You know, I was actually thinking about the concept
of a library the other day. Oh yeah, just the
general idea of one. Yeah, specifically that
really old -school image. You know, you picture
the smell of dust, the silence, rows and rows
of physical books that supposedly contain everything
humanity has ever thought or written down. It's
a really powerful image. It is. It's basically
the external hard drive of human civilization.
Exactly. But today, for our deep dive... We're
going to talk about a library that makes the
Library of Congress look like, I don't know,
a distinct stack of Post -it notes. And the crazy
thing is this library doesn't store books. It
doesn't store maps. It stores the literal instructions
for building well, everything alive. Or at least
everything alive that we've managed to actually
run through a sequencer so far. Right. We are
diving into GenBank, and if you're joining us
for this deep dive, you probably already know
we love a mind -boggling number to set the stage.
So to give you a sense of the sheer scale of
this place right off the dot, as of October 2024,
this database contained 34 trillion base pairs
of data. Yeah, 34 trillion. It is a number that
is genuinely hard to even wrap your head around.
It's totally abstract. Yeah. Right. I mean, I
can't picture 34 trillion of anything. But by
the end of this conversation, we're going to
make that number feel very real. Because GenBank
isn't just some cold storage facility for data.
It is effectively the operating system for modern
biology. Oh, absolutely. Like if you've eaten
bread today, taken an antibiotic or had a COVID
test, your life is intersected with this database.
That is no exaggeration either. GenBank is the
gold standard. It's the open access collection
of all publicly available nucleotide sequences
and their protein translations. If you're a researcher,
you aren't just visiting this library. You are
practically living in it. It's the water the
scientific community swims in. But here is the
hook. And really the reason I wanted to pull
these specific sources for us today. tend to
think of scientific databases as these pristine,
infallible vaults of absolute truth. Like, you
know, if it's in the computer, it must be right.
And that is a very dangerous assumption to make.
Because as we dug into the reports on GenBank.
We found that this library is messy. It's growing
so incredibly fast, doubling in size every 18
months, actually, that it's starting to break
the systems meant to organize it. We've got fake
news in the form of misidentified fish. We've
got anonymous turtles that shouldn't exist. And
we have a quote unquote leaderboard of life that
puts common bread wheat way above human beings.
It is a perfect storm of exponential growth and
human error. It's a testament to human curiosity,
sure, but also to our fallibility. So today's
mission is to basically unpack this beast. We're
going to look at the scale. We're going to look
at those charts to see who the main characters
of biological research really are. And we're
going to talk about why, despite being the scientific
Bible for so many, it might be harboring some
pretty significant glitches. It's a tree deep
dive into the very infrastructure of modern biology.
So let's start with that number again, 34 trillion
base pairs. Help us break down the actual inventory
here. It's difficult because the scale is just
so vast. But let's look at the distinct sequences.
As of release 250, which came out in October
2024, we are looking at over 4 .7 billion distinct
nucleotide sequences. And just to ensure we're
perfectly aligned here, when we say nucleotide
sequences, we're talking about the A, C, T, and
Gs, right? The raw code of DNA. Correct. The
base genetic code. And that data covers more
than 580 ,000 formally described species. So
from the absolutely smallest bacteria to the
blue whale, if a scientist has sequenced it,
it is likely sitting on a server in Maryland
right now. 580 ,000 species. That is an absurd
amount of life. But what really jumped out at
me in the source material was the speed. In the
tech world, you know, we always talk about Moore's
Law. Right, the idea that computing power doubles
roughly every two years, the benchmark for rapid
progress. Exactly. But GenBank seems to be leaving
Moore's Law completely in the dust. Yeah, biology
has its own version of that law, and it is remarkably
aggressive. Since GenBank started back in 1982,
the number of bases in the database has doubled
approximately every 18 months. So it's actually
growing faster than the computers we use to analyze
it. In some ways, yes, it is exponential growth
on steroids. Imagine a physical library where
every year and a half you literally have to build
a new wing twice the size of the previous one
and you fill it instantly. And who is building
these wings? Like who is actually running this
thing? Because managing 34 trillion base pairs
sounds like an administrative nightmare. It is
a massive coordination effort, as you'd expect.
It's produced and maintained by the NCBI, which
is the National Center for Biotechnology Information.
Which falls under the NIH, the National Institutes
of Health in the U .S. Correct. But, and this
is a really crucial distinction, it is not just
an American project. Science doesn't really respect
borders and neither does DNA. GenBank is part
of the International Nucleotide Sequence Database
Collaboration, or INSDC. That is quite a mouthful.
It is. But basically it means they are constantly
synced up with the DNA data bank of Japan and
the European Nucleotide Archive. So while the
main servers might be in Maryland, the effort
is truly global. It's practically a planetary
brain. Okay, so if the server is the brain, I
really want to know what it's thinking about
the most. One of the most fun things we found
in the source material was this concept of the
top 20 organisms. The leaderboard of life. Yes,
exactly. I want to see who the main characters
are. If we look at the organism with the absolute
most base pairs stored in GenBank, who takes
the gold medal? Now, I'm going to go out on a
limb here. I feel like humans are pretty obsessed
with ourselves. We sequence the human genome.
We're constantly studying our own diseases. Surely
we are number one on this list. That is the most
reasonable guess you could possibly make. And
you would be completely wrong. Of course I am.
We didn't even win our own popularity contest.
We didn't even make the podium. Homo sapiens
humans actually come in at number five. We have
about 27 .8 billion base pairs stored in GenBank.
Number five. Wow, that is actually kind of humbling.
Okay, so who beat us? Who is the undisputed heavyweight
champion of GenBank? That honor goes to Tritacum
Estabum. Tritacum. That sounds like... Common
bread wheat. Wheat, this stuff in my toast is
number one. By an absolute landslide, wheat has
a massive presence in the database, about 215
billion base pairs. That is almost 10 times the
amount of data we have for human beings. Why
wheat? Is wheat secretly way more complex than
us? Or are we just that obsessed with carbs?
Well, it's a bit of both, actually. Genetically,
wheat is a beast. It has a huge, highly complex
genome. It's hexaploid, meaning it has six sets
of chromosomes. But really, this reflects human
civilization's absolute dependence on agriculture.
Right. If the global wheat crop fails, we have
a global catastrophe. Exactly. We are studying
it constantly for food security, for yield optimization,
for disease resistance. We are obsessed with
wheat because our survival fundamentally depends
on it. scientifically, it gets the lion's share
of the attention. Okay, that makes total sense.
Respect the wheat. So who took the silver medal?
Who's number two? Now, this one tells a very
specific story about our recent history. It's
essentially a snapshot of a crisis. Number two
is SARS -CoV -2. The COVID virus. Exactly. And
just think about how remarkable that is. This
is a virus that functionally didn't exist in
the public consciousness or the database just
a few years ago. Now it has about 165 billion
base pairs stored. That is incredible. It really
visualizes the pivot, doesn't it? It shows the
exact moment the entire global scientific community
just dropped what it was doing and stared at
this one single thing. It really does. It shows
how GenBank acts as a mirror for scientific priority.
It doesn't just store biology. It stores our
reaction to biological crises. When the pandemic
hit, every lab with a sequencer anywhere in the
world started uploading viral genomes. So we
have wheat at number one, COVID at number two.
Who rounds out the top three? Barley. More grains.
We really are just farmers with fancy computers,
aren't we? Humans like their beer and their bread.
It's pretty undeniable at this point. Okay, so
wheat, the virus, barley. Then the lab mouse
at number four, I assume. Yes, most musculus
is number four. sitting right there just ahead
of humans. And if you go further down the list,
past humans at number five, you see the other
standard research staples. You have E. coli at
number seven. The absolute workhorse of the lab.
Exactly. You have the zebrafish, Danio Rario,
at number nine. Dogs and pigs also make the top
20 list. It's really interesting that the list
isn't necessarily the most complex animals or
the coolest animals. Like, I don't see any lions
or tigers on there. It's literally just the animals
we need. Precisely. It is a pure reflection of
human utility. We sequence what we eat, what
makes us sick, and what lives in our houses.
It's a very pragmatic list when you look at it.
So how did this all begin? You mentioned 1982
earlier. That feels like the Stone Age for computers.
I'm picturing giant tape drives in those green
tech screens. You're not far off at all. And
the origin story is actually quite surprising
because it starts right in the shadow of the
Cold War. Really? Yeah. How do you get from the
Cold War to DNA sequencing? It traces back to
the Los Alamos National Laboratory. Wait, the
atomic bomb place. The very same, specifically
the theoretical biology and biophysics group
there. There was a physicist named Walter Goad,
who is essentially the father of GenBank. I suppose
if you have the massive computing power required
to calculate nuclear blast radiuses, you might
as well use it for biology, too. That is exactly
the idea. The math of pattern recognition overlaps
more than you'd think. So in 1982, with funding
from the NIH, the National Science Foundation,
the DOE, and even the Department of Defense,
they officially launched GenBank. They must have
been tiny back then. Minuscule. By the end of
1983, they had stored just over 2 ,000 sequences.
2 ,000. And now we're at 4 .7 billion. That is
quite the growth spurt. It was a very different
world. In the mid -80s, the project was actually
managed by a bioinformatics company at Stanford
called Intelligenetics. It didn't actually move
to the NCBI until that transition period between
1989 and 1992. And I read in the sources that
this project actually helped kickstart the way
scientists talk to each other online. Is that
right? Yes, the Bio -CI or BioNet newsgroups.
It was one of the earliest examples of open access
communication among scientists. Before the modern
internet as we know it even existed, GenBank
was already fostering this culture of share what
you find. It created the template for open science.
Which brings us to the mechanics of it. How does
the data actually get into the library? Because
I think there's a common misconception that there
is some librarian of life sitting at a desk at
the NIH scanning books and carefully typing in
codes. Yeah, that would be physically impossible
given the volume. GenBank relies entirely on
direct submissions. So it's user -generated content.
Like the Wikipedia of DNA? In a sense, yes. If
you are a researcher in a lab in Brazil or a
grad student in Tokyo, you use web -based tools
like BankIt or a program called Table2Azen. You
essentially fill out a form, attach your sequence
data, and send it off. And what about the big
sequencing centers, the ones churning out terabytes
of data a day? They have automated pipelines
doing bulk submissions 24 -7. It's a constant,
unending stream of data. So say I upload my sequence.
Does it go straight to the public? Or is there
a bouncer at the door checking IDs? There is
a vetting process, but it's specific. GenBank
staff does examine the originality of the data.
They perform quality assurance checks to make
sure the file isn't corrupted and that it makes
basic biological sense. Once it passes that...
They assign it an accession number. Like a Dewey
decimal number. Think of it more like a social
security number for that specific piece of data.
It's a unique identifier so other scientists
can find it and cite it in their papers. Then
it's released to the public database. You can
download it via FTP or search for it using a
tool called Entrez. And the cost for all this?
Free. It is totally open access. Anyone with
an internet connection can look at the code of
life. However. And this is a nuance people often
miss when we talk about this. I saw in the notes
that open access doesn't necessarily mean free
of strings. That is absolutely correct. The NCBI
places no restrictions on the use of the data,
but that doesn't mean the data is completely
free of intellectual property claims. What do
you mean by that? Well, some submitters might
claim patents or copyrights on the specific data
they submit. NCBI explicitly says they are not
in a position to police those claims. So just
because you found it in the public library doesn't
mean you can necessarily use it to make a commercial
product without checking the fine print first.
That is a really fascinating legal gray area.
Okay, so we have this massive, open, rapidly
growing library. But this is where I want to
get a little critical. This is the deep dive
after all. Right, you're anticipating the garbage
in problem. Exactly. If this library is built
almost entirely on user submissions and users
are human, well, humans make mistakes. Can we
actually trust everything in GenBank? The short
answer is mostly, but definitely not blindly.
Because GenBank relies on the scientific community
to submit data, it naturally also inherits the
scientific community's errors. Let's look at
some examples from the sources, because some
of these are wild. I saw a story about a fish
that was having a serious identity crisis. Ah,
yes. The infamous case of Nemipterus mesoprean.
Which is what kind of fish? It's a type of threadfin
bream, a pretty commercially important fish in
the Indo -Pacific region. Okay, a threadfin bream.
What exactly happened to it? A recent study looked
at the mitochondrial sequences for this fish
in GenBank, specifically the cytochrome -sick
oxidase subunit I sequences used to identify
the species. They found that 75 % of the sequences
assigned to this species were wrong. 75%. That's
not a margin of error. That's just wrong. It's
a comprehensively failed test. It is a massive
error rate. And the reason why it happened is
the real insight here. It highlights how the
system... creates a feedback loop. Walk us through
that. How does a fish get that wrong in a processional
database? Imagine Researcher A catches a fish.
They misidentify it as a threadfin bream, sequence
it, and upload that sequence to GenBank. Now,
Researcher B catches a similar -looking fish.
They aren't totally sure what it is, so they
blast the DNA against GenBank to check. And they
get a match from Researcher A's upload. Exactly.
They see the match and say, aha, this must be
Mipterus misoprion because the database says
so. Then they upload their data confirming it.
So they are matching their catch against a lie.
Precisely. It creates a chain of errors. Citation
laundering. Essentially, that becomes very hard
to break because everyone is citing the same
bad data. You end up with a mountain of evidence
that is essentially built on sand. That is wild.
It's like copying off the smart kid's homework,
but the smart kid actually failed the test, and
now the entire class is failing. And it's not
just fish. There was a manuscript looking at
birds, specifically cytochrome B records, that
showed 45 % of the erroneous records lacked a
voucher specimen. A voucher specimen. That sounds...
like a coupon? In biology, a voucher specimen
is the physical backup. It's the actual preserved
bird or fish or plant sitting in a museum drawer
somewhere. It's the physical receipt. So if the
data looks weird online, you can physically go
to the drawer and look at the actual bird? Ideally,
yes. But if you don't have the physical bird
and the digital record is weird, you have absolutely
no way to prove if it is a brilliant new discovery
or just a careless mistake. So those records
are just ghosts in the machine. In a way, yeah.
They are unverified beta points floating in the
system forever. And then you have the anonymous
problem. This is the turtle subject X issue we
read about. Right. Often, researchers will submit
a sequence before they have formally named the
species. They might call it something like Pelomedusa
SBS, a CK 2014. Catchy name. Rolls right off
the tongue. Very. But they do this to get the
data out there quickly, or to support a draft
paper they're writing. The problem is, three
years later, they publish the paper, they officially
name the turtle... Pila Medusa variabilis, but...
Don't tell me. They often just forget to go back
to GenBank and update the original record. So
the library is full of books with temporary titles
that never get changed to the real ones. Exactly.
It causes ongoing confusion because you end up
with duplicate entries under different names.
A researcher might think they've found a brand
new species, but they are actually just looking
at an unupdated clerical error from five years
ago. Now, for a biologist studying turtle evolution,
I get that that's annoying. But does this have
real -world stakes? Like, what if I'm a doctor?
Let's say I have a patient with a weird infection,
and I sequence the bacteria and check GenBank
to identify it. Could this be dangerous? It could
be if you rely only on GenBank. That is the critical
warning here. For clinical identification, like
blood cultures, where a patient's life might
be on the line, experts highly recommend not
putting all your eggs in the GenBank basket.
What should they do instead? You combine it with
other more heavily curated databases, things
like Aztaxany or BB. Like boutique libraries.
Exactly. These are smaller but much stricter.
Someone has actually checked the books thoroughly.
GenBank is the giant warehouse. It has everything,
but it's messy. These others are the carefully
curated collections. That makes a lot of sense.
It seems like the tradeoff for having the biggest,
most comprehensive library in the world is that
you're just inevitably going to have some graffiti
in the margins. That is a very fair way to put
it. You need the warehouse for discovery, for
the big picture, for the exponential growth.
But you definitely need the boutique collections
for precision. So let's wrap this up. We have
GenBank. It is a $34 trillion base pair behemoth.
It puts wheat and COVID above human beings in
terms of raw data volume. It was born in the
Cold War and now it lives in the cloud, sinking
globally every day. And it is still doubling
every 18 months. It truly is the backbone of
modern biology. But it's also a stark reminder
that data isn't always the exact same thing as
truth. That is the key takeaway for me. GenBank
is an absolutely essential tool for open science.
We couldn't do modern medicine without it. But
we have to remember, it is a record of submissions.
It reflects what scientists think they found
at that exact moment in time. It is a living,
breathing, and occasionally mistaken history
of our understanding of life. Absolutely. And,
you know, it raises a pretty provocative question
for anyone listening. Well, we are currently
building the future of biology. We are training
complex AI models on this data. We are designing
synthetic life. We are hunting for new drugs.
But if this database contains millions of anonymous
sequences or mislabeled records that are rarely
updated or purged. Are we building our future
on a cracked foundation? Exactly. Are we inadvertently
baking fiction right into our biological reality?
And as this library grows faster than any human
can possibly read, doubling every year and a
half, how do we ever hope to go back and clean
it up? We might just be creating a web of knowledge
so complex and interlinked with tiny errors that
we can. literally never fully untangle it. It's
a very distinct possibility. Well, on that slightly
existential note, I'm going to go look at a sandwich
with a lot more respect from now on. All hail
the wheat genome. Number one on the leaderboard,
number one in our hearts. Thanks for joining
us on this deep dive into the library of life.
We'll catch you in the next one.