these two classes by inspection of the gene predictions about three quarters of this group appeared to be pseudo genes and about 30% of these were pseudo genes so that's actually a quite sobering percentage of the total set so just in terms of this it does suggest a way though of getting around the problem we can capture a lot of these pseudo genes depending on homology by inspecting members of local gene clusters carefully but we have to be careful when we deal with these local gene clusters because in there seems to be a lot of the unique biology there are 25 clusters in mouse that appear to be specific in the mouse and they involve proteins involved in reproduction immunity and host defense some of these are the genes undergoing positive selection again a clue as to where new genes may be coming from so we have to be careful to maintain these without while we're trying to get rid of pseudo genes so anyway basically we're going to try to take advantage of the fact that processed pseudo genes can be eliminated by conserved centenie processed pseudo genes these duplicated forms we can take advantage of relatively rapid divergence and then also we can look for single exon genes is where multiple exon forms exist so we carried this out on chromosome 7 we found clear evidence based on CDNAs for 605 genes and then using these criteria using gene prediction programs and filtering them based on the suggested criteria we ended up with another almost 550 bringing the total genes to almost 1200 for chromosome 7 we then turned around and searched intergenic regions for evidence of protein homologies and pseudo genes and we found a remarkable number some 940 so almost as many pseudo genes in this chromosome as real genes at least by discount so we wanted to have some kind of check on this to see what was if we were on target or not and so we took advantage of the fact that there are synonymous and non-synonymous changes synonymous changes don't change the coding potential of a gene whereas non-synonymous changes change the amino acid and will change the function and so one should have a neutral rate and the other will affect function so when we applied it to 7 only 5% of these 1150 genes appeared to be likely pseudo genes by this statistical analysis by contrast of the 941 pseudo genes that we had again by this kind of analysis 97% of the pseudo genes so we had actually done a reasonably thorough job of both getting real genes and getting rid of pseudo genes I think this is promising for the future and indeed when we looked at these pseudo genes 80% of them appeared to be processed pseudo genes these are made by this reverse transcriptase mechanism 20% came from duplications interestingly it also appears that genes that are in these duplicated regions of recent duplication that Eric alluded to appear to be under relaxed constraints so anyway I think that this is a very powerful benefit of being able to compare sequences I think you'll hear from further speakers more on the same theme but with this and maybe one or two other genomes I think basically we'll have a complete and thorough parts list for human and that'll be a very valuable thing for the future you've seen this already it was with all the features of it these are, I should acknowledge all the members of the mouse consortium who participated in this and the participants of an Eichler and Latina Hillier as well as all the folks at WashU were critical in the in the humans chromosome 7 analysis and of course all the data is freely available thanks next speaker is also on the subject of comparative genomics it's Eric Green who's the scientific director here at the NHGRI moved there after his period at WashU and has made many contributions to the genomics well today we celebrate a hallmark event of course in the history of science with the completion of a finished human genome sequence but as we marvel at our accomplishment we also look to the new scientific horizons that are brought about by this informational resource and one of the many areas that you've certainly been hearing about in this session that we're going to pursue is the comparison of the human genome sequence to that of other organisms as you just heard about for the mouse and Shirley Tillman alluded to and certainly Eric Lander talked about if I can have the first slide but really it's appropriate to pause and ask the question and thinking about new frontiers in comparative genomics is why sequence additional genomes especially beyond those that have already been completed by the human genome project and I think there's several reasons that can be cited for this first, sequencing other genomes provides intrinsic biological insight about other organisms for the experimental models the data facilitates research with those organisms each with their distinct anatomical physiological and developmental features but there's also great interest in gaining insight about the genetic blueprint of livestock and also companion animals the second reason is that comparative sequencing provides tremendous insight about the basic underpinnings of genome evolution in the case of comparative sequencing you gain insights about the mutational mechanisms and DNA repair the acquisition of new functions through evolutionary change and of course the molecular basis of speciation but I think most important for this session is the fact that comparative sequencing is key for interpreting the human genome sequence and because of its importance I really plan to emphasize this last area in my talk today so shown here is one ten thousandth of one percent of the human genome sequence the next great challenge in genomics is to identify all of the sequences in the human genome that are functionally important in essence this is going to involve using computational tools and experimental tools to essentially highlight all of the sequences the subset of the sequences that have a functional role first we need to identify the one to two percent of the sequence that corresponds to exons but far more challenging we need to go in and identify the larger amount of non-coding sequences that function as regulatory elements to control gene expression that mediate chromosome dynamics and in fact that function as yet undiscovered ways indeed I think it's really important to make a distinction between functional elements that do and do not code for protein because the paths towards their identification is really so different so for simplicity I will make the general claim that coding sequences that is genes are relatively easy to identify this is because we mostly know what we're looking for we have complementary data sets such as ESTs and full length CDNA sequences available to us and because of our understanding of basic gene structure we have ever improving tools for using computational methods for doing gene predictions and so in short while I recognize there's a huge challenge as Bob just alluded to finding all the genes I'm not particularly worried about our ability to find all of the coding sequences in the human genome in contrast we have just a huge task ahead of us in identifying all of the non-coding functional sequences because these are very hard to identify why well first of all little is known about actually what we're looking for in the first place there's virtually no complementary data sets that are available to help us and our general ignorance is basically left us in a state where we don't have very good computational methods that are available for actually identifying sequences among non-coding DNA that are functionally important indeed the conclusion from my talk which I'm happy to give now is that a major role for comparative sequencing is going to be the identification of functionally important non-coding sequences in the human genome and I now want to provide you some evidence to support this statement well recognizing the value of comparative sequence analysis the generation of the human genome sequence was quickly followed by the generation of the mouse genome sequence and detailed comparisons have been performed providing tremendous insight about the mammalian genetic blueprint as you've heard about from several earlier speakers I really find several findings from the initial comparisons of mouse and human particularly relevant for today's talk first 40% of the human genome sequence will align with the mouse genome sequence but as you just heard about only about 5% of the genome is estimated to be under evolutionary selection implying that about 5% of the mammalian genome is functionally important of course we don't yet know precisely which bases constitute this 5% that functionally important 5% is divided up into about 1.5% that is protein coding and the remaining about 3.5% that is non-coding the challenge thus becomes to identify this 5% from among this 40% identifying the 1.5% which is protein coding which I've already told you was relatively easy but then identifying this 3.5% which I have already argued is actually going to be quite hard well a powerful approach for tackling the challenge is to identify sequence from additional species especially those selected at distinct evolutionary positions and then look for sequences that are in common sequences that are shared over large evolutionary distances are likely to be functionally important and indeed efforts are already underway to sequence additional vertebrate genomes such as the rat and zebrafish and chicken and chimpanzee and cow and so forth but one limitation is that only a handful of additional genomes can be sequenced at least for the foreseeable future mostly because it costs upwards of about $100 million or more to sequence a vertebrate genome using today's technologies well the comparative genomics program NIH is interested in generating data to address the question what are the best combinations of species to sequence for identifying all of the functional elements in the human genome and rather than tackling entire genomes our program is sequencing limited or targeted regions of the genome allowing sequences to be obtained from multiple other species in turn sequence comparison can be performed with an evolutionarily diverse more evolutionarily diverse set of species providing quick insight about how to use such data sets for finding functional elements we are now sequencing literally dozens of different targeted regions across the human genome in some cases in greater than 20 species I want to briefly show you some illustrative data generated for this region of human chromosome 7 which contains these 10 known genes the one I want to highlight is the famous CFTR gene the gene mutated in cystic fibrosis this gene was identified in 1989 and since then it has become one of the most intensively studied genes in the human genome with hundreds of investigators pouring over this gene for the past 13 plus years and publishing well over a thousand papers with so much work performed on this gene one would think we now know everything there is to know about it including all of the regulatory elements and all of the functional elements embedded within it indeed this is far from the truth so to illustrate this point shown here is a 30 kb interval within the CFTR gene encompassing exons 11 and 12 along with the intervening intron shown here with the various geometric shapes they are just simply corresponding to different repetitive elements several years ago we sequenced this region in the mouse genome and compared it to the human sequence which was generated in partnership with the Washington University Genome Sequencing Center shown here is an output which you have actually seen a version of it is a sequence with a comparison tool called PipMaker developed by Webb Miller's group with the middle section simply blown up here at higher resolution PipMaker generates pair wise alignments between two related sequences and then generates a percent identity plot or a pip that depicts the positions of the resulting alignments as along the length of the reference sequence in this case human as a function of the percent identity from 50% to 100% so in short as you look across this interval and you see all of these little dots and lines they simply indicate regions where the human and the mouse sequence are highly similar ranging from 50% to 100% identical immediately notice the striking sequence conservation in the two exons which is exactly what you would expect but in addition if you look in the intronic region you immediately see the point made earlier that just under half of the human and mouse sequence aligns with most of the alignments being within the non-coding intronic region now presumably some of these intronic alignments correspond to functional elements but how do you find the functional elements amongst all of the noise clearly one vertebrate species in addition to human is not sufficient but what about using multiple other species well we have now sequenced this region and multiple other vertebrates so primate sequence such as from chimpanzee and from baboon reveal the very high sequence similarity in humans as expected well over 90% across the interval for both species various other mammalian species such as cat and dog and cow and pig and rat give patterns that are more like the mouse with varying degrees of sequence conservation across the intron moving beyond mammals to a bird the chicken and a fish one encounters very very different pattern with the intron now essentially being devoid of any sequence conservation indeed the only consistent feature among all of these vertebrates happens to be the presence of sequence alignments within the two exons as you can see the patterns in sequence conservation among the intronic regions of the mammals certainly appear quite chaotic and quite noisy nonetheless we sought to investigate whether we could use these sequences as a group to try to identify discrete regions in that intron that are highly conserved among multiple species towards that end a postdoctoral fellow in my laboratory Elliott Margolies working with Matthew Blachette a postdoctoral fellow in David Hauser's group are developing algorithms for identifying highly conserved regions using multi-species sequences specifically they take all of the sequences from this various species and then through alignments in various sophisticated analyses they can detect actively conserved sequences and then in a final distillation step they identify those sequences that are conserved in multiple species we call these highly conserved sequences MCS for multi-species conserved sequences well by analyzing sequences from all of these species we can identify a small number of MCSs within the interval for example MCS is shown now in pink overlap both of the exons as expected another MCS just happens to sit right there interestingly a little bit of database searching reveals that this region actually contains a rarely expressed alternately spliced exon two other MCSs sit there and there and it turns out that these were found to overlap sites that are sensitive to cleavage by the enzyme DNAS1 and such DNAS1 hypersensitive sites are not to be associated with regions of transcription factor binding thus anecdotally there appears to be evidence that indeed MCSs within this intron do correspond to functional sequences in addition though there are a handful of additional MCSs within the intron the functions of which are currently unknown in total we find over a hundred MCSs across the CFTR gene with roughly two-thirds of these residing in non-coding sequences so despite the huge effort of studying the CFTR gene for all of these years we can now identify literally dozens of new non-coding sequences within the gene that are highly conserved in multiple species but for which virtually nothing is known about at a functional level by analyzing the larger 1.8 megabase region encompassing the CFTR gene we now find that about 4% of the sequence falls within an MCS this value of course is nicely consistent with the estimated percent of the mammalian genome that is thought to be under selection of the 4% corresponding to MCSs about 27% corresponds to known coding regions and a bit more fall within UTRs leaving about 68% that reflect unknown presumably non-coding regions the bottom line is that sequences from multiple vertebrates each of which individually looks hopelessly noisy and confusing can be used to find discrete highly conserved regions that represent candidates for functional elements but at the same time I make absolutely no claim that the species that we've sequenced so far represent the best set for finding such elements indeed if you look at the evolutionary positions relative to humans you can quickly see that while we've sampled a nice distribution of vertebrates we've really emphasized these conventional mammals as our data reveals there's still quite a lot of sequence conservation between humans and these mammals in fact there's almost too much conservation but this falls off dramatically when you go all the way over here to a bird in fact often there's too little conservation for some comparative studies sequences from species residing in evolutionary positions between these birds and these mammals might prove to be very useful such as marsupials and also monotranes indeed if we return to that same region of the CFTR gene sequence from two marsupial species the North American possum and the Australian dunnert reveal a much simpler pattern of sequence conservation in particular within the entronic region less conservation than what you see in these mammals but certainly more conservation than what you see in the bird or the fish species suggesting that marsupial sequence may have proved to be a valuable resource to serve non-coding sequences in particular when you consider the fact that these smaller regions of conservation very nicely overlap regions where I told you previously was exactly where the MCS is reside so in short our multi-species sequencing effort is providing a framework that we believe will help guide the selection of future genomes to sequence this includes both establishing approaches that for assessing the relative value of different species sequences for doing comparative analysis and with the detection of MCS being just one of many metrics one could imagine for doing that assessment but it's also providing sequence data from previously unexplored genomes and I think really that while the results of our program are still preliminary I think ultimately it's going to come to show that we should sequence an additional set of mammals probably a marsupial and a monitoring maybe another bird and a fish and an amphibian maybe a reptile or two and I don't even have time to tell you a whole other set of reasons why we would want to sequence multiple primates but I would send you to a recent paper that's published last month in science from Eddie Rubin's lab that I think nicely makes a case for that so in summary the generation of a complete human genome sequence brings new challenges that will be greatly aided by a Noah's Ark approach for multi-species sequencing such evolutionary diverse genome exploration coupled with new powerful computational methods will I think help illuminate all the functional elements in our genome and facilitate the comprehensive understanding of our genetic blueprint and in closing I just want to acknowledge the dedicated members of my laboratory as well as the NIH Intramural Sequencing Center I want to also acknowledge the various collaborators many of whom I know are sitting in this audience but really my presence on this stage today reflects a thrilling journey I've had in genomics for the past 14 years including my extensive involvement in the human genome project since its inception but I really owe that presence to the remarkable opportunities and support that my parents actually provided to me and they actually journeyed here from St. Louis to attend this important symposium and so in closing I want to acknowledge them and actually thinking about my journey I want to thank them for their contribution to the human genome project and I want to thank all of you for your attention thanks Eric we're all convinced to sequence more and more genomes if we weren't before the next speaker is Pat Brown from Stanford who I think the most important thing I can say about him is he's just done wonderful wonderful things with DNA chips and studying gene regulation and so forth and he's going to talk to us about genomes life stories that's okay I think Eric has your slides so he has Eric's talk on the computer not his and I think we can work with that I'm afraid I can't spend any more time introducing Pat he's the one of the five speakers that I really don't know let's see if we get it sorted out David are you willing to come next David? Oh you have it? okay it looks like we're set thanks Pat thanks very much Richard thanks everyone for your patience while I mess around my computer here the idea that variation in genes gives rise to variation in the form in physiology and behavior of people and other living things is one of the most powerful ideas in biology and really the central idea of genetics inherited variation in the sequences of genes leads to the kind of variation between species and between individuals that's illustrated in this picture from this somewhat infamous issue of Science Magazine and what's easy to see just by looking around this room there's another kind of variation in genes that's much less familiar but much richer in variety and arguably even more important and to see it we need only just to look closely at one person the tens of trillions of cells in your body are so diverse in appearance and behavior that if we didn't know any better there would be thousands of completely different organisms and yet every one of these cells has an identical genome the variation that produces this extraordinary diversity is not in the sequences of genes but in their parents of expression and even though the sequence of the genomes in these cells doesn't change over time the structure and physiology and behavior of these cells is constantly changing in response to changing signals and conditions and life experiences during development a simple tiny round cell too tiny to see with the naked eye can turn into a gigantic neuron that's more than a meter long elaborately branched and electrically active and this nice quiet resting lymphocyte here when it senses the presence of a bacterium can turn itself into an angry lethal cell that will hunt down and kill the bacteria well if these cells if each of these cells has an identical genome how can they be so fantastically different? yes each cell has an identical genome the words in the genomes vocabulary are identical but just as you can use your vocabulary of about 30,000 words to write a million different stories just by using them in different combinations and patterns the genome can use its vocabulary of 30,000 or so genes in different patterns to write a different script for the life story of each cell in your body and that's exactly what it does well wouldn't it be wonderful if we could read all those life stories? there's a line that's often quoted at least it's often quoted by scientists from Marcel Proust that says the real voyage of discovery is not in seeking new landscapes but in seeing with new eyes and I must say I find it quite remarkable how much his handwriting resembles a PowerPoint font well as frankly Francis Collins rightly emphasized the genome sequence is a public resource available to everyone and anyone can use it to make new eyes that enable us to see ourselves in a new way and here's an example of new eyes made possible by the genome sequence a DNA microarray which is made by simply printing tiny spots of DNA representing each of the 30,000 or so human genes in an ordered array on a simple glass microscope slide using a simple benchtop robot and you can barely see the pattern of these 30,000 tiny spots in visible light it really doesn't look like much but when we use the microarray to look at the gene expression pattern in a cell or a tissue or a tumor it lights up each of the colored spots that you see here represents one of the 30,000 genes and we can visualize the gene expression pattern using red and green fluorescent dyes to label the messenger RNA that we isolate from cells so that the color of each spot shows us whether a gene is expressed and at what level this particular picture actually comes from an experiment that my son Zach a high school sophomore did last summer looking at how the gene expression pattern in his dad's blood he particularly enjoyed the blood drawing part actually this experiment changed hour by hour over a couple of days and I think it's an example of how the big science of the genome project enables lots of smaller scale science a high school student with his own hands and eyes can see the whole genome come alive well with these new eyes we've now embarked on a voyage of discovery reading the life stories that the genome writes for every cell and every tissue in the human body and we're just getting started on this of course but okay so we have lots of colored dots that I'm claiming represent a life story of some kind but it is supposed to be a story it's not in any language that we understand but there's hope because the genome's language has a beautiful intrinsic logic and order that mirrors the intricate molecular choreography of the cell and the diversity of cells and tissues in our body and we can use this inherent logic to organize the data that we collect on this voyage of discovery into a new kind of map of the genome that's not a map of its physical structure but a map of its expression program its voice so to speak and the format of this map is like a table each of the horizontal rows you see here represents a single gene and in this particular map we're only looking at the 6000 that vary the most in their expression in these samples and then each of the horizontal columns represents one of the samples we analyzed and it's pretty much a grab bag of a bunch of human cells and tissues and half of them being non-malignant and half of them being cancers and then each individual pixel several million pixels in this map represents the expression level of a single one of these genes in a single one of the samples and we use a color code with a very dark blue color representing no detectable expression and the brightest yellow representing the highest levels of expression and all these shades of turquoise to allow visualization of the data and the final thing that we do is to reveal the intrinsic order in this program is we use a statistical clustering method to group together genes that have similar expression patterns and we do the same thing to cluster the cells and tissues according to the similarity in the gene expression scripts for those tissues so notice how diverse and intricately patterned this gene expression program is which really parallels the diversity the molecular diversity and the functional diversity of the cells and tissues in our body now if the gene expression patterns are really the genome script for the life story of each cell as I said then we would expect that similar cells and tissues should have similar scripts and they do for example right here in this series of about 12 samples here are all normal brain samples and you can see how they have a very characteristic and distinctive gene expression pattern with subtle differences depending on the region of the brain but that distinguishes them readily from all the other samples in this set and right next door we have a set of samples that are all brain tumor samples and you can see that again they have considerable similarities in the normal brain but also distinctive differences that separate them from the normal brain samples and also from all the other samples in this set and you could do the same thing with the dozens of other kinds of cells and tissues and tumors here each one of them has a very characteristic and distinctive gene expression pattern that causes it to be localized in a particular way in the map here now this means that we can use these gene expression programs in principle to identify and classify an unknown sample so for example if one of you were to walk up to me with a little tube containing a sample of RNA from an unknown tissue I would, I could analyze it using a DNA microwave by comparing it with a map like this I could have a pretty decent chance of being able to tell you not only what tissue it came from but also quite a lot about the physiological state of that tissue and I think it's easy to see how this can become a pretty powerful diagnostic technique the expression patterns also tell us a lot about the genes we learned what the words in our vocabulary, the vocabulary that we use in our everyday lives basically just by observing how they're used and we can use the same strategy to learn the meaning of the words in the genome's vocabulary the functions of the genes these detailed pictures of each genes if you just read along the rows here you have a very detailed specification distinct for each gene of each gene's expression pattern that's specified, we're basically evolution that said it's, it's it improves fitness to have that gene expressed and what tissues and under what conditions and by looking at these rules for usage we can start to build up a pretty well developed idea about the function of the many thousands of genes that are newly discovered by sequencing and for which we have no experimental data as to their function and then as we learn this language we begin to use what we know about specific sets of genes the words that we're able to recognize to read the scripts just in a sense as we might read the script of a play and then visualize in our minds eye the action on the stage for example this little cluster of genes right here is a set of genes all of which encode proteins that are exclusively localized in the mitochondria and they have a role in the mitochondrial function the cell's energy factory so to speak and so their expression basically gives us an idea of the energy needs of the cells and how much and what the oxygen demands of those cells are because they're involved in aerobic respiration and you can see obviously that the brain needs a lot of oxygen and so does the heart here okay and this little cluster here is a set of genes that are exclusively expressed in cells and tissues from males and so basically this tells us which of these samples come from men and boys and and then this cluster of genes here is specifically expressed during cell division in dividing cells and so its pattern of expression shows how fast the cells are dividing and you can see for example that in the normal brain there's very little cell division going on whereas in the brain tumors there's substantially more cell division and the same is true for the many other cancers that are represented in this sample they tend to be dividing more quickly which is no great news so that's just three clusters of genes and of course if you look at this map you can see how there are many hundreds of additional clusters of genes that we can use to build up for each of these tissues a reasonably rich picture of what its molecular characteristics are and some ideas about what it's likely to have your own physiology will be okay well this is just sort of a static picture of the different cells and tissues in our bodies and we know that every one of these cells and tissues is sort of continually reinventing itself the living genome doesn't impose sort of an authoritarian hard wired program on the cells but it sort of sits right at the interface between nature and nurture so to speak and it is continually bringing new pages to the story in response to its life experiences and so the corollary of that is that we really want to be able to get dynamic pictures of the expression program under all the different conditions to which each of these cells can respond and that's going to be a very huge task that we've barely begun I just want to illustrate one important example of that and that's the cell division cycle this is a map very similar to the one I showed in the previous slide but in this case we've organized it such that you can see for this set of 900 genes that are periodically expressed during the cell cycle the waves of expression of those genes as the cell progresses through the cell cycle and by recognizing that these 900 genes are regulated during the cell cycle and looking specifically at where they're expressed during the cell cycle we can build up a better picture of the molecular choreography of this important process and also the role that each gene plays in it well we're just really beginning this voyage of discovery but already the results are starting to have some influence on how we think about important medical problems as Eric alluded to briefly in his talk and for example here's a map of the gene expression patterns taken from several hundred human cancer samples and this map gives us a very rich picture of sort of the molecular composition and the wiring of each different kind of cancer cell its interactions with surrounding cells and tissues, what systems are active what are present and even what potential drug targets are available for treatment of the cancer and you can see from the color coding of the tumor samples here kidney, brain, prostate, breast, stomach, ovarian, etc these little branches at the top here that each of these cancers that we can distinguish by sort of classical pathological means also has a very distinctive and characteristic gene expression pattern this already suggests the potential of this kind of analysis for identifying molecular markers that could be used for early diagnosis and screening and so forth but if you look more closely it's also evident that cancers that originate from the same tissue in this case breast cancers that we would today give the same diagnosis are not all the same in their expression patterns in fact there's a lot of diversity in their molecular programs and this parallels the clinical observation that cancers that we call by the same name and this is a very challenging clinical problem so these gene expression portraits of cancer have now become the focus of a large scale effort by hundreds of groups around the world that's starting to begin to define a new more precise and hopefully predictable molecular classification of cancer that I think in the next few years will lead to more individualized and hopefully more effective treatments now there's an important subtext to this story and also to every story that you've heard and every story that you'll hear today and tomorrow and it's probably the most important and the least appreciated story of the genome project but it's been touched on by virtually every speaker this morning Francis Collins rightly emphasized that the human genome project has been a model for the value to science and society of the immediate open and unrestricted access to new scientific discoveries in particular genome sequence information none of this would have happened were it not for the fact that for the past DNA sequences have had a unique status among scientific information they've been kept in an open public resource freely available to everyone from online databases like GenBank for anyone to use for any research purpose our ability for example to make and use DNA micros is absolutely dependent on our ability to find, organize and analyze copies and copy every gene sequence and just to give a fresh illustration of this ripped from the newspaper literally of the public benefits of open access Joe Derisi was a brilliant young scientist at UCSF with these colleagues here made a DNA microarray by simply downloading every viral genome sequence that was available in GenBank a little over 10,000 of them and synthesized little bits of DNA for each of them and printed them in this microarray that you see here so when the Centers for Disease Control got their hands on the first sample from a SARS patient they immediately sent an aliquot to Joe with a single microarray analysis Joe had tested this tiny sample for every single one of these 10,000 known viruses and was able to report back to CDC that it was almost without a doubt a novel coronavirus previously unknown in humans but with features of many animal coronaviruses so what's amazing about all this is that DNA sequences are unique in being treated as public resources essentially every other kind of published information becomes the private property of the publisher who thereafter has monopoly control over who can read it and how it can be used doesn't matter if the research was sponsored by public funds or for the public benefit just try to imagine if DNA sequences were treated the way every other kind of published scientific research is treated not freely available for you to download and use creatively and use to develop new and powerful algorithms but only available in the publisher's terms at the price the publisher sets in a thousand different journals each with different restrictions on access can't search the sequence itself you can only search it by the name of the gene and a few keywords all the things that we've taken for granted that we can do with sequences wouldn't exist today and the value of the sequence information to the scientific community would be so diminished that it's very unlikely that there would have been a genome project so why should any other kind of scientific information be treated differently from the information of genome sequences in GenBank imagine if all other scientific publications were open available for creative development of new tools like we've seen in genomics to search, annotate, interlink integrate not just DNA sequences but the whole body of scientific knowledge what Shirley was alluding to and many of the other speakers I think you'd see a transformation of science in medicine that would match what we've seen in the genome project and why should a college or a high school student with a passion for science or a cancer patient who wants to learn about the research on her disease or her physician the people whose taxes paid for the research for that matter why should a physician or a scientist in Audis Ababa or Bombay, the people around the world who could benefit from ready access to the latest research why should they have any less access to the published results of publicly funded research than I have at a wealthy research institution like Stanford that can afford to pay for access is this what scientists and the public and the government want? Of course not but that's what we have today what this system is a loss of science in society and I think we as scientists owe the public who supports our research the full benefits of the research they support and we owe each other the full benefits of the work we do as part of the scientific community and this is an easy problem to solve it's not rocket science so let's fix this system there's an active grassroots movement of scientists to make all scientific publications freely and openly accessible check out this website and in closing I want to urge all of you to do what you can to help us make all scientific research a public resource the way the human genome is today and I want to just say thanks and congratulations to the genome sequence team Thank you so very much Pat for the beautiful work and also those great comments about free data release and now we're going to shift over to some computational genomics and welcome David Hausler from UC Santa Cruz and he's of course very well known for us for the beautiful work out of his group on the browser and the work in assembling the draft thank you very much Richard well I'm in the awkward position of returning us back to comparative genomics a little bit here and I would say that Eric Lander has already said a lot of the things I'm going to say and then Bob Waterston stole my opening joke that Eric Lander has already said a lot of the things I'm going to say so we're in some kind of a recursive function here moving backwards I don't know so I'll do the best I can with what I have left that's unique in this talk so I want to talk about three things I guess I'm here I'm here representing an interesting subculture here the cyber geeks and the math nerds that have all come into this field and I think have invigorated it in an interesting way so what are some of the things that they've done in the analysis of these in these big analysis projects I'll hit on just three in this talk sequence assembly is a very important thing that has to be done computationally genome browsers which I want to emphasize are a kind of a new computational microscope on the genome and then computing evolution's path which returns us to the comparative genomics theme so assembling the genome some of these stories have been told at length in the popular press where in the draft phase of the sequencing there were two great projects one led by Gene Myers and Granger Sutton who actually pulled together that whole genome shotgun data which was an enormous computational task and then there was also a somewhat last-minute and now famous effort by Jim Kent who was then a graduate student and he pulled together all of the data from the public project into a coherent draft sequence and during the hectic days the month before the June 26th announcement he typed out some tens of thousands of lines of code icing his wrists at night just furiously trying to put together a program that would pull together that sequence into as coherent a picture as was being pulled together by Myers and his team at Solarra and I think this is one of these cases where you have 400,000 pieces of course the groundwork for those pieces was laid by Phil Green another one who fits this prototype quite well we never could have gotten the pieces that needed to be assembled together without the brilliant advances by Frapp and the other mechanisms but you see that these people have pulled together data unfathomable to do by any kind of hand analysis in the nick of time and one thing we're proud of here at Santa Cruz is that that draft, that first working draft was posted on the World Wide Web from UCSC July 7th this shows internet traffic now outgoing is the green traffic this is the weeks of the year 2000 and you can see it's rather quiet accessing the UCSC site until the news that the first working draft of the human genome was set free emphasizing some of the previous speakers and unrestricted you could go ahead and get a glance at your own genetic instruction book and that hit caused half a terabyte of information to be broadcast out on the net that day July 7th broke all previous records and you know scientists all over the world had access to it who actually looked at it other cyber geeks they were looking for mysterious messages counting the number of times gattica had occurred in the sequence it wasn't exactly scholarly work on July 7th but at least it was free, it was out there everybody could jump at it and get to it and so I'm very excited about that event if we look recently other important advances have been made in assembly the arachne assembler is a landmark putting together again the whole genome shotgun sequence of the mouse and the David Jaffe in Seraphim Batsuluit in the lander group and also there was another assembly I want to mention here the fusion assembler put together by Jim Mulligan so there were actually two assemblies of the mouse genome and it was beautiful to see both teams in this friendly competition we were able to compare the assemblies it was tremendous, it was almost a dead heat they were very very close and what we needed to settle on one for the paper and the arachne was chosen and then most recently the wonderful announcement in November 2002 of the first draft of the rat genome built by yet another technique which is an interesting technique developed by Richard Gibbs people at Richard Gibbs group to pull together a combination of clone based and whole genome shotgun data so these are all advances in computer scientists need to keep track of what's going on in the field and be there ready to pull together the new kinds of data sets if you look at this next phase what does it do so that gives us the sequence but what do we do with it now I want to move into the second part part of the talk where we look at genome browsers as microscopes we heard about the ensemble browser at the press conference there's another browser called the NCBI map viewer and then there's the UCSC genome browser which was also created initially by Jim Kent and now we have a large team at Santa Cruz of several staff and graduate students who work furiously on this in times of urgency and no more than in the last four days I have to say credit to these you can go to genome.ucse.edu right now click a button and you will see this current draft we only got on Wednesday and normally it takes the thousand computers that we have there it takes longer than that to actually pull together the map every all the information in GenBank on to the genome sequence and give you a browser experience but they've made incredible time here what does it mean to have a browser of the sequence now if you look at the way we had to access the genome before the sequencing project this was our view of the genome a cytogenetic view of the genome or genome as we heard on some of the beautiful film clips here a genome that's based on maps of genetic markers or radiation hybrid markers these maps were crude the distances were measured in centimorgans even in the radiation hybrid map if you go back you can't get more than a resolution of 100,000 bases or more with this now we have an opportunity to go much richer and if you think about the great things that happened based on these early crude maps it is amazing think about the discovery if we think about the discovery of the Huntington's genes I was just talking with Nancy Lexler about this the story is amazing Lucky Jim Gisela getting it on the first marker in Hausmann's lab that's amazing nowadays you shouldn't need that much luck so the bad news is we're kind of taking away the luck factor here with these browsers if you look at this we can take a piece of the in this case chromosome 4 and start to zoom in on this and what the first thing you notice is that that old world the side of genetic world the way we thought about the chromosomes in terms of bands has been mapped on to the genome sequence along with the full genetic maps and radiation hybrid maps and this is the work of Terry Fury in my laboratory here who's here today and it was a very very difficult informatics test to actually connect the two worlds our previous maps of the genome there's so much literature and culture that lives in those old worlds it has to be pulled forward so you zoom in to a certain part of the genome and immediately you start to see these large blocks of Sydney that Eric Lander talked about you can see the relationship with mouse at this very very large level these are pieces of mouse chromosome 5 and 7 that are orthologous or derived from a common ancestor with these regions and we can choose then to go ahead and zoom in again on a piece of that so we take this and zoom in again and we see a cluster of genes and inside of that is the Huntington's disease gene right here these genes are depicted the boxes are depicting exons the hashed lines are depicting introns Phil Sharp told us so much about and at this point it looks like there are a lot of exons but that's actually as you zoom in you start to see at this level we really can't see gene structure it's kind of blurred together so let's zoom in again a little bit and blow up this Huntington's disease gene you see the enormous number of exons and the complexity of the gene here we see now a more refined version of the comparison between human and mouse this is a careful plot of the similarity between human mouse and 50 base segments normalized to what we've estimated as a neutral rate of evolution in that segment and you see we're speaking around exons but you can't see that here we see the single nucleotide polymorphisms that we've already heard about as well and they tend to blur together so let's take an exon here and zoom in a little more and we see so there we see the exon basically swimming in an ocean of introns here is the high conservation levels that you see that are often associated with exons here are some of these interesting spikes that Eric Green was just talking about and here's a polymorphism I've expanded this track and if you zoom in on that further you can get down to the base level this would get you right through to the dbSNP database at NCBI you can look this up it turns out to be a coding SNP so it actually will change the codon in that exon and maybe of significance and so you're often looking this is a launching point here for going into various parts of the experiments as Eric said so if you look what else can you see there are all kinds of other types of information that can be tied to this not only information from the literature here is one of the starting of the 5,000 articles on Huntington's disease gene or if you click through to the mouse ortholog of this you can see the expression of this in the head fold of an 8.5 day embryo in this image if you look through the expression data that Pat Brown was so elegantly talking about you can select data this actually is data from aphymetrics technology that's put on the web free by the Novartis Foundation but there's also a track showing the data from Pat Brown's lab as well and that is very important information you see the higher expression in brain tissue there so all of the different types of data that can be created either archivally or from high throughput methodologies can be linked to the genes and so I want to present a little bit in this last part of the last comment about the browser phase of this talk of a kind of a vision why do we have what are these new microscopes that we can put on the data put to the data my goal is to see our lab try to produce a browser that is essentially a continuous engine for discovery it would be possible in the not too near future not too distant future to look at a browser that could take multiple streams of high throughput genomics data producing variety chromatin immunoprecipitation and all kinds of other new methods they're generated a secretly fed into nightly updates of the browser and display and at that point that browser becomes a new tool for looking at the genome right today I want to emphasize this different philosophy right today you can go to that website and you will see all of the data that we could suck out of GenBank map to new regions of the genome before all of this high throughput data is already mapped there if you go to one of those places you will see a configuration of data that no human being has looked at before this is produced nightly by machines and collected and put out there it is a microscope you're seeing new stuff you're not seeing things and that has already been sorted over and picked over it's amazing and it's a good it's an exciting thing to give it away in that sense the last part of my talk is about challenges in the genome analysis and we've seen this determine the functional elements including the coding and non-coding and I want to emphasize again that we want to get to a full systems biology and it's hoping that the computer nerds and the math nerds will get some kind of model of the cell but we are not anywhere near being able to do that until we get the players right and we're not we even haven't gotten that right completely these show gene predictions that are made by computer this is the ensemble gene prediction in red here done by the group Clamp and Bernie at all and then this is the gene prediction by David Culpin his team at Affymetrix these are the two predictions that were used primarily evidence based predictions that were used in both the human and the mouse papers to identify initially a crude cut at what the gene set was you can see the evidence from express sequence tags very much highlights this particular gene the insulin like growth factor binding protein 6 so this is a case where the gene predictions are quite accurate but unfortunately this is the exception so if you look at other places where the computational predictions from several different groups have been made they disagree quite large in large extent and so we are at a case where we haven't saw the automatic gene prediction so that's really a significant challenge we hope has been said before conservation with other species will help and this is an image of the conservation levels that you've seen before in 50 base pair segments with mouse looking at another member of the IGF family the acid label subunit and if you look at this these peaks represent a chance 1 in 10 chance 1 in 100 or chance 1 in 1000 on this log scale of seeing that much conservation under neutral model and outside of the coding region there's a distinctive peak here and this is in fact associated with a known transcription factor so we can in principle identify this these might be real as well but this also might be noise and so I want to emphasize what Eric Green was saying we need more than just the mouse on this if you look at the data that Eric Green's lab has produced you can get a fascinating glimpse at some of the other structures that lie hidden from our current view the computational analysis can bring these out and here I'm emphasizing a different type just to be different instead of looking at the kind of sites we've seen before here we're looking at Matthew Blanchett's program looking at blowing up the region here in the 3 prime side of genes that has a structure to this so computers can also be applied in this case the M-fold program maybe in combination with the high conservation in this structure a stronger prediction of functionality and maybe even a hint and here I go really giving away something that is kind of crazy we're currently looking at this one with many areas in the laboratory this is about a 90 base pair region in the middle of the ST7 gene which is associated with tumor genesis and even in autism in one study this thing is right from either flanking exon we have 90 bases of DNA that is conserved with only one substitution in all nine mammals and look at its structure this thing does something I have no idea what it does at this point I'm just stupid enough of a geek to give it away at this point for anybody to look at I guess but it's fascinating it's fascinating to see the kinds of things you can discover with sensitive computational analysis and I was very gratified to see Eric's data on how accurate the computational predictions were when you got the marsupials and essentially confirming well, okay, just to wind up here we've seen this about 5% of the human genome we estimated about 5% of the human genome was visibly under selection this was based on comparison with a neutral mutation rate estimated from ancient ancestral transposons that we believe are under neutral rate there are a lot of caveats on this I want to say let's be cautious about this we haven't actually observed the selection in this, we need more species to really get a model of this we want to go far beyond these simple two-species experiments and crude, crude estimates like this 5% we want to actually have a model of the way molecular evolution is happening there's Adam Siepel in my group has got an exciting new model that's starting to get at the way codons evolve with about 500 different parameters we can get much more richly parameterized mathematical models continuous time Markov models of the evolutionary process in different types of functional sites and those will be ever more powerful microscopes on the sequence in summary, I think all of the previous speakers have led up to this but I'm not sure that anybody has said it in kind of simple terms there is a grand challenge out here of comparative genomics from the point of view of the human genome and that is to reconstruct the evolutionary history of each base in the human genome going back as far as we can we should do that I love I love the I love the analogy of origin stories that we heard earlier we care about how things got here not just for recognizing their function but I think we all care about how we got here thanks so I want to acknowledge a lot of the people in my lab again they can throw me off the stage if I read all the names I acknowledge a number of people there in the previous talk but there are a huge number of people that go behind the scenes under the hood so to speak on all of this and they deserve credit and then of course Eric spoke eloquently of this amazing collaboration the most amazing experience to work with this group of people we all hated the conference calls but in fact it was an extraordinary experience and this is unfortunately a very limited list so I won't read it so they don't drag me off with a hook thank you David thank you our last of these five targeted speakers is David Bentley a human geneticist for a long time and one of the people who was wise enough to move to the Sanger Center in its earlier days because I think he saw the power of what he was getting into he's going to talk to us today Richard thank you all for the pleasure of hopefully taking a brief exploration of human sequence variation which is very much a story about uncharted territory an exploration which starts of course in the foundation of genome sequence information we have just one reference genome sequence with us today there are some six billion individuals out there which remain relatively uncharacterized and there's an enormous wealth of information that we can bring to bear what can we use from genome sequence information clearly the two important areas the sequence providing us with this metric on which to lay important information and particularly the annotation of the genes incomplete but growing all the time and to overlay with that on the same coordinate system of course our increasing knowledge of variation at all points in the genome couple of information, metallial frequency and so on and to focus combining those two bits of information on the all important subset of functional variants most of course which might be expected to lie within protein coding sequences but perhaps now the emphasis is also on variants which lie or give clues to other possible functional sequences and therefore the requirement to really continue this effort of annotation alongside discovery of further sequence variation what about the origin of sequence variation just very briefly of course it arises from an ancestral sequence by a combination of substitutions of insertions and deletions but also by recombination which results in the reassortment or recombination, new combinations of previously existing variants providing an increasing number of independent but co-existing haplotypes which can then undergo selection, positive or negative or genetic drift which results in gradual alterations in the frequency which has haplotypes occur within a population as we move to look at a present day population the majority are SNPs of course perhaps particularly attractive because of the possibility of new automated methods for acquiring the information about SNPs in multiple individuals the suggestion of collecting personal genetic information on a very large scale the technology being available therefore to ultimately provide a barcode of an individual whether or not it's the right thing to do it is certainly technically very feasible review of SNPs in the genome 90% of all variants, the remainder being insertions and deletions, one SNP per 1.25 kb between any two genomes that is, and of course given that each one of us has two genomes one maternal and one paternal each of us then carries something like 2.5 million variants just between the two genomes in our deployed genetic content in trying to focus more on the particular functional variants we can take one looking towards the exons if we consider this fraction of 1.5% or so of the genome containing exon protein coding sequences perhaps 60 to 120 thousand of these variants in every individual might be within the exons and perhaps a third of those are non-conservative and therefore really top candidates for possible functional variation because they in some way visibly alter the protein structure and possibly function but of course this again neglects perhaps one third or two thirds more of variants that might lie outside exons and so we should not focus all our attention on the exons themselves Examples of functional variants are familiar to but this does illustrate a point in some cases variants are immediately predictable because of the nature of the change they give to the annotation of the gene in which they lie in other cases even well known variant perhaps rather less obvious on the change that it creates in the amino acid and further functional verification in order to prove or disprove a mechanism by which such variation might actually give rise to a phenotype and thirdly of course again a well known example but involving alteration interaction between two proteins factor 5 clotting factor the Leiden mutation abolishing the ability of the clotting factor to be degraded and resulting in elevated levels and a potentially higher risk of thrombosis as a result of the example I choose because it illustrates the principle of the association study of the beginnings of looking at more complex diseases which are so far successful in taking of course a collection of well phenotype cases and carefully matched controls to eliminate the potential variability between the two collections before proceeding with a test of such a functional variant and then to demonstrate that there is distinctly different distribution of those variants in the controls compared to the cases illustrating of significance also illustrating of course the substantial amount of additional information that we as yet do not know about other factors which confer risk which may be additional genetic variants or also environmental factors suggesting as well the long road to dissecting out every risk factor involved in any complex disease this then is an example of the direct approach for association studies where we have the gene we have candidate functional variants and we go ahead to test them in determining such a genetic association by contrast of course given that we do not have a complete knowledge of all the genes or other functionary important regions we may not have indeed knowledge of either the genes or the functional variants it has been suggested and much discussion is now focusing on the value, the prospects and the limitations of an indirect approach using large panels of SNPs spread across the genome placed in the reference sequence and selecting subsets perhaps those nearby on the basis of their performance in association studies to some extent or completely may reflect the as yet undetected functional variant and allow a targeted search to complete and characterize the functional variant from the data from the indirect assayed SNPs I'd like to spend a few minutes just exploring one slide to explore the direct approach and the implications of such an approach and also of course the implications surrounding evaluating the indirect approach as part of the haplotype map project the direct first, if we consider the possibility of discovering SNPs perhaps by targeted sequencing of the exons one provides the possibility by such a targeted sequence based system of going deeper should one wish to find additional variants additional variants which therefore may be rather rare and the possibility is therefore there for going to any depth to examine the possibility of the involvement of very rare variants in disease once one is established perhaps the major associations the caveat in such approach of course to these other regions which may turn out to be functionally important one might wish to go back to sequence those regions as well as the annotation develops in the next few years in the genome and this then of course results in perhaps a more complete view but still one is worried perhaps about the unknown ultimately one would argue that it would be jolly nice to get complete genome sequences from a large number of individuals that would be grand challenges facing all of us around the world as we look for technologies for cheap whole genome sequencing to return to the indirect approach for a moment I wish just to take you through the concept of linkage equilibrium if we consider two SNPs which might either be SNPs in the map or one SNP and an actually unknown functional variant a bililic variant if those are in equilibrium there is nothing tying them together and one sees all possible combinations of the two alleles there is no linkage equilibrium or no LD by contrast if there is in fact complete LD a scale of 0 to 1 and a D prime value indicates complete LD there has been no ancestral recombination in the population to disrupt the two ancestral haplotypes the two alleles and therefore one sees only a subset a limited number of the possible theoretically possible haplotypes and this principle then gives the first view of the possibility one might obtain a simplified view of the variation in the human genome on the basis of the LD between SNPs a simplified view which would be enormously useful in applying more refined analyses of association studies haplotypes then can be defined these are the limited number of haplotypes and LD therefore is reflected in this limited number of reserved haplotypes in the genome LD to look at the properties of LD association studies indirectly will only work if this LD applies across the genome LD does decay with distance from the early study we took on the chromosome 22 data led by Ian Dunham's group to illustrate that basically SNPs which are close together do have a high LD and it decays over 50 to 100 kb in general looking at all pairwise comparisons of SNPs if we look along the entire chromosome 22 you see this marked feature of the very variable levels of LD great peaks covering several hundreds of kb where there's very high LD between SNPs indicating therefore the association between variants in these sorts of regions would be likely to be very substantial and very useful for association studies but also other areas troughs where at this level of density of analysis it's an approximate figure indicates there's more work to be done the all important correlation given that LD is perhaps highest at areas where there is least ancestral recombination correlates very nicely with the observation of present-day recombination in pedigrees giving an important clue or confirmation of the correlation between present-day recombination and the presumed driver ancestral recombination of LD if we look in greater detail on chromosome 20 this is now a 10 megabase region from a current study as you increase the density of SNPs in the study the actual pattern of LD is very robust these LD plots to market density so a reliable robust dataset and furthermore again you see the correlation with genetic distance the flat areas correlating with the areas of high LD I now want to show you just two views as we begin to develop views of looking at LD within the human genome views which as yet will stand for the refinement but ways perhaps to try to infer useful reagents for association studies and also useful ways of looking at the underlying processes which have given rise to LD first of all the proposer from the Whitehead group examining the D prime values between pairwise markers and if it extends over all combinations of pairwise comparisons within a group one can declare a block of high LD on the basis therefore one assumes that there is little or no ancestral recombination in the entire region and therefore as one carries along the genome one then selects from these regions a subset of reference SNPs which actually still captures the limited set of common variant haplotypes which occur within these regions these other regions are not studied at this stage to sufficient detail and additional work is required but we have at this base one way of looking at a simplified view of the genome clumping together the variation of developing a simplified set of reagents to reduce the cost of association studies without loss of information we look on chromosome 20 for a moment a slight contrast to the previous data that I showed you here as you increase the SNP density you do see marked changes development of the block structure as you move to higher density so the analysis is not particularly stable at low marker densities and really does require gradual development up to in this case something like 65% two thirds of the chromosome looking in well established blocks of high confidence so towards an end point but not there yet the other view I'd like to share with you again an early and incomplete view the concept instead of looking at the blocks of high LD to look for the events of recombination the minimal number of recombination events which are required to fit the data for the pairwise LD between SNPs if we look at a simple run of the genome along here in comparison of pairwise LD what is striking then is the areas which are white reflect very high levels of recombination which must have occurred red reflects the absence of detected recombination an incomplete data set as yet but clearly one can see this very heterogeneous distribution of recombination and a very marked hotspot if we look at this part of the plot it's a hotspot in a West African population the data was provided from the Whitehead group analysis done at Oxford but also the same point identified in the CEF pedigrees indicating a level of correlation between different populations which needs further exploration I don't have a slide but just to mention briefly much of this work does involve the acquisition of further data one looks hopefully to the work of Alec Jeffries and the Leicester group where a very small region of the genome was nevertheless covered to 95% high LD blocks of 60 to 90 kB and the areas in between by now only 1 to 2 kB were directly confirmed experimentally to be rich in recombination events which would be an attractive and important outcome if the same pattern could be developed over the entire genome so what about the prospects for applying our increasing knowledge and hopefully simplification of sequence variation to the discovery of disease genes which reflect for a moment on the progress to date during the course of the last 15 years this data from online Mendelian inheritance in man provided by the NHGRI illustrates certainly an encouraging growth in the discovery of genes involved in human disease but once you point out of course most of these are Mendelian high penetrant alleles monogenic disorders on the whole and therefore this is certainly a valuable approach and we should not ignore it will continue to be valuable there are additional Mendelian traits segregating in families that can be approached by this sort of approach which has really been the foundation of much of disease gene discovery over the past 15 years but looking ahead to the complex disease I'd like to show you the iceberg the tip of which Eric showed you before indicating encouragement for the prospects that a number of associations are emerging in different complex diseases well known complex diseases where the all important allele frequency of the functional variant is indeed high and therefore a common variant can be detected given a sufficient relative risk in moderate size at least by tomorrow's standards association studies which can be done on increasingly refined sets of SNPs in the absence of direct knowledge of the functional variants but by working with the product of the haplotype map of the human genome these two factors are critical in the success with which such associations can be done reduced allele frequency of course requiring larger populations and also low relative risks requiring much larger samples to detect and there is a point perhaps if this arrow continues downwards well one moves out of the realm of the common variant common disease hypothesis there's every reason to suspect there's a complete allelic spectrum of variants causing human disease and therefore for these rather rare variants one should look again at the sequence based approaches through genes we should not cut ourselves off from being able to go deeper and deeper in obtaining a fuller knowledge of sequence variation in order to have as somebody mentioned earlier a full understanding of the genetics of human disease by way of concluding remarks I'd just like to share a couple of concluding remarks for you which reflects the very importance of the completion of the human genome sequence the international availability of it and the importance to everybody in the world these remarks are drawn not from the literature but from recent quotes to start with that of Elia Sahuni who said at the end of last year in sober reflection we have never faced a problem like this in the history of science as we move forward interesting to note Francis Crick took the same observation there seems to be no limit to the problems which now confront us it's all right and good very well to have the responsibility of realising the challenges that face us bigger than ever before as a result of the production of the human genome sequence what we also need of course is the sheer enthusiasm of young investigators who are prepared to dive in and use the information perhaps not seeing the full picture as yet and as exemplified now we have a little sequence of everything which was told to me by a young postdoc in India on reflection on the release of the human genome sequence thank you thanks very much David Bentley we're going to hear from two more speakers about the future the first one it's an enormous pleasure for me to introduce Ari Petrinus the Associate Director for Biological and Environmental Research at TOE really needs no introduction to you though but Ari I'd like to take this chance to really thank you publicly for your wisdom and cross agency leadership across sector and across oceans I think without you we wouldn't be where we are today thank you Richard those are very very kind words it falls on me to tell you now the road ahead for the Department of Energy and even though I must say our paths will diverge somewhat from the National Human Genome Research Institute in the years ahead many of the talks that you've heard today in fact have highlighted and have emphasized how much still remains very common to our pursuits and that opens tremendous possibilities for other cooperation in the future so let me first start by joining others in first of all congratulating the many scientists that have been involved in the completion of the human sequence and of course we take great pride in the sequencers at the Joint Genome Institute and their colleagues and collaborators at Stanford University and at other laboratories around the DOE complex that have been so instrumental in completing their piece of the human genome sequence chromosomes 5, 16 and 19 those folks shown here in fact our team deserve a lot of congratulations and a lot of thanks for the many contributions they made and I salute them publicly for all their contributions I'd like to say that our genome facility at Walnut Creek production genomics facility of the Joint Genome Institute will continue its sequencing tasks in the years ahead to both serve the sequencing needs of the DOE program as well as about the Genomes to Life program but more importantly will become a scientific user facility like the other scientific user facilities that the Department of Energy is well known for serving the broader needs of the scientific community in this particular case serving the sequencing needs of the scientific community following a rigorous peer review and evaluation in the selection and the queue in which those organisms will be set you already heard many talks about the value of sequencing many organisms and of course I am particularly biased towards the microbial world but in general I can use another famous man a famous quote for why we need to go further into sequencing other life forms the invitation program that you see that you had today started in fact with a reference to Aristotle who looked at the causative force between an acorn and an oak tree he made many mistakes when it came to biology but this is one mistake he did not make when he said that we should venture on the study of every kind of animal without distaste for each and all will reveal to us something natural and something beautiful these are great words to live by and to use as a compass as we proceed in this new century of biology the human genome project in our opinion has been a lot more than just a revolution in understanding human biology a lot more than understanding and doing major contribution making major contributions to human health and human medicine although they are of course the most prominent and maybe even the most important for us at the department of energy there is also in terms of setting the stage for the future in terms of developing the technologies pursuing aggressively computational science building the resources emphasizing high throughput and yes building better public and private partnerships for us it's been more than just biology even though for the little islands that we represent within the department of energy biology has always been central and we see it as encompassing the other disciplines computational sciences engineering chemistry material sciences and so on in the department of energy we are committed as I mentioned with the sequencing facility to building and operating for the broader scientific community the scientific user facilities that will be central to this new century of biology the light sources that the department of energy has built and operated were now dominated primarily by the science of structural biology and yes by structural genomics a little example you heard from Shirley Tugman a little while ago the department of energy is committed to maintaining these facilities and in fact building the next generation the fourth generation of light sources perhaps based on the free electron laser that can do even more for structural genomics and structural biology very important in this vision for the future is computational science and on this also on this field we intend to play a major role in order to support the broader scientific community's pursuits and needs in computational biology here's a little graph we came up for in the context of the genomes to life program representing our future in the post human genome program world this is one example of the kind of challenges we face in computational biology and the various problems we have starting from where we currently are with respect to comparative genomics and maybe a bit in terms of genome scale protein threading this is in fact our current computational capability which is in the 10 teraflop for those that are teraflop literate the fact of the matter is if we are to accomplish some of the goals that you've heard about with respect to molecular based cell simulation that Francis spoke of or the protein machine interactions that Eric alluded to we need orders of magnitude greater computational prowess we need something in the teraflop range and the multi teraflop range this is 10 to the 15 floating operations per second and in order to accomplish this we need to make significant investments in the computing science the basic computing science to realize that vision clearly for us the future in the post human genome project world is the program that was identified earlier and that's in the issue of science that you picked up perhaps this morning it's the genomes to life program a labor of love from many many people starting by our statutory advisory committee the biological and environmental research advisory committee chaired by Ray Jestland and many of his colleagues and across the scientific community that brought came together over the last three years to give substance to this vision and it's a vision that have that has significant and potentially valuable applications to the department of energy's goals and missions here are some perhaps futuristic ones but are the nevertheless realizable given the successes that this project the human genome project has had over the years I show three examples here in terms of bioremediation and the potential to save billions of billions of dollars in toxic waste cleanup and disposal which is one of the tasks the department of energy is saddled with from the legacy of the cold war another one is to help stabilize atmospheric carbon dioxide through carbon sequestration to combat global warming and another one is to contribute to the US energy security through bio hydrogen based industries for example in essence clean energy through the miracles of biotechnology one can say these are futuristic dreams they are however dreams that could be realized with the proper investments in the basic science that is described in the genomes to life program in just a few words the genomes to life program has four basic goals the first goal is to understand the fundamental multi protein molecular machines of life that make a cell work the second one is to understand the regulatory networks that drive these molecular machines of life the third goal is to relate the functional diversity of microbial communities whose tasks are primarily focused on microbes and microbial communities especially in the case of bioremediation microbial communities that exists at our contaminated sites the fourth goal which underpins all others and it's one that I made reference to is the computational infrastructure that needs to be built to support these goals that you'll hear from Francis and that you've also heard from other speakers earlier today so join us in realizing this potential of the genome revolution and read about us in the science magazine our article that you received today or visit us at our website doegenomestolife.org I think we all stand at the edge of an incredible region of undiscovered territory and we are excited and thrilled by the potential of exploring it properly and putting it to use for our goals and for the good of humanity I hope we will be able to meet that challenge and accomplish those goals and I thank you for your attention Thank you very much Ari and it's over to Francis who's going to give the closing talks and close out the session I might not get a better chance ever to thank Francis publicly not just for your stewardship Francis I have no doubt that we wouldn't be here but for you but for your staff who I think we've just developed some strong relationships with and respect so much and I know you're going to be thanking them publicly elsewhere so you won't name them all but also to say thank you for bringing us to an end and thank you for letting us focus and go out with a bang and not a whimper Thanks Richard well yes bringing us to an end not only of the genome project but of a long day and a wonderful day it has been I hope you also have had the same sense of awe at the lineup of presenters that have come to this podium since this morning and a similarly spectacular lineup will be here tomorrow I am impressed by you and by the goals who are still here and I will not abuse you by going on at great length but I did have a couple of things that I wanted to share with you particularly about the vision for the future of where we might go next before I do that I'm not sure that all of you saw because it was just released at noon today a really quite a remarkable statement coming from the heads of government of the six countries regarding the completion of the human genome sequence these six countries who work together to do this and so I'm going to read this to you because I think it is unprecedented for such a statement to have been made and it undergirds the historic significance of what we're celebrating today in a rather remarkable way it reads we the heads of government of the United States of America the United Kingdom Japan France Germany and China are proud to announce that scientists from our six countries have completed a sequence of three billion base pairs of DNA of the human genome the molecular instruction book of human life remarkable advances in genetic science and technology have been made in the five decades since the landmark discovery of the double helix structure of DNA in April 1953 now in the very month and year of the 50th anniversary of that important discovery by Watson and Crick the international human genome sequencing consortium has completed decoding all the chapters of the instruction book of human life this information is now freely available to the world without constraints via public databases on the world wide web this genetic sequence provides us with the fundamental platform for understanding ourselves from which revolutionary progress will be made in biomedical sciences and in the health and welfare of humankind thus we take today an important step toward establishing a healthier future for all the peoples of the globe for whom the human genome serves as a common inheritance we congratulate all the people who participated in this project on their creativity and dedication their outstanding work will be noted in the history of science and technology and as well in the history of humankind as a landmark achievement we encourage the world to celebrate the scientific achievement of completing the human genome project and we exhort the scientific communities to rededicate themselves to the utilization of these new discoveries to reduce human suffering and it is signed by His Excellency Jacques Chirac the Honorable George Bush the Right Honorable Tony Blair His Excellency Gerhard Schroder His Excellency Eunuchiro Kozumi and His Excellency Wen Jabao those are the heads of state of the six countries isn't that amazing so I couldn't deny myself the chance to read that to you and that was just too stunning to find that coming out here in the middle of today well what I want to do in a few minutes and it will be only a few because I'm going to exhort you to read more about it in this article that you were given as you came to the meeting today I want to say a word about this vision for the future a vision which we at NHGRI have been working on for more than 18 months and many of you in the room if you didn't run fast enough found yourself being pulled into that process by being invited to workshops to early house meetings by being called on the phone by various of us to seek your advice so this is very much a joint enterprise and well it should be we've heard a lot today about the human genome project about the things that were accomplished during its 13 year enterprise all of which are detailed in this visual which is the center fold of the article on the vision for the future but it's the to be continued down there in the corner that I think we should particularly now be thinking about and which will be the major focus of what is being discussed tomorrow and has already occupied a lot of this afternoon because after all we have accomplished all these original goals and the question on everybody's lips is what's next and preparing for that has been a process involving more than a dozen topic specific workshops that we have organized some with our colleagues and other institutes over the course of the last 18 months and that has involved both an early meeting at early house and then a more recent early house meeting where we tried to pull all this together as well as significant involvement by our council having been after all the ones to finally endorse the enterprise as it came out and I want to express my gratitude particularly to the council and to the subcommittee that led this enterprise particularly to the 600 scientists who were involved in the effort to distill all of these wise ideas about priorities into this vision and most particularly to my colleagues Eric Green Alan Gutmacher and Mark Geyer who slaved through more revisions of this document than you could possibly count in order to produce what you have in front of you and of course if you turn the pages you'll quickly encounter this metaphoric image and it is an image that we intended to carry a certain message and I hope it does that for you because you will notice that this is a building a building we want to now put together we need a good architect in order to design it and this in fact is an article that aims to lay out the blueprint of this building to design its architecture it's a solid foundation that's the human genome project down there and it has three floors this building does genomics to biology to health and to society it also has these six cross cutting elements which touch on each of the floors because they're important for each of the floors and they are as you can see labeled resources technology development computational biology training LC and education please note if you will the pathway up to the door is handicapped friendly we are very much interested in reaching out particularly to those who need medical advances to happen quickly because that is our dream for what the genome project will produce and notice also that the door is wide open because we will continue the process of having our data open and accessible to all and that is very much underlined by the words in this document and by a recent important meeting in Florida where the importance of pre publication data release of all kinds of data that represent community resource projects was laid out in a fashion that I think was very well received by both data producers and data users because I don't have time to go through the 15 grand challenges that are outlined in this document I am just going to mention a few in passing and again I think it's important to note that this is unlike the previous plans for the genome project that you may have read in previous years in science where we were basically putting together a description of how we would achieve the goals of the human genome project those are now achieved so in a sense we are really doing what Bruce Alberts and his panel did back in 1988 starting with a blank slate and saying where should we go next and of course the slate is not really blank it's already been written upon by a lot of people with good ideas and it's our task to try to sift through those so in the genomics to biology floor you will see things like defining the structure of human variation obviously now already underway in the elegant talk you heard from David Bentley with the production of the human haplotype map and all the ways that that will get used the sequencing of lots of additional genomes also very much underway as you heard from Bob Waterston and Eric Green and others to develop new technologies for sequencing, genotyping expression analysis and proteomics those are critical I think that point has been made repeatedly today that we should never underestimate the value of advances in technology they will often be the things that allow the real leaps forward we need to identify all of those functional elements of the genome we are initiating a project called ENCODE which stands for the Encyclopedia of DNA Elements that invites all investigators be they experimental or computational be they in the public or the private sectors be they in the US or elsewhere to focus their energies together on a carefully chosen 1% of the genome already identified and to get everybody to put their heads together and figure out how can we really identify which parts of the genome do what and that is a very exciting idea a cooperative enterprise to figure out how to determine the parts list of the genome and if that succeeds and we expect that it should then we'll be able to extrapolate that to the other 99% we need to push the proteomics agenda and there again I think technology is going to be critical in order to identify for mammalian cells all of the proteins and their interactions and ultimately though I take the points already made by my friend Ari and by David Housler that we have quite a challenge if we're really serious about developing a computational model of the cell still a worthy goal one that sort of electrifies the imagination I mentioned technology let me just put a few challenges out there which if achieved could change a lot of course we heard about the haplotype map in its construction but the utilization of that map in a broad sense to look at virtually any disease in a case control whole genome association study will be dependent upon drop in cost for genotyping so that you could carry out such studies for $10,000 or less on thousands of DNA samples that cost curve is coming down it needs to come down quite a bit more so I'm hoping that within this room or over in Missour if there's anybody still over there or the people who are listening to this on the web or by satellite download or wherever we'll begin to think about new ideas about how to do that or to do this maybe we wouldn't worry about genotyping so much if we could just sequence the whole genome and be done with it for $1,000 or less and there are new technologies that you've heard about briefly at least at this meeting the sequence single molecules using approaches like nanopores which if they can be reduced to practice hold the promise of allowing us to jump to a whole new Moore's law curve than the one we're currently on the one we're currently on is pretty cool because we're dropping the cost of DNA sequencing by a factor of 2 every 22 months but wouldn't it be great if we could jump to another curve and start down that one because if we want to get to $1,000 for a mammalian genome we need a new technology or we need another 15 years and we're impatient wouldn't it be nice if you didn't have to go to all the trouble of reconstructing and cloning and putting together the fragment that you want to use to make a transgenic mouse or do whatever experiment you're interested in and you wouldn't have to keep freezers full of all of those clones that you've mostly forgotten where they were and they've lost their labels and so on we would love to move into an era where when you want a particular DNA molecule you just punch it into the computer and out it comes in a couple of hours with high accuracy and low cost and moving into the epigenetics is an area we need to push to we need to understand methylation and other epigenetic phenomena and we need to of course look at modification states and abundance of all proteins here's a challenge in a single cell in a single experiment again these are quantum leaps they will be enormously difficult to achieve but we are intentionally putting them out there as challenges for the scientific community perhaps the floor that deserves now the most intense attention is this promise that we've always had that the genome project was going to move its benefits into health once we had a lot of the materials together and now we do and so in the genomics to health arena the focus on identifying all the genetic and environmental risk factors for common disease is a real possibility the ability to develop sentinel systems that can be used on currently healthy individuals to identify early onset of illness even before symptoms have appeared and those same kind of systems as elegantly described by pat brown to develop a new molecular taxonomy of all disease one that I'll come back to in a minute is the business of chemical genomics where we try to take advantage of the wealth of small molecule technology and make it available to academic researchers I'll say more about that in a moment if we're going to see all of this really reach into all the places that it should we're going to need large human cohorts in order to assess genotype phenotype correlations in an unbiased way and we need those cohorts to include not only people who are ill but people who are unusually healthy because they may tell us interesting things about genes that confer resistance to disease along that same line our study of genetic variation may very well help us sort through the complex area of health disparities most health disparities I suspect don't have much to do with genetics they have to do with access to health care they have to do with socioeconomic status and culture and other things but we have a chance to begin to unravel that and furthermore as David Weatherall will talk about at the end of this meeting tomorrow we have an obligation and an opportunity to apply genomics not only in the developed world but also in other parts of the world that are very much in need of such advances let me say just a word about this high throughput robotic screening because I think it's a topic which is less familiar to many academic researchers but which in fact could be a paradigm shift if we can learn how to appropriately develop the opportunities think about the genome translation toolbox that has now been developed what's in it well, things like arrays to look at transcription things like knockout mice things like full length CDNAs now being produced by the mammalian gene collection program here at NIH things like SIRNAs to knock out expression things like the databases that we all depend on and small molecules well, what about that? of course, this is a central focus of what the pharmaceutical and biotechnology industry are doing as the first step towards drug development you make an assay you screen a library of a very large number of molecules and you identify those that have an interesting effect be it agonist or antagonist why could we not in fact make that same technology more broadly available to academic researchers in order to facilitate the identification of very useful probes for biology and the identification at an early stage of compounds which might ultimately become therapeutically useful there are reasons to believe that such an initiative is an idea whose time has come this new paradigm of scope may be required to assign function to the genome access currently to small molecules is limited certainly in academia and actually the technology in pharmaceutical industries is also right for further advances there are 4 convergent developments in the last 5 years that particularly make this an attractive initiative at this time the human genome project advances in combinatorial chemistry that allow you to develop large libraries of molecules that do a good job of representing chemical space compound brokers from whom you can buy such collections already put together from reasonable prices and automation technology from the genome project that allows you to do very high throughput screening at a reasonable cost and putting all those things together suggests the opportunity of a public sector screening and chemistry initiative just quickly to say how this might work those of you involved in drug development would be familiar with this pipeline which begins with identification of a target and then development of an assay screening, high throughput or otherwise some limited medicinal chemistry to take that first set of hits and convert them into compounds of reasonable specificity and solubility and then the really hard work begins if one is going to go on to put this into clinical trials with lead optimization toxicology safety and then phase 1, 2 and 3 and sometimes more than that trials the success rate and the cumulative cost curves for this pipeline look like this now what is the public sector's current role in contributing to this well it actually is fairly limited it is probably primarily in most academic labs devoted to the business of identifying targets but notice this sweet spot that's here in terms of the success rate and the cumulative cost what we are proposing among many other things in this vision is that we contemplate the idea of moving the public sector contribution to this enterprise to the right to encompassing the ability to do assay development high throughput screening and those first steps of medicinal chemistry optimization in order to generate useful compounds which investigators can use to probe pathways and if they turn out to be interesting to hand off through an appropriate licensing agreement to a company that will turn that into a therapeutic agent I think that is a very exciting paradigm it's an unfamiliar one to many academic researchers there are few labs out there beginning to do this and it has a feel of something that could really change the way we approach problems in biology and medicine finally before I run out of time if I haven't already genomics to society that third floor is just as critical maybe even more so than the rest of the building because if we don't focus on these issues and give our energies to them these advances may not have the benefits that we all dream of we need to continue to push the privacy and discrimination agenda that the United States Senate currently wrestling with two fairly similar bills on genetic discrimination will figure out a way to craft a compromise and will in fact put that through and this will be the year where the genome project was completed the year of the 50th anniversary of DNA that is also the year that federal legislative protection against genetic discrimination is achieved in the United States we need to think about patenting and licensing practices not just about genes but also about haplotypes and protein structures and a whole host of other entities that lie ahead of us for which I think unfortunately we're not all that well prepared in terms of how we're going to handle intellectual property very importantly all of this study of variation is going to have profound consequences for our understanding of race and ethnicity and we are obligated as scientists to learn what we can and then translate that into information that will be usefully included as part of the often contentious dialogue about race in our society as we learn more about variation and its role in disease risks undoubtedly we will also learn about its role in behaviors including some that are quite controversial and we need to be prepared for how that kind of information is going to be incorporated into our thinking and I would say that it is also our obligation as scientists to define that there are certain areas that ought to be considered off limits and I shouldn't have said as scientists in society together with scientific input and societal norms there may be areas such as for instance almost everybody currently considers reproductive cloning to be areas that we don't wish to go into who decides and then how does one enforce those boundaries is a question that we have to wrestle with so there's our building read more about it in this article if you will we'd love to hear more feedback from you about it it is something that I think will be guiding us although it will require revision I'm sure on a regular basis it is I think inspirational to consider this as a real opportunity for the future and to consider what we might do here I offer you this quotation which are the final words in this particular article taken not from a scientist but from an architect an architect who built quite a few buildings here in DC as well as in Chicago and other places Daniel Burnham make no little plans said he they have no magic to stir men's blood themselves will not be realized this might have been the motto of the albert's panel I guess but it could be our motto here today as well make big plans aim high and hope and work remembering that a noble logical diagram once recorded will never die but long after we are gone will be a living thing asserting itself with ever growing insistency a wonderful goal for the next several decades thank you all very much