 So I'm Maynard Olson, I'm Professor Emeritus of Medicine and Genome Sciences at the University of Washington and the emeritus part is for real, I actually did retire a few years ago and spend very little time at the University of Washington and I'm involved in a variety of other activities but not any longer active in research. And I grew up in Bethesda on Edgemore Lane, yes, my father was an intramural NIH researcher and what was then called the Division of Infectious Diseases and at that time responsibilities had not sorted out between the CDC and the NIH to the degree that they have now and most of my father's work was more similar to what goes on now at the CDC than what goes on in the NIAID but at any event I kind of grew up in this culture and left, I was born here and left my senior year in high school but to the West Coast where I've spent a lot of my time since then but I went through the public schools in Bethesda, Bethesda Elementary, the no longer extant Leland Junior High School and BCC. I can remember the NIH campus when I was a young child still rented some of the land to a neighboring farmer to graze cows on because the initial land gift here was larger than the NIH could put to scientific use at that time and that was when the NIH particularly to the north was surrounded by farmland. So things have changed. I was always attracted to the sort of basic physical sciences and probably would have become a physicist but particularly at Caltech quickly became aware that many of my peers had more facility with mathematical physics than I did. I like to think that I can actually understand these things but it takes me longer than it took them to and I thought chemistry would be a good compromise. It's a basic science that chemists are proud of their field as being what is sometimes called the central science. Whether you're a physicist or a material scientist or environmental scientist or a biologist you've really got to deal with chemistry. We live in a chemical world. We're chemical entities. So it was a good choice. I stuck with it through graduate school, got my PhD in inorganic chemistry. Physical inorganic chemistry, studied the mechanisms of small molecular reactions. All the while ignoring biology completely it's impossible to exaggerate my level of ignorance about biology. It is still true to this day that the only biology course of any description that I've ever taken was at BCC, 10th grade, Mr. Butterfield's high school biology course and I didn't take any biology in college, not even any biochemistry and less so in graduate school where I was really on the sort of chemical physics my first paper. One thing that Francis Collins, the current NIH director and I have in common a little known fact is that for both of us our first serious scientific paper was published in the Journal of Chemical Physics. It's not too well known even here on campus that Francis that is he was an MD-PhD but the the PhD part of it was in quantum mechanics. So there's a long tradition of chemists migrating into biology and Francis migrated by his route. He knew a lot more biology of course when he did his migration because he had an MD. I knew nothing but that's always been my style. I prosper in fields where I don't know very much going in because something about the way I learn things requires that I start from scratch. I don't do well in fields where you have to start with a lot of highly structured pre-existing knowledge. So just for example in chemistry I was never good at organic chemistry. Organic chemistry is a fascinating subject but the people that are good at it develop a tremendous amount of highly structured kind of pre-existing knowledge and then they attack a new problem from that reference point. I've never been good at that. I like to stay closer to things that I've just had to sort of slowly figure out for myself. That's just a characteristic. At that time at postdocs were actually not much of a feature of the chemical education landscape. They existed but there were relatively few of them and they were not an obligatory step. So right out of graduate school I took a real job, a tenure-track job at Dartmouth College in the chemistry department and with the still the intent of doing sort of physical and organic chemistry research and teaching and spent a few years there but fairly quickly discovered this really was not a good long-term plan. I liked the teaching but sort of needed a fresh research topic and so molecular biology was an obvious one to look at. It was a very exciting period in molecular biology. The period after the discovery of the double helix and kind of the working out of the genetic code and the mechanism protein synthesis and so forth that had stabilized but there was a big sort of what next kind of question hovering over molecular biology at that time that even an outsider such as myself could grasp and was strongly attracted to that. There were of course a lot of molecular biologists that were eager to get on with studying what were then the traditional topics of you know basically transcription, translation, control of transcription and so forth in ever more mechanistic detail that didn't appeal to me. I had actually done a lot of mechanistic studies in chemistry mechanisms of reactions and as part of what I wanted to get away from my view was that these highly reductionist approaches are extremely effective in their early phase and then fairly quickly get into a phase of diminishing returns. So I was not interested in that asymptotic sort of phase. I wanted to get in on a more ground floor of something and so with the rather vague ideas of this sort in mind I negotiated an early sabbatical from Dartmouth. I did some extra summer teaching and so forth I really wasn't eligible for a sabbatical but took one anyway and it's a complicated and sort of personal story as to how I ended up in Seattle but my sabbatical was in at the University of Washington. We will discover if we keep on this biographical track that you know my relationship with the University of Washington is less straightforward than it might seem. People like to give formal introductions of me because I was at the University of Washington. I went then to Washington University and then back to the University of Washington and of course was born in Washington DC so in any event this was my first phase at the University of Washington was unsabbatical from Dartmouth working in the laboratory of Ben Hall to say a highly distinguished molecular biologist who shared a few things with me that made us a really good match. His PhD was in chemistry, biological chemistry to be sure. He worked on nucleic acids but from a very chemical point of view, Baldoti, he and he likes to do new things. If you look at Ben Hall's CV it's a series of new things. He has an attention span of about five years and then he moves on to something really new and I was a bit like that although I like to think my attention span is longer maybe 10 to 15 years but I also like to move on to things that are new. So what was kind of new for us was somehow this sounds an absurdly vague idea but it's pretty much the way we talked about it at the time is that the molecular genetics of at least simple eukaryotes we're working on yeast saccharomyces was actually very well developed by 1974 when I showed up in Ben's lab quite an impressive intellectual and technical edifice doing Mendelian analysis in yeast unlike bacteria where most of the early molecular biology was done you know yeast has Mendelian genetics in fact it's Mendelian genetics is often been described as sort of so good it looks like it was designed by a geneticist it's an immensely better organism than peas or people to do Mendelian genetics on for various kind of technical reasons and this was all quite well developed. There was no molecular genetics in yeast a little biochemical what we would call biochemical genetics sort of protein level and a few interesting fusions between the Mendelian genetics and the biochemical genetics but nothing that we would currently really think of as molecular genetics but 1974 was the year that the first recombinant DNA papers were published and they were much on Ben's mind and actually mine I read about them in the New York Times a somewhat famous first front page paper in the New York Times about the development of recombinant DNA methods Cohen and Boyer's work and it's mostly famous because the an intellectual property lawyer at Stanford read this article and told the university that gee they should look into patentability of this method which neither Boyer nor Cohen had considered and they were right at the kind of the deadline for doing that but managed to get a patent. I read about it and said wow this sounds like you're going to be able to get at genes as sort of chemical entities and there was actually you know a lot of nice work in bacteria that had been done kind of getting at the genes but it was all done biologically by you know these rather esoteric methods like transduction and you know with prophages that excised imperfectly and various things just the vocabulary tells you that this is a very biological approach. Cohen and Boyer I could understand this you know you get these molecules and you manipulate them that's what chemists do and then you put them into cells and there was a generality to it all that greatly appealed to me and also it fit the criterion that I outlined earlier that nobody knew anything about these methods they Ben Hall and there was was really interested in getting them going in his lab it didn't have them going the only person in Seattle who had any experience with him was Stan Falco who was even then and is certainly now a sort of distinguished student of the molecular genetics of bacterial pathogens and that was key because of course it was bacterial technology and he was much ahead of anyone in the genetics department where I was and so we learned some things from him about doing recombinant DNA but anyway there was no knowledge base and our goals were vague we wanted to get the Mendelian genetics sort of together with the with the recombinant DNA methods and that I was good for this to take on this project because you know the biologists in Ben's lab and the genetics department you know they all had this functional orientation and so they were always looking for some project that would teach them something functionally about biology I was ideal because although of course I understood that function I understood then and understand now that function is really the ultimate goal in biology I looked at what people were actually doing to learn about function and the methods all seemed to me just so hopelessly underpowered I was coming from chemistry where we actually had some tools and you know we had NMR we had you know a very sophisticated spectroscopy we had we had stable isotopes and radio isotopes and so forth we had a lot of tools and we could actually learn something about the functions of molecules you know looking around the genetics department I loved the environment that people were you know they're very smart you know Leigh Hartwell was right at the kind of the peak of his you know getting his his approach to studying the cell cycle off the ground you know these were he was another Caltech graduate I hit it off early on with him he was a young faculty member there and stayed in touch with him for you know throughout our both of our careers but they I love the people I like the environment but I looked at what they were doing you know they had toothpicks and petri plates and they were sort of streaking these colonies out and using velveteen I don't know this this wasn't going to get us very we weren't going to learn very much about functional biology by these methods so I wasn't concerned about getting a project that would get me to study function yeah I wanted to get these methods kind of sort of build I wanted to work on methods because I thought that's what that's what this field needed was immensely more powerful methods and and recombinant DNA looked like the uh foothold so that was kind of my postdoc was working on that and it it went well actually and I think a lot of it was our our point of view and also another thing about chemists is that chemical experimentation is very like because of all these methods and so forth much more sophisticated business chemists are not easily discouraged by experimental problems in fact chemistry was way past the stage even then and as you much farther past it now when you could hope to discover anything interesting and chemistry without developing new methods you know the methods had been you know the methods that were very well understood had been applied exhaustively and if you really wanted to learn something new about molecules you needed some new way of setting them so I was quite comfortable with this whole notion and you know I think key to Ben and my approach is that we were kind of scrappy experimentalists and didn't give up easily and and there were a lot of experimental problems but the uh it did go it did go well we uh yeah we really combined I don't know how much detail it's worth going into but we I think we we combined Mendelian genetics and molecular genetics in some reasonably new ways and didn't learn anything functionally I think I left Ben's lab not having learned one new fact about the yeast cells that you know how they do things but we knew a lot more about the genome and the genes how they were organized how and and more more than anything else kind of how to get at them than we knew five years earlier when I first come there so anyway it it went well right from the beginning so I I quit my job at Dartmouth and went went back to went back to Ben's lab I took a few had to take a few months to settle my affairs at Dartmouth and then I got back I basically spent five pretty continuous years there and it was during that period that I collaborated with Ron Davis it was actually only in Ron Davis's lab for one week Ron Ron and I still sort of joke about this that uh the uh but Ben had arranged this collaboration I I didn't know anything about work in Ron's lab but Ben recognized that he really was at the cutting edge of recombinant DNA techniques then and that expertise didn't didn't exist in Seattle and uh they he arranged anyway uh Ron hosted me uh yeah extremely talented graduate student uh John Cameron who was I worked with you know kind of at the lab bench John interestingly uh later went into clinical medicine psychiatry I think and did not pursue a molecular biological research career but he was to this day is one of the very best experimentalists I've ever seen at the lab bench and so in a week I learned how to make lambda clone libraries and screen them and brought that back to Seattle and that was one key part of our success was having some of one of Ron's many contributions to molecular biology was that uh you know he also came from a chemistry department just as an aside in fact the Caltech chemistry department but uh he uh he also simply believed that uh that these technical problems that were ubiquitous in molecular genetics at that time uh not only needed more work but they they needed good solutions I think that's the one thing I learned from Ron don't settle you know for a halfway solution that barely gets you by your immediate need uh if the if the problem is a you know it's a fairly fundamental one uh technically speaking solve it well uh because then that solution will have a whole variety of applications not just to your project but that's how the field really advances the attitude and this attitude served me extremely well and I really did get this from Ron the the tradition in molecular biology I mean I was hardly the only person who discovered that it was hard to do these experiments and make them work but the tradition was a scraping by tradition that you get this stuff just to work well enough to do your project kind of and I got much more of this attitude from Ron that you should get these methods working well and then when you move to the next step of complexity get those methods working well that you can't really build on methods if you're just scraping by I mean I'm you know a huge fan of Ron's I had this early contact with him but I've admired his work ever since he uh a few years ago I was pleased that I was I was on the selection committee for the Gruber Prize in genetics just have a major prize and and I was able to play a significant role in persuading them to award this to Ron because I think he's under under appreciated yeah his contributions are so ubiquitous and so manifold that it's hard to know where to start I remember on the Gruber uh in the Gruber discussions that uh that Jasper Rine a star postdoc of Ron's wrote a letter of support and I was joking with Jasper about this letter that I think that I'm not revealing anything here that's meaningfully confidential and all parties here would be happy to know about this story that he violated every every known rule in writing this letter for we all write a lot of letters like this and he violated every known rule because it was about five pages long and it rambled from this to the next to the next to the next but that's what was needed I mean if you go through Ron's contributions you know it is many things and if I my my best unifying theme is so when I've already used is that he chose he was largely methodological it's actually hard to associate Ron's long career with learning anything about how cells work I'm sure he would come up with some examples but they're not what stick it was he recognized the difference between kind of a protocol and a method that a method is you know some new source of leverage that that can be used to address a very wide range of problems so that I think that early early on he really was the one who got recombinant DNA working on scale you know that it wasn't a question of getting you know a few hundred clones and hoping that yours was one of them the one you were looking for was one of them it was a question of you know making libraries from complex genomes that had a hundred x coverage so that even things that were way underrepresented would still be there and methods of screening these libraries effectively in a you know in a day or two not through some long complicated thing that was a early phase but he always understood much better than I did when I went to his lab I was another thing I remember discussions when I was there that influenced me we didn't do these experiments but how he had a much more biological view of genes than I did I had a chemist view of genes you know these are polymers and they yeah yeah they had interesting functional effects but that was I was above my pay grade Ron understood that genes were you know their interest in genes they were boring polymers they were they were interesting because of their functional effects and that you had to study these and functional effects by getting genes into cells and that was a major theme of a sort of middle phase of his career was many different ways of taking genes from anywhere and getting them into cells usually predominantly use cells and the real strength of his of the all of the applications of use transformation that he developed is that he wanted to have very tight genetic control over the genes once they were there you know you can do a complication complementation test just by getting the DNA in there and as long as it gets expressed fairly quickly transient assays and various things dominated the literature for a long time Ron wanted to be able to make a chromosome out of it he wanted to to put it in a chromosome he wanted to be able to replace the you know the gene copy that was there to begin with exactly with no other alterations he wanted to be able to have them at high copy number not all at once of course these are different goals and so he gradually developed you know building on work there was a lot of good work being done in yeast at that time and centromeres and telomeres replication origins I think Ron's main contribution was to the replication origin story but you know you need to know all these functional parts of a chromosome to have this kind of control and that became important later in my career I think was a obviously a direct influence on on building yaks because they were yeast artificial chromosomes and they differed actually from the kind of thing Ron Davis had been doing much earlier only in that they involved large large segments of exogenous DNA as opposed to the few thousand base pairs that were the kind of 1970s and early 80s recombinant DNA technology so anyway he's and and and he's gone on and done you know still more things I think there's not any kind of direct lineage between Ron and you know say the next generation as it's called for some odd reason sequencing but but there are many indirect links because he understood that DNA sequencing was too hard and needed to be done much better and he explored kind of many approaches to that problem and trained a lot of people that played a key role in developing next gen sequencing and it's I think of him as kind of the godfather of next generation sequencing he's not the father of it but he's the godfather but he had a big influence on me and one of the characteristics of Ron's methods is that they work and that's not a shock and awe issue because you know that shock and awe is all about somebody got the thing to work once under you know some tenuous the scraping by kind of environment that's when people pay attention because it's new you know ballet or something is you know it was not a not a method you know not a protocol anyone else could follow and took many years of work to to make it possible even to clone sheep with any regularity much less mice and so forth but if you're working within a field there's a big difference between this great by phase and really having a good experimental control over the system you're working with yeah so that project was kind of a natural outgrowth of what I did in Ben's lab I didn't work on the project in Ben's lab but but by the end I was thinking about it Ben used to affectionately refer to it as my megalomaniac project but and I don't think that was a compliment actually but I you know we had all the raw materials to integrate the genetic and physical maps but didn't know how to do it on scale basically this project I did on tyrosine tRNA genes in Ben's lab involved integration of of the global genetic map because it was had been built by then at globally with toothpicks and petri plates but the and we we did physical map correlations using this cloning technology like from ron's lab did local correlations of little chunks of the physical map with chunks of the genetic map or loci on the genetic map but it was you know quite clear that the next step would be to have a complete physical map of yeast that you know sort of gene size resolution genes in yeast are not very big and so it you know had to have a resolution of say a few kb at least and yeast has a 15 million base pair genome and so you're talking about you know mapping thousands or you know perhaps 10,000 sites and restriction maps of those days you know they had 10 sites on them not not 10,000 the methods just wouldn't scale they they weren't very reliable actually even those 10 sites because of the methods that they had used just good enough that was this so I had begun thinking pretty formally about this problem especially the last year I was in Ben's lab and by then I finally you know could market myself as a geneticist despite the fact that I still hadn't taken any biology courses and and so I was looking looking for jobs and found a very good one at Washington University in St. Louis where they were building a brand new genetics department and they you know I don't know we had a kind of a shared view of where genetics needed to go with I think the zero baseness was very important I was not an attractive candidate to the best well-established genetics departments because I they could always find somebody better qualified than I was to do any particular thing but we had this kind of zero based kind of idea you know Bob Waterston was on the search committee that hired me at Wash U at any event as I was making that transition while I was still in Ben's lab I started thinking about this problem of how to how you actually would build a physical map across the whole genome I could picture pretty well how to do the correlation with the genetics map but it wasn't so obvious how to build the physical map and I but I had the basic idea by the time I left Ben's lab one I think the major influence on me during that year was Kim Naismith who's gone on to have a very distinguished career doing functional molecular genetics Kim wanted to understand how cells do what they do chromosome synapse and so forth what are the mechanisms and he has excelled at that but he he he was interested in in this idea of building a physical map and was really the person I talked to the most about it and the you know so the method I decided decided on was a generalization of what Kim and I in particular in Ben's lab were already doing and I think anybody who was doing recombinant DNA experiments sort of well meaning in the Ron Davis style did did what we did is that because in the Ron Davis style you didn't just get one clone and then go on and study it you know you got 20 or 50 and and the way you avoided artifacts was that they had to build you had to be able to build a self-consistent physical map locally out of these clones and if you couldn't do that then that was a good criterion for throwing out spurious false positives from the screen and so forth and so you would you know you typically would be probing for essentially a point on the chromosome with a hybridization probe and you would get a bunch of clones that hybridized or Kim was starting to do this functionally so he was selecting for by complementation in a yeast recombinant DNA library going into yeast but it's the same idea is that he was insisting that some particular point or modest number of base pairs be in all the clones but the libraries we Ron Davis always says he made libraries with random endpoints as part of the Ron Davis dogma so in Ben's lab we made libraries with random endpoints there are a lot of reasons why that's a good idea so you you would get out a bunch of clones that all had some point in them but they had random endpoints then you'd cut them with restriction enzymes and run them on a gel and you know they would share various numbers of restriction fragments depending on how much they overlapped they would share the restriction fragments in their overlap so we had done a lot of that and that seemed to me a method that would generalize to the whole genome to skip the probing step make a Ron Davis quality kind of library and just start picking clones the numbers were not that intimidating at least for yeast yeast has a 15 roughly 15 million base pair genome and the you could get inserts with in lambda clones which we were using at the time that were 15,000 base pairs so one x coverage of the used genome would just be a thousand clones and similarly you could get that many on one petri plate and you know Poisson considerations suggested that even if allowing for some non-randomness in the sampling that 10,000 clones or something should be enough well you know that's a lot looked at one way but it the appealing thing about it was that you're just there's a linearity to the experimental work if you needed twice as many clones it was only twice as many work much work or somewhat less than that because of economies of scale but the number of pairwise combinations that you could consider at the data analysis step when there's a square the number of clones and so obviously I plan to do that in a computer so this seemed like the ideal approach is that we make the experimental work linear and then make the map building the n squared process so if we were going to do 10,000 clones then we'd do 100 million comparisons and that was not computationally daunting it it wasn't entirely trivial for the computers of 1980 but it wasn't daunting you know I knew enough about computational complexities I'd been using computers when I was at Stanford in the chemistry department and use them quite a bit at Dartmouth where John Kemeny had been a leader in and really getting you know time-shared computing terminals out there where students and faculty could use them you know instead of having a computer in Stanford I used to walk out through the eucalyptus trees to some you know air conditioned building that was away from the center of things where they had a huge computer and type out punched cards and put them into the machine and leave them for an overnight batch run and so forth at Dartmouth I got this idea sort of that Kemeny you know Kemeny was sort of a Ron Davis of computing I you know I don't think he I don't think there was any fundamental contribution really to you know to computer science he was a mathematician but he understood that computers should be used they should be integrated into everything he was a sort of a Johnny Appleseed of this and he became president of Dartmouth and quite prominent and Dartmouth really pioneered they developed the basic language at Dartmouth yeah so it you know it's not a modern language but the name is important he wanted language that you know wouldn't have a steep learning curve he wanted Dartmouth students to learn it and then figure out what to do with it themselves not you know not have some highly structured assignments but get it involved in their work that a lot of that rubbed off on me when I was on the faculty there and so actually I didn't use computers at all in Ben's lab we didn't have any and I didn't I didn't use them at all but I I knew that we could we could do that we could do the n squared comparisons and I was pretty confident that we could do the n kind of complexity experimental work so now of course I hugely underestimated how hard it would be but you have to you don't start projects if you think they're going to be as hard as they turn out to be the amazing thing is I actually is that I got a grant there was you know there was no there was no genome kind of term genomics had not yet been invented there were no you know there was no place to send these grants there were no reviewers that were qualified to review them there weren't any genome grants I I'm quite confident that if you went through the vast archives at the NIH that you would find this was the first genome grant to come in the door and to the that anybody would recognize this is a genome grant the amazing thing is it was funded you know that you know there's those argument well you know they don't really fund things that are that far off the wall but it went it went to GMS and was reviewed by the genetic study section which David Remundini was the executive secretary of and had a lot of yeast geneticists on it and fly geneticists a model organism oriented section the although it was called the genetic section I I served on it for many years later it they didn't do mammalian genetics it was this was they basically did plant genetics and model organisms and so they got this grant and you know it had a I think you know had a it had a cerebral tradition I saw that later I don't know who the actual reviewers were but they they liked it and here I was it's assistant professor I had no preliminary data I did I had done some I did some computer simulations when I got right when I first got to St. Louis there were no computers in our new genetics department but I I found some over in an epidemiology kind of unit and found a mainframe that punched cards again I remembered a key point when the deadline for the grant was getting close I dropped my deck of cards on the shuttle bus and at that time the the instructions in a computer program they were just ordered by having the cards in order and I dropped the deck and and had to reconstruct the program by shuffling the cards and getting it working by the next morning and so forth but I had done some computer simulations that showed that I that that at least on on simulated data I would actually be able to build a map I didn't have any didn't have any any preliminary data anyway amazingly it was funded five five-year grant to a new assistant professor to do something that was just off the wall you know they weren't choosing the best of many grants of this kind they'd never seen anything like it and wished me well so I started to work and and that went on for it it was a good solid 10 years before we had a map that kind of still was not perfect but was pretty good and well correlated with the genetics map the genetic map and widely useful even in that time to use geneticists so I had to get the grant renewed once I actually the grant for another five years and this just shows how much times have changed so we'd published I think about three papers in the five years and they were all purely they were component methodologies how to do this part of it and this part of it we had not yet published our first paper showing that the method was going to work that we could actually put these component technologies together and and make them make a map and and it got renewed for another five years so I think that happened because by then the the project was well known in the youth community it's not well known generally but it was well known in the youth community and and we were from time to time able to help other youth geneticists with their problems in some part of the genome or another most of youth genetics at that time involved you know cloning and analyzing you know little segments of the genome and from time to time we could be helpful and people could see you know they I presented data meetings and they you know they could see this was this was going to work needed just a little patience and so that went on you know it was not a big project I did a lot of the work myself and I never had more than I never had a student or postdoc working on it that was true actually for the whole course of the project you know it was me and one or two technicians and in the later stages I would have usually one computer programmer and the so that's why it kind of went slowly but the pace was actually about right I think this is an important point that's not well understood about these early stages of technology is that if I had a lot more money there's no assurance whatsoever that it would have gone better and it might have gone worse because the reality is we didn't know how to we constantly ran into problems and we didn't know how to solve these problems brute force never works you cannot solve problems that you don't know how to solve by brute force you have to do some trial and error it's very difficult to parallelize you know you can't have one person trying one thing and one person trying another and some Darwinian kind of a struggle I've never seen that work you know you need a small group very small group that figures out how to solve these problems and and it's difficult to rush this is a generalization about the history of genomics is that it's actually hard to explain you know you take some silicon valley type I mean they're the worst they're the worst because you know they're smart and think that the kind of lessons that they've learned from the growth of it generalized to all of life and it tried to explain to them you know why it was you know why it took this long took as long as it did it was only you know from the time I started working on the yeast map that was 1979 you know to when the human genome project had had a fairly good sequence of the human genome it was only a little over 20 years and the number of people working in the field sort of grew exponentially and funding grew exponentially through that whole period and eventually the field was mature enough that a lot of parallel trial and error could go on but anyway to try to explain you know why it you know why it took 20 years to somebody in it is difficult because and the core reason is that they they're not dealing with the real world they're dealing with a an idealized world that they create that's what computer science is it's a idealized world you know the transistors are on or they're off you know a bit is either set or it's not set and the deductive logic and the combinatoric kind of finite mathematics works and applies and now most of them don't actually understand what was going on with the electrical engineering side of computers I mean to get computers reliable enough so that they so that software could become the major problem took many decades because a real transistor of course is not on or off you know that's a complicated device that you know has its own ideas about that there are big quality control problems all these kinds of things but that's not part actually of the silicon valley legend the silicon valley legend is that somebody else you know figured out how to build these chips that were reliable enough that everything became a software problem and well in in recombinant DNA techniques just in experimental biology you're not working with materials that are as easily modeled as doped silicon and so we had you know bigger problems everywhere you look and they were messy problems and they took a lot of trial and error and this is a history of the human genome project that has not been written and is worth looking at is you know really actually what were kind of even if you write a pure wig history of the human genome project much less a history that captures the confusion that prevailed at along the way you would quickly identify at least a couple of dozen and that would be a very sparse list of problems that got solved that proved to be critical that no one recognized very far in advance of sort of colliding with this kind of problem and where there where there was no consensus about what the best way of solving the problem would be and and often even the people who solve the problems didn't recognize the really the really high bounces where the solutions that had unintended consequences and they good unintended consequences that is they they affected multiple things I mean I just could give a very trivial example would be that in the in four color fluorescence sequencing you know it's still dependent on electrophoresis with single nucleotide resolution you know these are polymer molecules they did actually how they migrate actually their sequence actually matters not just their length whereas the kind of the idealized model depended on single nucleotide resolution by length but independence of sequence and so you got these compressions and unsequensible sequences because the molecules adopted odd confirmations and so forth and people tried running the gels very hot and putting in a lot of denaturants and so forth none of it worked very well a major breakthrough and the only one but a major breakthrough was that for completely independent reasons the the fluorescent labeling kind of went from from sort of five prime end labeling to three prime end labeling of these molecules with determinators and the the dyes you know were so highly decorated the the nucleotides that were being added that were so heavily decorated with all this organic chemistry that didn't belong there that it it turned out to interfere with the formation of the hairpin structures at the end of ends of a lot of these molecules that caused a lot of these compressions and unsequensible regions so that was an example of an unintended consequence of a couple of different things going to determinators making these energy transfer dyes which required a lot of a lot more modification of the nucleotides and it you know it solved a problem that had lingered for years and years largely solved a problem that had lingered for years and years and and there are many other examples but anyway our 10 years on the yeast project I you know I it's a that would be a micro micro history but I could I could defend most of them obviously I made you know there are some places you just find I made a mistake read the data that we already had wrong and in retrospect it was pretty obvious I should have done something different but but on the whole I can defend most of that time in this way you know we just had to get kind of muddle through and but always using kind of the Ron Davis rule that that you know of course initially you muddle through but then you figure out you know what what what's what am I basically doing right that's solving this problem and let's really understand the problem now and make this a robust solution because genomics has always been experimental genomics has always been it you know very frustrating from a process point of view you know it has a zillion steps and and and and there's no none of them is strongly rate limiting this is a process engineer's nightmare is you know many many steps you know starting with you know some blood that's drawn from a you know a patient and ending up with a gen bank file there are all these steps and the and and and there really is just not a rate limiting step in there there's nothing even there you know there are 10 or 15 steps that you know very slight changes in the way that you're doing things can shift shift the burden but even then they're not typically strongly rate limiting and so that means that no one thing you do is going to have a big effect that's just reality and that's as true today as it was then it's just the way it is now of course the you know the methods are much better now and they're fewer steps they're still a lot but they're fewer steps and and they work better but there's not you tell me what what's the rate limiting step in clinical sequencing you know you've got a cancer patient you want data now you know you take a blood sample or a tumor biopsy or something and you want to hold genome sequence of this thing well there are series of steps that you've got to go through and and making any one of them go even go away completely doesn't actually change the throughput very much because it's just the way it is and and these are messy materials as I said this is not dope silicon we don't have good models for most of these steps actually so there's a lot of empirical work that goes into characterizing kind of how you get robust protocols but that's a so that was the use map you know it it it it it worked took a long time the uh we had good you know good relations from really the beginning with the only other project which was John Sulston and Ellen Coulson's nematode project that was I learned Bob Waterston was the intermediary because he trained like like uh I guess like John Sulston but John Sulston had been part of Sydney Brenner's kind of MRC nematode group I'm not sure what exactly what his role was uh how you know how that happened but anyway he was a card-carrying member of that small group Bob Bob Waterston was a near charter member himself having postdoc there and uh and he was uh you know he was my neighbor at and cheerleader chief cheerleader uh through this whole yeast project phase uh at Wash U and so he was in close touch with John and and was the only person initially who realized that I was setting out on this yeast project and they were setting out on this worm project with similar goals and and certainly closely related methods they they methods differed in detail but but they were random both random clone strategies that involved getting these restriction digests the end you know end complexity picking of clones and n squared complexity map assembly by in a computer and so they you know you can't have better competitors than these you know first of all we're you know really great people uh outstanding scientists and uh just uh you know pleasure to interact with and we traded a lot of information throughout and and importantly uh we both had the similar attitudes toward what we were doing and uh we're both reluctant to publish uh kind of a landmark paper uh because it was we had too many problems and I didn't want to sweep them under the rug uh but finally uh Sydney Brenner who had been watching all this pretty close up uh sort of decreed that it was time for papers and uh so he instructed uh John to write one and me to write one and uh can't say no to Sydney Brenner you know obviously it was very generous for scientists of his stature to take such an interest in this and he communicated these two papers back to back and in the PNAS in 1986 and uh and that was he was absolutely right he he had a much better sense of the kind of politics if you like of genomics he saw that that that that was that was kind of the end of the period when uh you know these you know small groups could work year after year after year pretty much on their own uh with essentially no competition and not much interest in what they were doing and uh that it was uh you know going to become a big field and uh would would have the acquire the dynamic of a big field as opposed to a peripheral activity within molecular biology and genetics so we did we published these papers and you know we were both I think at essentially the same stage uh they were working on a bigger genome but they you know the uh they were you know they adjusted details of their strategy so that the amount of work that they had done was comparable to the amount of work we'd done the uh they had the same continuity problems that we did and uh but also had the same successes you know they were you know we were both at the stage where we were building very good contigs uh they were disappointingly small uh typically uh you know the typical contig was only a couple of tiling links of whatever kind of clones you built them out of and uh now my computer simulations once I got the cards all in the right order at my uh deck at cards back in you know many years before when I had simulated the problem I got bigger contigs um and uh there are a lot of reasons for that but uh that problem actually continued to plague genomics uh and uh is not totally gone today the main reason that the human reference sequence is sort of god's gift to uh the NHGRI's gift to kind of the uh genomics community is that uh is that it has excellent continuity it's not perfect but it's excellent and achieving that level level of continuity today starting from scratch is extremely hard uh but having one prototype it's relatively easy to assemble uh very similar genomes and uh anyway we hadn't solved the long-range continuity problem yet and uh that so that was 86 uh it was another few years before either they or we um started to produce contigs that were long enough at least in our model organisms so that uh the uh you know as a good criterion is is how many centimorgans have the contigs forget about kb but centimorgans because the way we're trying to integrate physical and genetic maps and you know they have to be a few centimorgans uh or else geneticists are going to be frustrated with them and uh getting them to a few centimorgans was hard but uh we uh we got there I think that uh you know one would have to ask Sydney uh kind of what he knew and how much of his vision was his vision I mean he's a visionary and how much he you know he was obviously uh several orders of magnitude better connected than I was to kind of the kind of the network of people thinking about these problems but I think his thing about us needing to publish in 86 was a recognition that uh you know there were going to be big efforts and uh the uh so it was about then and I I actually uh you know in retrospect there was not some moment when I heard about the human genome project it uh you probably know the date I'm not sure the the you know the famous Cold Spring Harbor Symposium was that 86 or seven 87 seven yeah so that would have been the spring of 87 so by then the proposal I didn't go to that meeting I went you know I was not well known I was reasonably well known within the yeast community but I was not well known outside of it and uh the uh Selston was much better known uh although not really in human genetics for example he was I mean he was already you know he John Selston won the Nobel Prize for the for his work on the nematode cell lineage which had which had preceded all of this uh this uh physical mapping and uh and that work was you know was really seminal and he was very well known amongst molecular biologists so he had a high stature of in in biology that I I didn't have I was an idiosyncratic yeast geneticist geneticist would sort of the way that I would have been I think described then the way I thought of myself uh Selston wasn't very well connected and I and I but I you know I started to hear uh you know um second hand third hand reports department of energy some Los Alamos technology that was going to really knock out this problem on human genome scale and the uh and by the time by the time of that Cold Spring Harbor Symposium I you know I I didn't go to the uh Selston went to the uh Sinchimer meeting at Santa Cruz I I just wasn't well known at that time um wasn't wasn't on the kind of invitation list for those kinds of events there weren't very many of those events but anyway he uh so I I I sort of knew knew that that a plan was being pulled together I I remember being at Cold Spring Harbor uh at a Banbury meeting and uh I'm virtually sure this was before the symposium and I can't remember uh the date or the topic I've been to many Banbury meetings but the but I remember sitting next to Jim Watson at dinner at the Banbury Center and asked him what what he knew about this human genome project idea and so that shows the level of my naivete he didn't know very much actually but uh the main thing he said was he would support it if they got somebody good to run it this was a long time before he was a candidate for this uh job but but his point was I learned I got to know Jim well after after that I met him a couple of times before then but we got to know him well during the kind of genome project and still talk to him pretty regularly but uh there's always a point you know that's a you know when Jim says something there's always a point now often people don't like it and uh you know he says things that he shouldn't say and so forth this is who he is but let's just stick to science policy issues there's always a point and he usually makes it very indirectly and the uh I told an anecdote this is a total digression but I told an anecdote about this which you can find in a book review that I wrote in bio essays reviewing the book of tributes to Jim Watson something something science it's the title of the book easy to find it was a Cold Spring Harbor publication of just essays of people who had worked with Jim and no Jim I wrote a review of it I didn't write any chapters for it wine bergen has wine wine wine garden Jim wine garden has a actually a somewhat important essay in there as far I know the only place there probably are NIH archives but it's the only place that I know publicly where he he just sort of wrote down his thought process in in sort of basically grabbing the human genome project for the NIH over a lot of intramural opposition but in any event it's an interesting collection of essays in this book review I tell an anecdote about where the three billion dollar budget for the human genome project came from and it's a it's a Jim Watson story and it illustrates this point that he makes his points very indirectly so his point about if they got somebody good to run it is that he he doesn't think anything good ever comes of a bureaucracy and his worry about the human genome project is that there was going to be a big bureaucracy build up and it would be an embarrassment to the whole field because it would flounder around that and and Jim actually often starts with these essentially political points and only later did I really extract from him his thinking about why it would be scientifically useful I think he took it for granted that it would be scientifically useful if it weren't a boondoggle and so what he was thinking about that night was how to keep this thing from being a boondoggle but anyway I you know I saw I heard these things and my I got swept up in the kind of that policy world you know because the I had nothing to do with the launching of the what turned into the alberts committee the nrc committee on mapping and sequencing I don't know the story Bob Cook Diggins book probably the best published source about kind of the kind of how that happened but you know the one thing the nrc does well is you know they have a they have a good staff and they research things and you know scientists they but but the staff I think they get the staff kind of outside volunteer balance well they do that well at the nrc the staff is very active and comp and complements this is at their best complements the the weakness of people like me that you know we don't systematically look at fields we know people we hear things we read papers but anyway they studied the landscape and and you know just I think they discovered my project in st. Louis is just one of a small number of activities that were really directly relevant to this proposal and so I was invited at one of their very first meetings uh I'm not sure if they had met previously to this meeting but it was the first meeting where they were taking kind of outside input to talk about my project in st. Louis and so I came and I talked about it we had just really gotten we were really just getting yeah I had the yeast stuff it was at a you know contigue closure phase and but we were we were the yaks were working by the time I talked there it was pretty clear they they were working hadn't done much with them but they were working and the so Bruce in particular was really encouraged me to stay in touch and and asked me to write him a letter which I did I I don't have a copy of this letter but I it must exist in the archive somewhere but I I wrote him a letter it was the first time I can I remember really writing down what I thought about this whole proposal and what the issues were going to be and I remember the main the main it was not a long letter it wasn't supposed to be a big white paper it was a letter and I remember the main advice I gave was not to listen to people who are saying that it was going to be easy and that there were two reasons that they shouldn't listen to them there were quite a few including Wally Gilbert who was on the committee we thought this is not going to be very hard I said you don't want to say this for two reasons first of all it'd be wrong and sets you up for failure because it is it's going to be hard and if you say it's easy going in and it turns out to be hard then that's not good and the other reason is this simply politically look you're you've got to argue for a special effort that was the key phrase in the NRC report is we need a special effort it's not going to happen by itself it's needs you know this is going to need careful attention it's going to be need institutional nurturing it's going to need a bureaucracy I God forbid and you know if you make it sound too easy then you're undermining the case that that any real special efforts needed the scientific community does well enough if things aren't so hard making ad hoc coalitions and scraping together enough funding at least to really get the thing seriously off the ground and so I want to make this and and the and the good thing about this argument is is actually true it's going to be hard what's difficult what difficult is explaining why it's going to be hard and we've already sort of covered some of that territory but anyway Bruce liked my letter I don't know whether he shared it with the committee or not and the you know the next stage of the story is as well known is that so while he got so impatient with the deliberations that he did a kind of this story actually is not so well documented but it's known but it's not very well documented he decided on a sort of what was a kind of a pre-celera sort of move you know we're going to get a little company together raise a little capital and go and do this and took some steps in that direction and and so he he resigned from the committee because of obvious conflict of interest and and the I don't know whether it was Bruce who decided or you know whoever but I got a phone call from John Burris who was the executive secretary asking me if I would take Wally's place on the committee and so I had real apprehension about doing that I mean you know there's a Sydney Brenner and Lee Hood and you know Frank Ruddle and Jim Watson and you know I'm Bruce Alberts and and so forth I'm leaving you know leaving out many famous people there was nobody on this committee that was you know a backbencher of my standing the only really the closest peer I had on the committee was Shirley Tillman and she but she was much better known than I was I don't know she was a leader kind of lineage and had already done well known work in molecular biology on functional aspects of global regulation and so forth anyway she was much better known but about the same career stage I was at and but Shirley and I worked well together we were the only people on the committee that had ever sequenced any DNA to speak of I've told the joke a number of times that it which is I think is accurate you'd probably get different versions of it from Shirley and Dave Botstein and me but and I don't know which one would be right but what I remember is that a coffee break one time Shirley and I were conferring about the gap between the reality of DNA sequencing in 1987 and billions of base pairs and we compared notes as to how much sequencing the two of us had done and she'd done more than I had because she worked on bigger genes but you know it was way up in the many thousands of base pairs and so we we wondered how many people on the committee had had sequenced with their own hands at least 1000 base pairs of DNA and so the only taker was Dave Botstein claimed to have sequenced a thousand base pairs with his own hands nobody else even claimed to have you keep in mind you know we have Lee Hood and on the committee and I while he wasn't there anymore but I'm sure he wouldn't have claimed to have sequenced a thousand base pairs that you know we had kind of they didn't do this stuff and Shirley and I had done it and not on any large scale but we had actually done it and so we initially disallowed Botstein's claim because it was based on having sequenced the Euro three gene in yeast he has a paper on that but the gene's only 1100 base pairs and there were two other authors on the paper and so we just thought it was unlikely that Dave had actually sequenced the thousand of the base pairs himself this was undoubtedly unfair and it was all this was all for fun so Shirley and I and to a more limited extent Dave Botstein you know had a little experience actually in the lab doing these things and we did tend to be the three of us tended to be allies on the committee on this side of caution you know we were caution in the sense no don't make this sound too easy it's not going to be easy the scale-up factors that we're talking about here are too big we you know the report rather famously and I think this was my suggestion adopted you know the de facto rule of Fred Sanger I don't know whether he ever wrote this rule down but it was commonly discussed amongst people who followed technical side of sequencing you know that you you wanted you know you wanted to move from project to project with a scale-up factor that was big enough that you couldn't just do it the way you'd done the previous one but that wouldn't break your system completely and he settled on an approximate factor of three and you know you look you went from phi x to human mitochondrial DNA to lambda DNA to eb virus you know the four successive factors of three and and if you look every one of those was done by really substantial innovation but not so much innovation that you really had to start from scratch well if you take you know take a baseline of a few thousand base pairs which was the state of the art 1987 and get the billions of base pairs it's a lot of factors of three and but you know we just hammered at the point that if if you're going to line up with anybody's view of how to improve this technology Sanger is probably the one to line up with you know we should claim that we know how to scale up DNA sequencing better than Fred Sanger did and and so that did kind of prevail if you read the report that sort of attitude and and actually if you look at what happened you know it it's not a bad approximation you know at at some stage you know the the technology that generation of technology got generic enough that that it could be parallelized the factor of three is is is when there's no protocol to copy you've got to work out the protocol and the there was a lot of working out of protocols it's more than protocols you know it's it's more a little more their strategy the strategy was pretty set it's more say tactics and there was still quite a lot of tactical maneuver going on until until really the end of the 90s when there was a kind of a massive convergence on a particular tactic and very little difference between the practice here and practice there and it became an issue of making best practice within a within a pretty tightly confined strategy make make sure best practice spreads quickly and and I think the that was one thing that NHR I think did well actually the it's easy to complain about the g5 system I was not a part of it and there still are hard feelings about many things but I think it was an effective method of having best practice spread rapidly and that that was that was probably the highest priority that needed attention at that time well framing it as a lender in Boston questions questions sort of invites a slightly humorous response is that one of botstein's major contributions is that he kind of created Eric Lander but you know so that was a major contribution the I I don't have a lot to say about Lander actually I'm happy to talk about botstein you know he and I were allies for a very long time the there's an anecdote about botstein and me it's a true this is a true anecdote I remember this pretty clearly at a you know he was a yeast guy and and a big big fan of the work even that I did in Ben Hall's lab which he followed closely I remember him sitting in the front row of a Gordon conference where I presented kind of a key aspect of this east tRNA study the more genomic aspect of it and he was just sort of beside himself with enthusiasm whereas lots of other people you know they thought this was kind of clever but you know waiting for the functional punch line and botstein took it for what it was as opposed to looking for me learning something new about tRNA genes but in any event I was like to tell the anecdote about when David and I first met the was at a yeast meeting at Cold Spring Harbor which was 1977 and I had just sequenced with Howard Goodman in and Ben Max and Gilbert sequenced the first of these mutant yeast tRNA genes and I was my first sequencing and Howard Goodman's lab had Maxim and Gilbert sequencing kind of up and running and I went down to San Francisco it is for a few days and learned how to do it and did some of it there and we got pretty good results and found our mutant this is the first sequenced eukaryotic mutation that it and I anyway so I presented this at Cold Spring Harbor at a session that botstein chaired and he had his usual enthusiasm for all of this but I'd never really interacted with him and so that night at the bar I drank a little too much not unknown at Cold Spring Harbor and somehow or another got into a argument with Ron Davis and Dave botstein this is the three of us and it was about DNA sequencing and really the sort of philosophy of science element of DNA sequencing in which I still remember the topic is that they took the position that DNA sequences in their nature had to be determined exactly and I took the position that you know experimental science is fallible it's inexact you do the best you can and you put airbars on things so this of course was an interesting discussion because it propagates through you know another 20 years of discussion and my position could be interpreted as am I not caring about quality but anybody who knows me knows that's not right I like to think that I won this argument long term because of course I was right philosophically and eventually basically Phil Green showed how to put airbars that you could really work with on these sequences and that that was the critical step in doing it at scale and certainly arguably the most critical step in doing it at scale so about at the night anyway there we were you know I'd had too much to drink they'd probably had too much too but I'm sure I'd had more than they had otherwise I wouldn't have gotten into this argument so here you know I'm a postdoc there's nobody and you know these guys were they were the kind of the rising stars and these genetics and molecular biology and so after a while this argument drew a crowd you know this was like a in a movie you know there's going to be a bar room fight or something it was a circle people that I said you know there's a postdoc out there arguing with Davis and Dave Botstein and so we I don't know we argued for an hour or two you know we're all big talkers especially Botstein and me and so I can't remember how it went out I you know I was drunk and so I what I remember is so I wake up the next morning hung over and I say oh my god what did I do I have to find some other career I made a complete ass out of myself in front of the two most important people to impress in the field that I'm trying to make a living in and this was well before I was out even looking for a job and so I decided I was gonna that it'd be bad form just not to show up at the meeting at the morning I'd already given my talk but I was going to just you know sort of sneak in the back and a little after the session started and so I'd be sure I didn't run into one of these guys and or anybody else who had seen this event so this was back in the Blackford Hall days before the grace auditorium was built and so I kind of sneak in the back back there and just kind of see what's going on and so suddenly somebody slashed me on the shoulder from behind and an inimitable booming voice David Botstein says hi Maynard that was fun last night so anyway Botstein and I have been great friends ever since and and we were allies in the you know on the alberts committee and then through a lot of these advisory committees we tended to agree we didn't didn't you know we didn't coordinate our arguments ahead of time but we tended to agree and and then we would team up and we were pretty good at improvising a kind of a back-and-forth approach where I'd fill in the weaker parts of his argument and he'd fill in the weaker parts of mine but and Botstein is very hard to argue about often I knew more about the technical details and so if but I could I could shore up his arguments said and he was powerful and formulating you know of a conceptual level policy kind of idea so a lot of it you know I think we've already covered what a lot of it concerned is that I remember the discussion on the NRC committee for example about some some people on the committee wanted to put you know year 2000 as the goal have the sequence done by the year 2000 and and I remember without again without prior coordination David and I were the two people who just thought that was not going to work now it was very difficult to sit there in 1987 and you know decide that 15 years might be enough but that 13 years was cutting it and you know but we were about right and when we just sort of both of us and coming at it from somewhat different directions sort of looked at what was going to have to happen and then happen after that it was hard to picture this thing getting done by the year 2000 but you know we thought you know certainly 2010 2010 20 this was probably very safe but the so that's an example and issues of that ilk you know recurred over and over and over again and the other thing that we both you know agreed on that was a powerful position was you know we both were yeast guys and and we both thought model organisms were actually the key to this thing that not just add on but the key that the we both took a fairly dim view of the human genetics community as a scientific community and there are still people that resent somewhat my attitude so he had some exposure to human genetics that I just didn't have the genetics department in Seattle where I made this rather abrupt transition into genetics that had no no significant human genetics Dan Gartler was there but he was not working with families for example was primarily at that stage in his career he wasn't it was a very positive influence on me but I didn't learn any human genetics from him he was primarily interested in the mechanism of exion activation at that stage in his career and the I just didn't have any exposure to human genetics I you know in more recent times have really become close with Arno Matulski but I didn't know him at that time I went to one lecture he gave the human genetics division was had had essentially no interaction with this basic model organism genetics department so that was where I came from David came from this human genetics department at Michigan and had had had some serious exposure to human genetics and knew a lot more about it than I did and of course played you know a historic role in recognizing that that of the various things that kind of we had to offer from the early stages of genomics that RFLPs were the thing that human genetics needed the most and wrote that kind of that 1980 I think paper I I didn't know enough about human genetics even think about that that about about you know why RFLPs were particularly the thing that human geneticists needed I I sort of saw human genetics as needing a sort of a massive collection of tools which was essentially accurate but but Botstein could see that this this this tool was ready to have a big impact in human genetics I didn't have that level of understanding the that they didn't have enough genetic markers basically to do much of anything I could just see that they couldn't do much of anything now they learned a lot of interesting things I don't want to be misquoted on this they I would I liked reading about you know human genetic diseases and and particularly when there had been some biochemical success like the inborn errors of metabolism I was quite impressed a circle cell anemia these were you know these were very interesting scientific stories and in some cases it already had some medical benefit but it all looked so peripheral to me to the core question of you know just why are people all so different you know this now population geneticists I had a little Joe Felzenstein was there I had a little exposure to population genetics not much Ed Moore and St. Louis when particularly when Dan Hartle came to that department but I wasn't so interested then I've gotten more interested since but I wasn't so interested then in a population geneticist's answer to these questions I wanted molecular answers I mean I there are a lot of heritable differences between any pair of humans and I wanted to know what molecules were different and and I could see that the tools just weren't there and so when I tended to look at human genetics and tell rather recent times this has changed and is changing and and young human geneticists today are a totally different group than my comments apply to but I'd say well into the 90s I just saw human genetics as as an overly self-satisfied kind of enclave that defined a certain set of problems which they could make a certain amount of progress on mostly importing technology from outside almost entirely importing technology from outside and and then that's okay the what I didn't like is that they were always too satisfied with the technology I spent you know I can't tell you how long through the the late 80s and 90s arguing with human geneticists the Hughes was the worst I was a Hughes investigator for a while a total misfit with this organization and they couldn't take my Hughes investigatorship from St. Louis back to Seattle in 92 so I left the Hughes then but for a few years before that I was a Hughes investigator and I used to go all their meetings and they had a lot of human geneticists and the yeah I had argued with these guys and they you know they they would differ about whether a 10 centimorgan you know a DNA polymorphism map was good enough or whether there might be some benefits to taking it to five centimorgans that you know they there just was no vision there about you know how are we going to ever do this that yeah how are we actually ever going to really understand you know what are the molecules that you know make people different from one another and they they had at any given stage a certain set of kind of method they were all doing the same thing some of them did it better than others but it was always the same thing and anyway I was frustrated by this community I just didn't see any vision there they were not very enthusiastic about the human genome project the human genome project was a so anyway Botstein and I agreed that you know model organisms were the key you know they it's sometimes presented as though you know we made the case that it was an important add-on it's actually the key that's how you know that's how we're going to figure out how to do these things and not just it goes beyond the methods the the conceptual framework what what is it that we're actually trying to do and what would the benefits of that be these questions needed attention and they needed refinement and they needed you know some sort of smart knowledgeable people arguing about them wasn't going on in human genetics and what's going on in yeast and worms and eventually you know the flies you know the flies were slow to the table they were slow to the table for actually a very simple reason first of all two you know a couple of reasons they had some of the insularity of human genetics you're either a fly guy or you weren't a fly guy and they and they had polyteen chromosomes and so they had you know they had cytogenetics at a resolution that other people could only dream of that's not going to solve the problems I mentioned it's not going to tell you why one fly is different than another fly out around a garbage heap somewhere but they could do a lot with them and so they stuck with chromosome walking way too long because they could walk better than other people because they could they could see where they were on this cytogenetic map and so forth but they you know they came around but they weren't they they didn't lead at all they dragged their feet you know it was it was really worms and yeast that led and they and then you know model organisms like Arabidopsis for example I mean they underslowed you know this can transform our business and so forth and it and it as I say it was more than methods it was a sort of a conceptual framework you know what is what needs to be done what's the relative importance of all these different parts how do they fit together and also just getting experience the factor of three kind of thing from Sanger but so that was another thing that David and I agreed agreed about but you know I think you know David's contribution he his contributions as an investigator were modest he was primarily an intellectual force every field needs more intellectual forces I've never been around a field that has enough intellectual you know has too much intellectual force excessive extra intellectual force and around a lot of smart people but intellectual forces a different matter and the and and I think it was that the things I've already mentioned and and well they're all things I've already mentioned but it was this combination of you know significant experience with the techniques much more than many of the other big talkers in the strong rooting in model organisms and and this awareness of of really what new new understanding human genetics at a sort of conceptual level was a powerful combination and his with his personality the the lander that I have to be another day or somebody else the yeah it's a different I think that's a whole whole different whole different discussion yeah so obviously you know that's one of the one of the primary pleasures of a career such as mine as you know occasionally to you know interact with young investigators that at critical stages of their career and sort of feels oh yeah did something good you know actually my biggest regrets and my whole scientific career they're not you know various scientific errors that I made is that I other other students I feel I let down you know I didn't quite figure out you know I'm sure they're basketball coaches that feel this way you know they just never quite figure out what to do you know this guy had a lot of talent but never quite figured out how to plug him in so he could shine but you should always focus on your successes Eric's one of my successes and yeah so Eric Eric is better known many of you than to me have seen him over more years and so forth he's a he's immensely better organized guy than I am at every level so it's kind of interesting that we work so well together during this formative period because personalities are actually quite different I haven't been surprised either by Eric's level of success or or even the general trajectory that he's taken because I always saw you know he's a leader I'm not I'm actually not not a leader I'm a I'm a I'm a sort of a old-fashioned professor I'm I'm at my best when I have a lot of time to think for myself I you know I talked about my sort of zero-based approach to things well that's not an efficient process and I just you know I've never you know I never I've been a I've been an acting chair of a few things just out of community service I do have some sense of community service about me but no desire for leadership now I just never never pursued any opportunities in leadership it's not what I want to do uh the uh Eric I you know he's a leader you know I could see that and uh you know he'd be after you know I was an odd choice of somebody for him to postdoc with and I don't you know he's the one who'd have to say why he did that it was pretty venturesome he'd had a very successful his MD PhD had personal reasons to want to stay in st. Louis uh longer after he'd finished his MD PhD that his PhD studies had been extremely successful were very well regarded in uh in the medical school there which was a leader in sort of glycobiology and you know he worked on sulfation of uh of the glycosylated proteins and uh it was uh it was biochemistry it wasn't really even biochemical genetics it was basically biochemistry and uh had done very well at it so so he's had at this sort of functional extreme of this spectrum that I've been discussing and uh here I uh you know I by then was a well-known figure at wash you I this was I don't know 1997 something like that 1987 I mean um and uh you know wasn't you know so well known nationally but I was well well known at wash you because uh more and more things were being built on this kind of this little startup project of mine I mean there were you know there was no genomics or anything that resembled genomics when I went there in 79 and when I left in 92 this is a huge center of genomic activity and grew more after that but if you if you look at that whole history you'll see that there was never any leadership of mine I guess I was never in charge of anything and uh frustrated the you know the the high command to no end because you know they wanted me to you know bring in tons of money and organize some big thing but I just that's not what I wanted to do uh but uh fortunately there were other people that did want to do it and were highly capable of it but anyway somehow into that you know why Eric decided to do this I don't really know it was quite a major shift for him from uh just the kind of experimental work he had been doing uh I wasn't you know he had an MD and uh planned to you know to to get uh I don't know if he was certified at that time or board qualified at that time as a laboratory medicine person but that was certainly where he was headed and I wasn't developing genetic tests there were people at wash you that were developing genetic tests there were all sorts of things going on that were much more relevant to what he was doing but so he decided to do that uh I you know Eric's not hard to read uh some students are hard to read but he's not hard to read you know he's energetic and uh you know hard working extremely competent uh hard to stop and uh we um you know I wanted to get a project going that would sort of do for yaks what we painstakingly worked out with lambda and cosmic clones in yeast and I could see that the parallel methods just weren't going to work they uh wasn't going to work and I was right about that no one's ever made them work and uh that that is we weren't going to be finger printing yaks by digesting them with restriction enzymes and doing n and complexity picking of clones and n squared computing and so far this wasn't going to work there are a bunch of reasons for that but it wasn't going to work uh so PCR was sort of the new kid on the block technically at that time it was brand new and uh so that it really appealed to Eric because that was it's relevance to lab medicine was obvious and uh and he uh so he you know he got that up and running and more than that it wasn't so hard to get up and running but got everybody doing it and you know he made the PCR transition in uh in in our lab and indeed in the whole department there uh and uh we uh yeah so we developed this idea that uh that uh you know we could combine the screening of the yak libraries which we shifted to a almost completely PCR based method I never liked hybridization screening of things I'd done a lot of it and I never liked it still don't like it it it resurfaces from time to time but uh it's not uh it's just not the right way of screening libraries in my opinion uh uh they uh so anyway we we we pretty quickly realized that uh you could build quite nice maps if you if you did this is just back to what I was talking about with with Kim Hay Smith and Ben Slap if you go in and screen a very deep library so we have round Davis's kind of conception always worked with a very deep library so you got a lot of independent clowns screen somehow or another for one point in the genome and uh so you have random in clowns with all of them contain this one point and uh and so PCR was our point that was getting down to a you know pretty close to a point and uh and we you know we quickly realized that we could actually build very good maps just from the just from knowing getting enough PCR assays across the region even if they were randomly spaced we could order the order the uh the PCR assays and the yaks and the same all in the same we could screen and order and so forth all in the same way and so uh that was the idea I actually you know it clearly that's a a compound idea and uh it wasn't some afternoon on the blackboard that we put all this together but uh we were trying to get going with yaks what we had going in yeast with uh these simpler clones and uh you know so he he got that up and running very quickly and uh and because he was more interested than I was in uh and collaborating with human geneticists uh he was very effective at you know not just using any old PCR assay but that was a period in human genetics when you know laboratories you know they had armed guards guarding their PCR assays and they wouldn't publish the sequences of the primers and uh they uh because it was the closest to you know some positional cloning project or another but he was very effective at at building collaborations I just left that to him completely I didn't have any interactions with these groups uh personally except just you know sometimes they'd ask me if I was really on board I always said uh it's okay with Eric it's okay with me but so for example that's how he built his ties to Francis Collins is that the CFTR search was right in its end game and the so uh the timing actually was sort of perfect because the Francis and his collaborators sort of found the gene by their methods and the process they developed huge numbers of PCR assays as they were looking for it and uh we were no longer a threat to them and uh and so uh they and Francis saw the you know the the the general interest of doing that kind of mapping and uh so he and Eric uh collaborated on getting all the you know they were the ones that you know got all these assays together and we did very deep screening across that whole region and kind of showed we could build these maps and the uh but it was a fairly brief period really by about I I I I I can't reconstruct the timeline but I doubt that Eric was in any meaningful sense of postdoc of mine for more than two years uh because this guy you know he was on a fast upward trajectory so I remember one day so I told him uh like I have too much space uh with all this space I'm uh you know my lab's getting too big I don't that's not big labs are not for me uh I uh I want you to take what was my main lab space including my office and uh I'm going to move down the hall to uh smaller space and uh smaller office and uh and start over uh with some new projects and you should run with this and uh so the lab medicine or somebody appointed him uh you know when when you're a fast rising star in a and have in a in a sort of the more clinical side of an academic medical school jobs are not never a problem and so and he got some kind of a job there and uh he did at first you know he didn't want to do this and so forth but uh I persuaded him and that would be good for us both and uh so he just went around with all that I wasn't involved in the uh you know I'm not an author on the chromosome seven mapping paper because I have anything to do with it uh he had a nice big contig out there on chromosome seven and he just went to work and mapping the rest of the chromosome and he worked with David Schlesinger and that's another complicated story but but the the ex chromosome was David Schlesinger's kind of baby and and chromosome seven was Eric's and and uh pretty much finished that in st. Louis I think um but then uh Francis hired him here and he went on and did things you know I think the the last thing I'll just stick in there are a lot a lot more could be said about Eric but uh the uh uh as far as his scientific contributions go uh obviously he was a a key player in that kind of formative stage of genomics uh but of the work that you know he really did as an independent investigator here I I do think that his comparative genomics is underappreciated uh you know comparative genomics has become you know so ubiquitous and uh and we're now accustomed to comparing whole genomes and you know so you know field is at a much more advanced stage but if you look at that you know he took his his intramural center in that direction of uh of doing you know multiple species well chosen multiple species uh fairly long tracks of DNA you know across multiple genes uh complete sequence and uh I think you know he showed uh people that this is uh this is the way to go we need uh we need multiple sequence alignment over long regions with well chosen phylogenetic uh comparisons uh produces an immense amount of information and uh and I uh I think that was uh a uh as an underappreciated contribution to uh it's the development of genomics as we now know it