So, ladies and gentlemen and Charlotte, thank you very much.
It's fantastic to be here today when this new building is going to be out.
So that's a real privilege, but also to to be here and to see Charlotte, who you use a Ph.D. student now is head of department and obviously thriving.
And I had a lovely lunch with some of her students and post-docs, so that's that's really great.
But also being asked to give the Florence Nightingale lecture is quite quite an honour.
Actually, I got you get these emails. I thought the Florence Nightingale lecture Wow.
Sounds really, really good.
So of course, I did a little bit of homework about Florence Nightingale, and Charlotte's already told us a little bit about her.
When she left, she lived till she was 90. She was a social reformer and a statistician.
Of course, I have no idea that she was a statistician before.
And clearly she cared about people as being a founder of of of nursing.
That was really important to her. She wrote very much and effectively what we would now call PR.
She didn't. It wasn't called PR then, but she wrote about medical knowledge using simple English.
And I think this is a message for all of us as scientists to try and use simple English to explain to people what we're doing.
But also, she helped to popularise graphical representation of statistical data,
which in this I'm sure those of you will know this is some a letter that she sent to Queen Victoria.
I can't imagine us sending letters to the Queen even today,
but she sent it with these sort of charts showing the results of the causes of mortality in the army in the Crimea.
And what this shows beautifully, I think, is that actually the causes of mortality from wounds here was very small,
whereas the cause of mortality from infectious diseases. The great part was much, much bigger.
And it was that that need to be addressed as much as the the wounds and the sort of surgery that went on.
And so as somebody who loves data myself and also loves presenting graphical presentations of data,
I was really quite delighted to find this because it really I think it's a suitable it explains it.
Explain to me why this lecture would be called the Florence Nightingale lecture.
So actually, I'm not going to do exactly what I wrote in the abstract.
You'll be pleased to know I'm going to say a little bit.
What I wanted to try and do was talk about how bioinformatics is, how I've been involved in bioinformatics through my career.
And that is like a history, a little bit of structural bioinformatics and then show how this work then feeds into the genomic
medicine that I think will change all of our lives and is perhaps relevant for this sort of lecture.
So I started out, as Charlotte mentioned, looking I was a physicist.
I came to the National Institute of Medical Research knowing no biology whatsoever,
and my project was to study this little molecule, which is an I.D., which the scientists in the audience will know.
The biologist is a critical coenzyme that we all have and we all need.
And it was my job to do some experimental work wet and dry, but also to do some computational work on this tiny molecule.
So for the chemists amongst us, this molecule has 10 rotatable bonds now compared with the protein.
It's tiny, but I was in this National Institute of Medical Research.
I was the only there were statisticians there because there is a history of the long history of statistics in medical scientists.
But there was nobody else really doing computing and the computer room was up in the loft.
It was a little room about the size of my office now, and I've not got a big office, I should say.
And up there, we had some, some graphics and I was trying to do computations on this molecule,
and it became very clear that actually this was quite difficult to do.
There was nobody else in the whole institute doing this sort of stuff.
There wasn't a discipline of bioinformatics at all.
It was just, you know, I was doing it,
and the great thing about actually this computer was that he'd had to screen two screens and I could look at these molecules in three dimensions.
Now for, of course, the young kids, they're used to doing three dimensional things on screens.
But then it was really fun, and I could programme the potential.
There were 10 potential images and I could programme them so that I had one for each of these rotatable bonds and I could make this this protein.
This molecule change its conformation just by trialling these little potentialities.
Now I made a big cow. You know, you learn in science that you go wrong sometimes.
What I did these twiddling and I thought that I would minimise the energy by hand.
This was my idea. Ten variables. I thought, no problem.
I'll just minimise it by hand. It took me six months to write the programme to allow me to do that.
It took me about two minutes to realise there was no way I could cope with ten variables in my head.
It's just too hard to do it. So, so anyway, I learnt a lot about sort of the computing.
But then I came to Oxford and I was in the zoology building, which was then beautifully white and and we we were up on on the top floor here.
And this was the time when David Phillips and Dorothy Hodgkin were beginning to solve the structure's proteins.
And I came actually to this is rather ironic.
I came to be the systems manager of the computer. Now anybody who knows me, I hate systems management.
And this was a complete disaster from one perspective, but from another perspective, I began to to learn about protein structures.
And you can see just from these.
So for those who don't know about protein structures, they are long molecules that fold up in three dimensions to create these beautiful things.
Some of them are very symmetric. You can see that one up there so that they rather like flowers.
When you look at them, they're beautiful things.
And we we spent during this time when when I came, so I started my Ph.D. sort of here, and there was only one structure.
And about this time they created something called the protein data bank,
where everybody who sold the structure around the world deposited these structures
into the data and AI systems manager used to get tapes that were about this big.
With these new molecules on every, every three months, it was we'd get a tape with the new molecules on and you can see that during my time.
So I was in Oxford in the seventies, basically.
And gradually, the number of structures that we had gradually increased.
And of course, it was a big event. We'd all go and look at nature when a new structure came out because we didn't have many to look at.
And so looking at those was was fantastic and we began to learn how to handle them.
But actually, looking at six is very different from looking at one hundred and fifty.
Today we have a hundred and eighteen thousand entries in the PDP.
So during my academic lifetime,
the number of structures has really increased from almost nothing to this vast body of knowledge that we have to handle.
And we have to develop the computational tools to deal with those.
And when I started, we were very keen. Being a physicist, of course, physicists think they can solve all these problems.
And the idea was that you could take the what the amino acid sequence, which is just a list of letters,
as you can see here, and predict how big show, how it folded into these wonderful three dimensional structures.
Now that problem is this problem that a three year old really can understand.
If you give them a shoelace and say, wind it up so that it goes to wall structure,
they would understand that even today we are some way away from being able to answer this question.
So the really hard question of doing this AB this show without previous knowledge is really hard.
And that was what drove really much of my research during the seventies when we looked at all different aspects.
And this is when it came sort of statistical because most of the most solving one of those structures generally took.
Originally, it would take five years or 20 years, even for somebody to solve just one protein structure.
As you can tell from the numbers today, it doesn't take that long. It's really quite well, still difficult sometimes, but what we did,
the other people perhaps didn't do was instead of just looking at one structure, I was in the lab where lots of structures were coming up.
We looked at many structures and began to do more statistical analysis of what was likely, what was unlikely we'd need at our random distributions.
So we have this different approach to looking at these structures practically.
And so salt bridges, when I started, I saw salt bridges, which are plus minus interactions between parts of the chain.
I thought those would be the thing that really stabilised the protein completely wrong.
They just they lie on the outside and they provide stability, but they are not the main contribution to stability for proteins.
And so we looked at lots of different things and did analyses that still are really used today in terms of,
I mean, Charlotte mentioned the beta turns that was that's a little beta term,
though that's a little tiny, little part of a structure,
but they're very common in proteins and they help the same to fold around into into a positive structure.
So we looked at all these details. But increasingly, as we got by this time, we probably had 300 fold.
We began to think, Well, actually, we need to find a way to classify these because we can't cope with them.
So I started off in Oxford, I had a blue book, and every time a new structure came out, I'd write in the book.
And of course, as a physicist, I wasn't really conscious of evolution and the impact that that has on proteins.
And these proteins can be called something completely different.
And yet look almost identical. And that was really confusing to me.
I thought, why does this all the other way round? Why does this cytochrome P450 not look like cytochrome b?
You know, why aren't the same? They've got the same name. They should look the same.
They look completely different. And so we have to find ways to really think about things.
And this is almost identical to what people did with plants or with animals in
terms of trying to classify them into their different species in some way.
And so we develop the thing that Charlotte mentioned cats, and this was very much with Christina Ringo,
who still looks after at UCLA and has made it really excellent, which provides a classification of these structures.
And this is essential and in applied statistics, of course,
this is always you have to get your data set right because if your data sets wrong, everything that follows is wrong.
And so deciding what you include and what you don't to generate a random distribution is just critical.
And so this was what allowed us, and that's why we started doing it.
But in fact, it's been used for many other things,
and we developed our classification of structures and we could see which structures were more common and which structures were less common,
et cetera, et cetera. And from this also which is interesting, we began to develop validation methods.
So when when people put structures into the protein data bank,
originally it was done by hand and it was all hand curated and during about the ninth, the late eighties structures were sold.
That proved to be wrong.
Now this this was amazing because in structural biology that never happened and still actually now doesn't happen.
Structural biology is fantastic because you know when you've got it right or wrong, fundamentally, if you do do it properly.
And so what we found was that we could take all of these structures and join them together and develop parameters that
would tell you whether it whether a given new structure looked like the previous structures that have been deposited,
whether it have the right stereo chemical properties.
And so Roman Laskowski, who's in the group and still works with me, he developed this tool called pre-check.
And in fact, wrote the paper, describing it in the Journal of Applied Crystallography that well-known journal that nobody has heard of,
and this is one of the top 100 cited papers.
So you don't have to publish it in the top journals to get citations if people are going to use the stuff, that's that.
But this was built on a statistical analysis of those data.
And and so when you get new datasets, being able to really understand, you know, mean values, deviations, et cetera, et cetera.
Is really important. And then we moved on to look at function, but I'm not going to go into that because really there isn't time to do that.
But I should say that during this time, up to about 19 nine, well, even up to nineteen ninety five, there were never any jobs for bioinformatics.
People advertised. There was never, you know, people go, you know, when you're at this stage, you go through nature looking for adverts for a job.
There were no jobs for.
In fact, when I was made a professor, the person who was doing the the the, you know, whatever it's called, where you say, Oh, congratulations.
Said, Well, of course, it works in what used to be known as the dust bin area of crystallography because this was the computational biology part.
Now, in fact, I think probably the bioinformatics world is actually, I would write probably five times larger than the structural biology community.
It's not a discipline area, it's a very powerful and very important, and I honestly think it really is at the heart of biology.
So this I this was this is what I was doing for many, many years.
But it became apparent to me that actually understanding using this structural
data to understand how proteins in the body work and how diseases occur,
you don't just need structural biology data, you need many other sorts of data as well.
And because I've been involved with the PDB, the protein data bank, I've been on their advisory board.
I've done lots of things and we we because of protocol and doing things like that, I was deeply involved in that and it frustrated me that.
When these structures were deposited, there would be many errors in them.
And this is part of what Project sought to clean up.
And so I realised that if this is done, what then the whole community benefits from it.
It's not just me or that person who solved that structure.
If you clean it up, if you make it as good as it can be at once, that everybody else can benefit from that.
And so at that point, I have been in starting up Mill Hill, Oxford.
But back in UCL, I was asked if I would consider going as director to more Ebi and Eby has as its
chief mission to provide biomolecular data of all sorts really to the world.
And because of my experience with the protein structures, I really felt that this was a job worth doing was very important,
and it was also something that I knew something about, although only in one little part of biology, not in the whole part.
And so I went off up to Cambridge and basically what the key the USP of of the FBI is that we essentially labs around the data around the world,
send that data, we archive it, we classify it, we share it with other data providers and analyse it,
and then we provide tools to help researchers use it.
So this is actually when I went, there were about one hundred and fifty people at TBI, but it was clear this was going to be key.
Now this shows all the different types of data resources that we are responsible for.
And believe me and you can see here we have our structures and we also have the protein sequences here.
And when I got there, there was the European nucleotide archive.
It was called and more than an array xpress, and that was all. Now there are over 40 different resources that are looked after by the FBI.
And the reason, as I said, that I thought so.
The idea of this arrow here is that we go from the molecular one end to the system to the whole organisms at the other end.
Now, at the moment, we really only handle properly, I think the molecules and they're not perfect by a long way.
And we were having a discussion at lunch time actually about the PDB and its pluses and minuses, but they're relatively under control.
If we think about the whole organism data or even the cellular data that is buried in the literature,
and we really don't yet have a good handle on that or good data resources that tackle that.
And so over the time that have been assembled, you can see we we have the literature, which is critical.
We have pathways and interactions. The chemical biology is important.
The expression on the metabolomics and proteomics, all of those now are part and parcel of us trying to understand the molecular composition of life.
One of the things that's happened, of course, is that during this time,
during the time I've been there, all of the data of increased very, very rapidly.
And in fact, if we compare our data with what is at CERN, which everybody always thinks is big, big data.
So instead, they store about 30 petabytes of data a year.
That doubling time is about two years, and they effectively established a whole network of futures across the world to analyse this data.
Our data are very different. They're not coming out of one big LHC in Geneva that they're developed by many people all around the world,
all the life science community which is running into the millions around the world.
And so our data are now large. We have now sixty five petabytes of storage.
The data aren't all the same. They're heterogeneous. I showed you all those different data resources.
They're all different, and that makes it much more complicated to handle. Our doubling time is about a year.
And obviously, this is a joint enterprise by everybody around the world.
And if we look so this now, this is a log plot.
This is the number of bytes here going up. So that's 10 pets a.
Right here, we've got from two, six to 216, and the these lines represent doubling every year, so you can see that the sequence data,
which is by far the largest by long way, and now the human sequence data that is about along the doubling every year.
Not quite. But something like that. You can also see that metabolomics, although it's so this is looking at small molecules and their distributions.
So if we look at the small molecules, that is increasing very rapidly.
But of course, because it's a log plot, this is tiny compared with the big data.
And the other thing that's happened. Well, actually, maybe I'll go back to that slide.
Is that the way that biology is done has changed radically with with big projects.
And this is just three four big projects that Ebi is being involved with.
And these are global projects that happen around the world. So they are projects that that generate massive.
So this is looking at marine microbial biodiversity.
This is the Big International Cancer Genome Consortium. This is ENCODE, which was looking at expression data, regulatory data.
And Tara Oceans was looking at organisms around the world.
And so this is. The way that the science is done now and the need to create these communities that use all of this data really becomes important.
The other thing is that as part of this, we gradually started to get information that is relevant to medicine.
So again, when I first went to eBay, I can remember saying, Oh well, the big frontier is how we engage with medical data.
Fifteen years ago, I have to say that the medicks really weren't interested.
It was too far away. It was too big a jump. Fifteen years later, that has completely inverted.
And now there is a very clear recognition that these data are relevant to to to what goes on.
So big data we we have big demand 11 million users a day to the website.
Five million unique sites. 9.2 million jobs a month, etc. So it's a lot.
It's a big setup. This infrastructure, and if we just look, this is going to work.
See whether it works. So this is alive as we speak,
usage of ebi data resources around the world and these maps that show the colours actually represent the different resources.
So you can see who's using what is any time and what you can see here.
So now actually, this year for the first time, our biggest useless are China.
And they use 20 percent of the hits on the website are from China.
And this has changed completely because when I started, there was almost no nothing.
So this has changed completely. And you know, you can't see, well, the US.
Yes, it's obviously changes with the time of day you can guarantee.
So you can't. Europe is obviously a big splurge here, but usually Australia goes to sleep round about now,
so there won't be much going on in Australia and the Americas wake up.
But I remember I was giving a talk in Iceland. And of course, everybody always looks at their place when you when you show this sort of a map.
And I said, Oh yes, Iceland uses it. But of course there are no uses and I thought, Oh no.
And just as I was speaking up came a hit from Iceland. They're fantastic.
So people all around the world really use the data and that I think that is fantastic because it's kind of that's what science is about.
It's about sharing the data and building on everybody else's data as well.
So bioinformatics. Effectively is able to bridge different biological disciplines,
and that's very important because it brings data together from different aspects of biology.
And increasingly, you can see that we go into it.
So most of the time the people, computational people have been down at the molecular or certainly in the structural biology,
obviously in the molecular end. But increasingly, the need to model the cellular,
the organ now at the organism scale is going to become more and more important, and we will get more and more data about that.
And this is a terrific opportunity where people who can do applied statistics honestly
will be king because it really makes a difference being able to handle all of this data.
And just to emphasise the importance of this, if I could say that for EMBL.
So EMBL is a European organisation and we have five year plans.
As Ian is wont to say, with the last one of the last, you know,
the Soviet five year plans, we still have our five year plans of what we're going to do.
But the next indicative scheme, which is what this five year plan is called, is entitled Digital Biology.
And I think that and of all of the scientists, assemble.
Now, about half of them spend on average half of their time doing computational biology and not just the experimental.
And so the growth of this bioinformatics computational biology is really important.
And you can see we have the experimental technology, which obviously is critical generating the data.
But of equal importance is the computational approach to these things.
And clearly, if we have better methods, we will get better biology and ultimately better medicine as well.
And so this is a list and I'm not going to go through them.
You can read them all of areas where which are sort of hot, where the need for good computational biology and bioinformatics is very, very clear.
So what I have not mentioned so far is the human genome.
And so this is the sort of second of last part of the talk.
I'm just talking about the impact that this has had.
So I should say when I was in Oxford, I drew a graph of the number of protein structures versus the date,
and you can predict how many structures there are. And actually, that prediction was pretty accurate.
You know, you know that you're going to get better at doing it, you're going to get quicker and everything.
The one thing which I didn't in any shape or form see was the fact that we would
have the whole human genome determined and the ability not just to sequence humans,
but to sequence a human and look at that and that the impact of this, this is just the stats for this is enormous in biology.
And I think it's only only just started just as this is sort of a fun thing.
In 2003, the cost of sequencing a genome was equivalent to that.
The price then of a house, the most expensive price house in London in by 230, it was the price of it was an Arsenal season ticket.
Now is probably the price of maybe an Oxford City season ticket.
I don't know. I'm not sure how much those costs, but clearly, actually most of us could today afford to have our genome sequence,
and that is a huge total change in perspective.
And of course, it's we all have basically the same sequence ninety nine point nine percent.
We've all got the same sequence,
but it's these little bits where we different these odd letters in the sequence that make us different from one another.
And so understanding this human variation is a huge and inspiring is what makes us human and what makes us different from each other.
And so there are many sequence efforts around the world of generating data that.
Some of which ends up a TBI. To look at all these different.
So in the UK, probably we have the biggest.
The 100000 genomes, which started just last year on campus, we have the building, the sequencing factory and it is like a factory.
The ground floor is taken by Illumina, who will sequence these 100000 genomes, and these are supposed to be all done by 270.
So it's very soon.
I mean, it's really very quick, and the only reason it's possible is because the technology has changed so radically over the last few years.
And of course, the problem isn't now to determine the genome, which, as I say, I never even envisage, but it's actually to decipher what it means.
And that is a huge challenge for the whole of biology and indeed for the whole of medicine.
Because when it goes wrong, then you can look to ask why you have a certain preference.
So no, I don't think there's going to be time for all of this.
Perhaps I could say that the the coding, the protein coding variants.
So what when you get this variation, one variation here, you can say for some of these variations,
not most of them, but some of them will occur in a protein structure.
And so how does that variant lead to a disease?
What's the effect?
So this is a summary of the way that these variants that where there are causative variants that cause or have an impact on the disease.
So here we've got some of those variants lead to a translocation.
So this is like a protein chopped in half with the blood coming out. So this is Roman's pictures, I should say.
So you can truncate the protein, you can have a variation in the middle that causes the thing to disrupt and unfold.
You can have a variation at the heart of the enzyme, the residue, and that can cause the enzyme not to work.
You can have a variation. There are metal binding site so that the metal doesn't bind anymore, and that causes a problem.
You can have variations that stop a DNA binding protein being able to recognise
DNA or protein protein interactions if you have a variant in the middle.
The protein that can't recognise its partner and that causes problems.
Or here you can have something that's affecting the substrate binding.
So this is a summary of some of the variants that we all carry and what their impact might be.
Now the question is, we all have individually, I think somewhere here, we all between any two individuals.
There are about, Oh gosh, no, I'm going to get this this number.
I always get these numbers wrong. There are about 10000 different DNA bases.
Some of those will be in proteins, but not all of them.
And so the question is how the one some of these variants, in fact, most of them have no effect.
They don't cause the disease so we can carry them around quite happily.
We can pass them on to our children. No problem. But others of these variants really do lead to disaster.
Or that disaster, of course, is only a modulator disaster because if it was a real disaster, then the foetus would die.
If you pass it on to your children, then the child has to.
It's only modulating the effect. And this is one of the big challenges that actually the variants we see are often rather gentle variants.
They just modulate something. And so identifying what's going on becomes quite a challenge.
So, oh, I just conscious of the time here.
Perhaps I could just say, I'll go through these very quickly.
One thing that people have done and we've done is to compare those variants,
those changes in our sequences that are associated with diseases and those that are,
if you like natural mutations that we all carry and you can do that by looking at this new data
and you can generate matrices that tell you how often different amino acid changes occur.
And then you can compare those matrices with those that occur for disease associated ones.
So I've not got time to go into the detail what you see.
You can then ask the amino acids, the residues in the protein.
You can run code to them according to how likely they are to change.
And you can do that for the natural variants, i.e. the ones that don't cause diseases and the ones that do.
And what you what you see is that there's a big difference between these two.
So some amino acids, when they change are very often associated with diseases.
Others are not. This is a purely statistical way of looking at it, and you can derive quite good on average values of what's good and what's bad.
And the predictions are really accurate about the eighty five, eighty seven percent, they're pretty good predictions.
The problem comes and in particular,
what you find is that the the disease associated variants occur in those residues that are best conserved during evolution.
So if you have a residue that's really, well, conserved and then you change it, that is likely to lead to a disease of the opposite end.
If you have an unconcern residue, then it doesn't matter if he changes or not.
So it's more or less what you'd expect. And we did a whole analysis of structural data, but there really isn't time to talk about that.
So I'm not going to go into that.
But I wanted then to think about this is if you like basic biology, the question is, is this really relevant for medicine?
And what are the challenges?
How can we move from our molecular knowledge to the medical challenges which are often more organismal rather than an individual molecule?
And where will this sequencing really have an impact in the clinic?
And so you can really immediately identify, Hey,
I've got five areas where the sequencing will begin and is already beginning quite clearly to have a major impact.
The first, of course, is understanding the molecular basis of disease.
As we understand more about diseases, we stand a chance of being able to do something about it and solve it.
The human variation and disease risk, which of those variants that we hold is likely to make us susceptible to cardiac arrest or diabetes or whatever.
You can begin to estimate this. The cancer genomics.
We all know that cancer is a genetic disease, which because we get changes to our DNA, we get those variants.
How does that happen and the work going on in the cancer,
some of it on the campus is phenomenal in terms of understanding how these cancers evolve and
what we're going to have to do to actually develop therapeutics that can really work long term.
Another amazing use of the sequencing is tracking the source of infectious diseases.
And by looking at the the genome of the bacteria or the virus, you can actually watch from this,
the data from the sequence data, how the bug can transfer from one back to another or from one war to another.
So you can really begin to track this infectious diseases and then you can say,
what can the structural data really help us to explain the causes of disease?
And the answer is often is the case is that to really ask these questions, you need to be very specific where you look and what you're looking at.
And this is a study that was done across the UK led from the Sanger Institute
funded by the Wellcome Trust in trying to decipher developmental disorders.
So these are children who often before the age of five will present to the hospital with some sort of problem.
And in fact, they did a study of just over a thousand children who had were identified as having one of these developmental disorders.
They did screening and they did whole exome sequencing, and they sequenced these children,
and these were the sort of disorders that they presented with.
So this is lots of difficult disorders. The problem for these children is that they present and they are not diagnosed.
So some of these children.
When they present when they're five, they're not diagnosed until they're 12 or later, and this can be really challenging not only for the child,
but also for the parents because they get passed from one part of the system to the other part of the system.
And so they collect data.
So the reason this is such a very nice dataset is that they collected data from the two parents on the child or sometimes those siblings,
and they could get all the data. The average age of the child when they first presented was about five.
So with with that data, for those children they had,
mainly these were the disorders that these children had so varied, they had very many different sorts of disorders.
What they were able to do from the sequencing was to identify in these children different.
So, so the key thing about doing the parents is that instead of 10000 variants, you only have 100 Nova variants.
And that cuts down because one of the major challenges, which still is really relevant, is that you can't identify which is the causal variant.
You can't say what's going on. And in this dataset, instead of having 10000 variants to look at, you only have 100.
And so they looked at this and what was they found 12 novel recurring genes.
And these are genes that were mutated in more than one child.
Now this seems to me like a fairly hairy way of going about things.
So you've got this mutation twice. But actually, the probability of that happening and not to have some cause is very low.
And so they can look at these recurring mutations and try and understand it.
So we looked at one examples of these.
So this was a protein, this protein and it and there were four different variants that were observed in different children.
Each of these was a different child. This protein is quite a long protein, and it's made up of these WD 40 repeats.
So it's this I said, like flowers and symmetry. This shows the the the structure of the protein, and it's basically got these repeats in it.
Now, when you look at these variants, those are where the variants occur.
This is what's called a sequence logo of the variants, and you can see that there are some places where the letters are big.
It means that residue is conserved. So this h histidine is conserved all the aspartic acid the day or the trip to fund the W. It's D.H.S.
W. And it turned out that those variants occurred there.
So this is now going back to our protein structures in this structure.
You have this beautiful network of hydrogen bond that ties together.
This the the auspice, serine and tryptophan.
And that link which occurs up here, is actually the thing that holds the whole of that repeat together.
And so it's a very nice example where the structure is able to explain these variants.
And so we saw variants in the ASP and in the in the histidine here.
And when we look at them on the protein, we see that they're all at the top of the protein, which is actually a recognition.
This is where it recognises another protein and that the natural variants are at the bottom.
So it's segregated very, very nicely. So let me just say that also, I'll skip that.
So this basic research can enable you to understand more about the variants and to link in some way,
at least make the first step between the sequence and the variant and how that disrupts the protein.
The question then, of course, is how that disruption goes on to create the the disorder.
You know why? Why does that protein being disrupted, lead to developmental delay or run or psychiatric problems?
So this there's still a long, long way to go.
So let's just step right back from this and say, Well, suppose we sequence every child born in Europe that's not going to be too far away.
I don't think I would think given a fair wind, it would be within the next 10 years that we will begin to sequence every child in Europe.
I hope so because it's getting cheaper and cheaper and we'll get.
Obviously, what this is going to do is generate huge amounts of medical data, so we're going to get individual patient data, it's closed data.
There's a there's a privacy constraint to it. Right.
So we have different sorts of data and obviously we need to bridge between our biomolecular data, which is open, free for everybody to use,
etc. And this very private clinical data,
which nevertheless needs to be shared so that we can really understand what are the causes of diseases and develop methods to improve it.
So perhaps I could just finish by saying if we think about the 100000 genomes as it is at the moment, the genomic medicine census,
they send their samples to Illumina to be sequenced and then all the data goes
into the dataset and it's actually going to be the interpretation of this data.
It's here that we're going to begin to understand what's going on and be able to then feedback to the clinicians in some way.
And this is the new part, I think, and to my mind, this is at the heart of genomic medicine because it will clearly,
if we don't get that part of it right, then none of the other part can follow.
It's not enough, but it's the beginning.
And so just to finish, then I think it's clear that in biology, the life sciences have developed a substantial data infrastructure,
of which Ebi is the European part of the European, and there's also in the states and in Japan and increasingly in China.
So we have many public tools and methods to analyse the data carefully.
It's also clear to me that genomic medicine will lead and even that BigQuery is going to be more complicated.
But we'll need new methods to handle and integrate the data so that we can make sense of the genome data and the outcomes from that.
And it seems to me that this endeavour is really only just beginning for the young people in the audience.
I think it's a fantastic area to work in, and if I was starting again, this is exactly what I would do.
So thank you very much.