raw material for adaptation, or be adaptive by their own right.
>That's not a risk with non-coding (or irrelevantly-coding) DNA
It's not risk free. It's just less risky. There are all sorts of elements within the "intergenic" region of genomes which can impact fitness. Duplications in these regions can change the "dosage" of genes or the availability of DNA to be transcribed.
Duplicating these regions is mostly dependent on how tolerate the species is of dosage changes. Humans for example are very intolerant, while plants are very tolerant.
Also, please stop using "junk DNA". It is outdated term ;).
And yes, I'll be dropping "junk DNA" from my vocabulary immediately. I knew some non-coding regions had been found to have uses, but this thread taught me just how extensively relevant it is!
If you're interested in that, I'd look up "Whole Genome Duplications". For instance, corn underwent a WGD about 7 million years ago (IIRC, the number might be off), and we can identify an 'a' and 'b' genome. After these events, most of the duplicated genes will be lost, either through purging selection or drift. However, some will stick around and take on new subset functions (see nkrumm reply).
If you really want to get into the weeds on this, there are many metrics that could, in theory, affect the 'evolvability' of a genome. Higher transposon (or any sort of repetitive element) content can lead to large-scale shuffling of genome structure, such as inversions, duplications, etc. The ecological success of the grass family, for instance, may have it's roots in genome structure.
Now, there's not a whole lot we can say about this because it's not something we have a really good grasp of. It's mostly some hypotheses that are quite difficult to test, but this is some of the most interesting genomic research going on, in my opinion.
All of this is quite difficult to understand, however, as it really depends on epigenetic mechanisms like chromatin state, dicers, RISC, and all that. Figure that out, and you'll realize there's no 'junk' DNA, just various non-coding sequence that's part of this larger-scale genome evolution.
One reason is the absence of sex chromosomes in plants. If humans get a duplicated sex chromosomes things go completely haywire. Another reason is that due to so many WGDs, plants have ample gene copies, so the copies can freely mutate around or even break, there's always a backup.
Not all of these shenanigans work out, in plants this has the wonderful name 'genomic shock'. In wheat there's a candidate gene (Ph-1) which seems to somehow stabilise these instabilities between chromosome copies. In other species people have been trying for a few years to make polyploids but have been failing because there is no stability mechanism - for example, there are only few papers where people managed to make a Brassica hexaploid, these hexaploids are usually very unstable and have problems generating offspring (see for example http://www.cropj.com/malek_7_9_2013_1375_1382.pdf )
I wonder if he said this with a straight face.
Alternative splicing, readthrough regulaton, multiple kinds of ribosomes, post-translational regulation, etc. all produce significant variation outside of the dna itself.
We have about a 0.0001% comprehension of this whole system, how can you possibly be so arrogant as to think you KNOW that certain parts of the sequence are not important or used in _any_ way? Then again, I'm definitely not qualified to comment on the topic.
> “As a matter of truth in advertising, the ‘finished’ sequence isn’t finished,” said Eric Lander, who led the lab at the Whitehead Institute that deciphered more of the genome for the government-funded Human Genome Project than any other. “I always say ‘finished’ is a term of art.”
The phrasing of your parent is arguably dismissive and does little to further substantive discussion. Can one meaningfully discuss "the human genome"? As you point out, each human (with the exception of identical twins) has a unique genome. I think it's reasonable and useful to be able to say "the human genome" as opposed to "the chimpanzee genome" or "the drosophila genome".
If one would rather insist on using the indefinite article, i.e., "a human genome", one can present it without resorting to a rhetorical question replete with scare quotes.
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)
Assuming each mutation is independent, etc, etc we get a back of the napkin via the binomial distribution with mean = n*p ~ 60 mutations per division.
However, that is for germline, somatic mutation rates are apparently 10-100x more more common: https://www.nature.com/articles/ncomms15183
These rates no doubt vary by cell type, environment, etc. I was actually thinking something like 1e-6 to 1e-10 mutations/bp/division.
Somatic mutations are just errors in genome duplication in somatic cell lines - i.e. not germ line. These accumulate over time and, yes, can be oncogenic. Many others simply have no consequence at all.
For many organisms, the germ line is 'sequestered' fairly early in development, in part to limit the number of replications needed, and thus errors that accumulate.
Members of a species share most of their genetic material with each other, but there are known areas of high variability between individuals. So to build a specific individual's sequence, it seems like they take the baseline "human genome" data, and overlay the areas that are known to change.
or did you think science was a book of unambiguous answers that only the godly scientist may read?
And: "“I’m between agnostic and a little skeptical that these bits will be important for disease, but maybe I’m saying that because we can’t read them,” Lander said.".