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group of genes that have the S same
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opponents. Um well actually have an
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imitation of the they're they're not
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actually speech said we cannot see any
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difference cross this and all this
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variations that the may have a very
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important functional impact if people
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in the fermentation bitterness they
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know a for you call I'll for any of
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those species just like one base pair
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mutation work have different functional
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impact. So what be very important to
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develop the college that you can
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sequences species travel and actually
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basically get DNA from sort of along
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not so long a fragment of DNA in
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sequence yet at a sample point. So this
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is what we've done so we've quite a
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company call completing a mix of
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practise two years ago. And was
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starting to develop the technological
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therefore to up to a level where you
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can sequence well every matter sample
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Eurospeech species that so the idea is
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basically you get a hundred to three
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hundred KBDNA match extra bar coded
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icicles that bar coded in a so you
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actually get a three hundred K be non
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fragments of sequestered in a venue
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assemble from there people in this
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world with sort of sick was you would
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know if you got through and make a B
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assembled uni from bacteria you would
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be able to assemble to to achieve that
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what was species that so by that you
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probably could recover basically a
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matter to you know really are not
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imaging level but the matter species in
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the a sequences. And this whole type I
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don't really have the data to show yet.
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Um because we actually got a
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preliminary results just a few weeks
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ago. And hopefully next year we will
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start to advance the real results are
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in our instruments you meetings are
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that week as started to sequence the
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species level from them at a a samples.
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So this is just a retail you that what
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PGI is this I mean for collecting data.
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And developing technologies to generate
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more accurately cheaper outdated. Um
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how about the other species I'm gonna
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talk about human right but we're not
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just to human mom matter samples we've
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done a lot of other animals like pagan
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mouse the which people we use a lot oh
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I'm a dog reasons which is quite an
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important of course would be important
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to ask I mean do they actually you may
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share the same inner sort of a couple
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tubers which you probably needing to
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different conclusions we used MS animal
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models. So without the mouse the using
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the same way of doing shotgun
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sequencing sample and so a first of all
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this is the comparison between mostly
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human so you can see the mouse
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catalogue your greater difference to
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the human kind of energy level then the
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functional now in not away. So that
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share a similar function but not the
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same genes which in some way it's good
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if you study the function there on the
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remote I I don't I that's as you can
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see there you are but realised what
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make use ratios use difference there.
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But the actually come going away so
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they're not really that much so the
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difference. And if you add in pay there
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what you want be more complicated first
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of all the here jean between those
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three species of quite small. So they
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can you know gene that they don't cheer
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but in the functional that was you know
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a lot it tells you again if you want to
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use that as use the mouse model opaque
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motto as active function allowances you
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may be fine but not look you mean to be
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G levels yet. And if you do this P box
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possibly analysis with all these
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different colours here. Um you see lots
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and yellow here. But the yellow
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represent pay a some specific. So that
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tells you actually the we got specific
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past we whatever cables copper more
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functional module so humane Miles
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probably imply the pickup environment a
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kind of different in human. And mouse
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maybe more function to to supplement
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tend to physiology anyway so this is
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the comparison between human pecan
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models. But that's them bodily some
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state right are we even sequence more
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than just the model exams for example
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kinetics I mean it looks nice wee
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sequence that we've got operation. BMC
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yet you know things honest started
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sequence side you know with norwegian
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group which you actually siamese a big
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farming business. So can we sort of
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understand why you know the Simon B is
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so different even you feed them the
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same thing I still all this kind of
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project a out of curiosity we are even
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sequencing the the fickle sample from
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wherever which basically you can
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probably walks through the use and talk
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somewhere else and get the sample be a
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lot and you can get chance of because
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samples there. Um but that's a quite
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interesting expect edition so we have
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some I can't Norwegians and this and
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we've sort of hunting both well so it's
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it's quite experienced. Um but again
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this is just for fun I mean that seems
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really a serious or whatever I can not
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say this is not zero size is it is a
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serious eyes but this is just part of
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our program to collecting all these
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fickle samples and and sequence that
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interacting and to enrich our gene Cox
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anyway so we have three more species
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any of those speech I mean panned out
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for example idea this the pictures you
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so it's sequence to got to use a pen a
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polar bear and just right I mean any
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species you can think of we can send a
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sample we will do a but this is just
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the one thing right to construct a
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reference in a sort of catalogue
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somebody has to do it and we are happy
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to be that kind of digital museums to
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store all those data and sequence. Well
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then of course the second parse would
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be can we start it attracted managing
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angst all mixed data from different
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time points right so this is what we've
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done together with Frederick at for
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swedish a whole again four hundred us
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one hundred samples but then for time
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three point at some three time points
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with the other sample. RS project
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already shows you some of the data five
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just try to sort of show you some of
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those jeans and you whatever matter how
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to use we've got right so hard they
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assemble whatever and you know the
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matter species whatever is kind of
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normal so it's not a sure enough people
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but they also sure very very low
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abundance like so they actually
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sometimes goes away after one but
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anyway so again that tells you it's
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those things up quite specifically
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quite kind of personalised those are
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the signatures of the got echo system
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in different states from you bound to
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be a mother as you can see the the
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shifting here yeah is because of the
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diets in the physiology I don't really
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go into the details of this carrots but
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I'm happy to show you you are
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interested. Um so the but then noticing
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will be interesting to collecting from
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different the geographic location one
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thing with down for example is a
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comparison between chinese and things
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which you we've got a lot of managing
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onyx data first of all you can see that
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different right so the the president
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human got but what we could use really
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unique at least on yeah you buy. PC
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analyses you can realistically always
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each other there. And that's even there
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is also similar to you upon is you
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preeminent or as you can see here is
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really compatible. But if you really
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look carefully they have lots of
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different for example that then string
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colossal wine which yeah figured out
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from their got materials. And drink
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lots of milk to I mean we I don't
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really I'll have lots of new but we
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have actually very very so it'll be the
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big expense relative expense you know
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prior you up but tier. And more
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importantly we are consuming more
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anybody got whatever treatment. So you
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see from our couple tours which we
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chinese individuals have more
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antibiotic resistant genes are we
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probably we used and but anyway this is
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not really good a few again is all
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meetings so that they would be taxed
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difference there. Um as you can see the
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the energy and the time reasons a
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couple had remote I'm reasons you know
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it's quite different there remembering
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transporters you know the anabolic by
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decorations is quite different the two
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the show you more specifics. Um chinese
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like to eat all these sorted for small
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whatever stuff there which may contain
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some of those solid years which is not
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really good sorry here so then you
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already see actually which from chinese
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calls the which probably also expensive
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for ten so Europeans Devine china maybe
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it's not very good for them if they
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don't really have a box to add your
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with by the way so you know so because
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of the price difference so we need
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actually a lot of MS it's sort of
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synthesising and be the missus facing
00:16:30
materials say because from the times we
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don't really you have enough. And if
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you look at the pin type that there is
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actually have produce sort of more
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casting away so they find more which I
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hardly noticed but I actually are
00:16:48
noticed afterwards after I think it's a
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but the chinese tires which yeah with
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relatively sufficient protein assembly
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that means a license may actually have
00:16:58
more but your is this sort of thing the
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size been I mean B complex essentially
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masses and so on as you all could see
00:17:06
it from this summarised the rockets my
00:17:09
it's just a kind of a to show that
00:17:12
there is a big difference because of
00:17:14
that your graphic locations of people
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of colour saying probably genetics all
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is probably that's why or environment
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whatever. So I actually initiate
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another project to see for the chinese
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beseech chinese we should genetic is
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chinese the probably boring you know
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them more you're your or united states
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try to see what stick sort of
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difference of what tears and is you
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don't have to Medicare sort of
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difference over there to see the
00:17:43
difference there but we don't have the
00:17:44
yeah but again this is a way of sort of
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connecting different geographical
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locations ages sequence MO but more
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importantly you want to collecting all
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this angst data from different disease.
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So you can see from all it's nice to
00:18:02
almost every disease are sort of
00:18:04
metabolic right and Dave anything
00:18:06
metabolic has that not wanna get "'em"
00:18:08
is you need to sequence the camp accuse
00:18:11
also we started from the type that
00:18:13
media switch your published a few years
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ago I think that's one of the first to
00:18:18
study is using the kind of do you lost
00:18:20
type of analysis but on the puppeteer
00:18:23
start not like the human study which we
00:18:26
need tens of some samples to get a
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minimal sort of P value or whatever
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significant signals there by just doing
00:18:35
them editing a mix analysis you only
00:18:36
need to do hundreds of samples that
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just shows you the diapers either all
00:18:41
be signal the statistical power there
00:18:42
is quite strong only need hundreds
00:18:44
examples already give you some results
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for example we gives you very very good
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real rock or whatever to tell you the
00:18:53
you can find out the perfect by
00:18:55
marketers just by sequence the the
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disease and control samples we did a
00:19:02
wide use all this fifty up by markers
00:19:04
as just show you two can clearly
00:19:06
separate have leds and controls there
00:19:09
there we go either monitoring I mean
00:19:11
this is the some of those patients the
00:19:13
gastric bypass treatment you can if you
00:19:16
just look at least fifty apart markers
00:19:18
there you can see the create she from a
00:19:21
kind of bad materials to a community to
00:19:24
have that are back to committee. So
00:19:28
that's just show you the those kind of
00:19:30
markers again could be potentially
00:19:32
interesting to be used to be mechanical
00:19:34
level for the that classes to to see a
00:19:37
how good treatment is still useful
00:19:40
really for the talk from just go
00:19:43
talking about all these obesity in
00:19:45
America a consortiums there I'm not
00:19:47
going to talk more there but we've done
00:19:51
another study on the kind of us got
00:19:52
disorders that this is the current
00:19:54
understanding of a kind of possible is
00:19:57
whatever physiology about you know kind
00:20:00
of ask the disorders but there is
00:20:02
people knows it's important on this
00:20:04
later but not much information. So we
00:20:07
were trying to think out can we feeding
00:20:09
the cats there can we sort of try to do
00:20:12
something thereby just multi a mix
00:20:14
analysis. So we started general also
00:20:17
data not just got the copious but also
00:20:20
the host them at that point a mix. So
00:20:22
we tested this zero butterflies there
00:20:25
we tested the euro metabolised. And by
00:20:27
sequence also a lot of cup of tears and
00:20:29
start to make links right situation. So
00:20:32
this is the correlation between the got
00:20:34
cold using the host a metabolised so
00:20:36
you started to make you know
00:20:38
associations between different
00:20:40
metabolised in different box has
00:20:42
started to have this kind of tables
00:20:44
they tried to see you know this
00:20:45
specific metabolites probably have a
00:20:48
strong began situation with respect
00:20:50
here is some of those but yours I don't
00:20:52
even have a name or anything like that
00:20:55
but we know it's there. And then we
00:20:58
started to fill in the gaps. So you you
00:21:01
have some sort of smart guess. Um I
00:21:05
know I don't have a smart oh right but
00:21:08
still you you have enough data you
00:21:11
could draw all these lines you know by
00:21:14
christian may possibly for example that
00:21:16
easy stuff that all these red star what
00:21:18
you stuff that you could start feeling.
00:21:21
Um so this kind of thing is this real
00:21:23
you the multi all mixed power right so
00:21:25
more and more sort of individuals have
00:21:28
the multi layers information then just
00:21:31
by statistics you try to make for them
00:21:33
to go this is another example is that
00:21:36
we've done between the chemical peels
00:21:38
the conferences found this study is not
00:21:42
just trying to find out by markers for
00:21:44
it is thing the the the colour cancer
00:21:47
patients and the controls of course we
00:21:49
actually did find some markers. But
00:21:52
more importantly can we find out
00:21:54
interesting markers to a sort of to be
00:21:57
the markers of different stage so we
00:22:00
collected all the sample from you know
00:22:03
state one two three and four and you
00:22:05
can discover for example the don't be
00:22:07
jeans is quite interesting if you to
00:22:10
sort of follow up that become a stage
00:22:13
apart markers. So that actually us
00:22:18
shows you again the progression of of
00:22:21
the cup material changes day and to the
00:22:23
potential by Marcus you can discover
00:22:25
from different states I'm not a scene
00:22:29
with down I mean this is again from
00:22:31
different point of study so from this
00:22:35
from each other strategies which try to
00:22:37
collect lot just a fickle sample but
00:22:39
also the dental sample for seventeen
00:22:42
which is just so shy to see if they
00:22:44
have the same a situation the bucks we
00:22:46
stick you know the disease and controls
00:22:48
in to see if they shared a lot. Um but
00:22:51
first of all again the as usual so we
00:22:53
we've discovered markers that the
00:22:55
related with the argument limited are
00:22:59
we starting patients there and actually
00:23:01
you discover gram actually got you know
00:23:04
probably be protective against the
00:23:06
disease which is good. But then you you
00:23:09
do all this study what this is another
00:23:12
one based on the cup active analysis
00:23:15
but then have a very specific a
00:23:18
phenotype characters here trying to see
00:23:21
if you can bring again the materials
00:23:23
reason this specifically not that makes
00:23:25
you RP level whatever IDT level there.
00:23:28
So it just this is just another way of
00:23:31
looking at the data this is that that
00:23:33
elevated right so you got a dental data
00:23:36
comparison with this other heavily data
00:23:38
and we got paid. Um a become a very
00:23:41
interesting for example some of those
00:23:44
materials are really representative in
00:23:47
this in the all these samples then
00:23:49
actually this study shows sometimes the
00:23:52
dental sort of markers is even stronger
00:23:55
associated. So you know what be weekly
00:23:58
because doctors told me that sometimes
00:24:01
the the puppeteer is could actually a
00:24:03
big into the bloodstream just from the
00:24:06
models I don't really need to go
00:24:08
through the got in stocks but anyway.
00:24:11
So that tells you that that will this
00:24:13
but to is is also very very important.
00:24:16
Uh for the rendered of structures are
00:24:20
not just to to develop a by matter from
00:24:23
but then so but ear is to to say this
00:24:26
is important but markers for a patient
00:24:28
step and also they're really useful I
00:24:31
mean we did we sort of find out to
00:24:33
compare with that because sample
00:24:34
instead of a sample but the total
00:24:36
samples are kind of more useful for the
00:24:40
evaluation of the university which is
00:24:42
the traditional. T so the correlating
00:24:45
the best to see you know if you have
00:24:47
certain materials actually these
00:24:48
treatment would be more efficient or
00:24:50
actually lasting peace you could become
00:24:52
a very interesting study there again so
00:24:56
this is just different part of the a
00:24:59
tier is how how that that these
00:25:01
disease. So we are collecting a lot of
00:25:04
those kind of information and basically
00:25:07
every possible easy samples we could
00:25:10
collect it over the country in china.
00:25:13
And sequence all of them and put it
00:25:15
into our database. And well there's a
00:25:19
lot of things that we've trying to do
00:25:20
again the collecting all mixed data
00:25:22
from the for intervention. So well we
00:25:24
know all the food directly sometimes
00:25:27
directly influence the got Mccall be
00:25:29
able to sometimes they sort of just
00:25:31
affect the the the release date active
00:25:34
the greetings there and there is a a a
00:25:39
very magic at Madison co traditional
00:25:41
chinese Madison which is being sort of
00:25:45
a documented for thousands of years and
00:25:47
all well nobody knows why and we sort
00:25:51
of just try to see you anything we
00:25:54
could do regarding to those tradition
00:25:56
tennis madison's that we made in this
00:25:58
to something that people think he's
00:26:00
probably important for the different a
00:26:03
sort of a physiology of human. And we
00:26:06
start to test tomorrow I mean I know it
00:26:08
sewing chinese I don't know how to
00:26:10
transit that you do initially anyway
00:26:13
but I just show you some of those
00:26:14
examples there like for example this is
00:26:19
the for this is come to the top on the
00:26:21
list is just a lou. Um it evaluates of
00:26:24
very different pictures of the remote
00:26:27
computer from the PC controls. And you
00:26:30
can look at the the but to resume
00:26:33
posting is actually the shortage pretty
00:26:34
aspect to this which is in general so
00:26:38
it's quite an amazing. So I I'm taking
00:26:41
a low everyday and so just to just to
00:26:44
be you the safe side. And another why
00:26:48
is the kind of to which is again a very
00:26:51
interesting results is sure especially
00:26:55
for the you know by assisting creation
00:26:58
my wife does not really like me to to
00:27:02
have gotten or every day but I do that
00:27:07
rely on the actually a outside of a by
00:27:13
the way so we are keep doing all this
00:27:15
kind of seems to not just from the
00:27:17
mouse study but also pick stuff too but
00:27:20
also human study too. So we we could
00:27:23
volunteers most of them. P gentle
00:27:27
always because we have six on employees
00:27:29
so they have that would the cool there.
00:27:32
So we have the interesting sort of
00:27:34
contain which we could a cafeteria
00:27:37
which you could put all these things
00:27:39
there. And some people sign up to say
00:27:41
now I want to do this study. And they
00:27:43
started you don't work I'd probably
00:27:45
every day thing we saw tested before
00:27:47
and after and all these things so just
00:27:50
trying to see how that affects all this
00:27:54
property to an odyssey of course people
00:27:58
have been discussing is cool robotics
00:28:01
you are provided. It's important that
00:28:03
so we've tried isolated all these high
00:28:07
P values stuff from all this disease
00:28:10
control starting okay. So for example
00:28:13
this one this a but tier is RTF one
00:28:16
zero one is one but here is that we got
00:28:19
it is healthy but you receive detect
00:28:21
like really said analysis that so then
00:28:24
we use that one mouse model issues a
00:28:26
very very good potential or problematic
00:28:29
sort of you could be used it in a way
00:28:35
to protect you against the have it
00:28:37
beeps again we're recruiting people in
00:28:40
PGI but then P jacket poised too young
00:28:42
so we recording their parents oh so the
00:28:45
parents you know newly discovered
00:28:46
directly D.'s whatever so I have
00:28:48
several sorry there it's loose that are
00:28:51
up robotic try to to see if they have
00:28:54
any better. And then we started because
00:28:58
we have a national team back at PGI and
00:29:01
we started to use a national team bank
00:29:04
become a nationals to back which is
00:29:07
good actually so we save all these
00:29:10
different kind of stews with different
00:29:11
each sex whatever we don't people that
00:29:14
means three toleration so it's not like
00:29:16
I so forty dollars at oh by the way so
00:29:20
well hopefully we can do more there.
00:29:24
And is still not very class and of
00:29:26
course to do fickle right estimation so
00:29:29
it's what you would be much better to
00:29:30
develop a pool tables still cocktail
00:29:33
solution which I so of beach yeah it's
00:29:36
really trying to do. So then from this
00:29:38
people back oh that's too bad we
00:29:42
selected all these individuals and then
00:29:44
it started to screen out all this
00:29:46
transpired just non targeted waiting.
00:29:49
Um so the work has been carried on
00:29:51
voting almost a year. I we've got
00:29:54
twelve point restraints entice about a
00:29:58
whatever twenty species. And which is
00:30:01
quite good. This is just the copper
00:30:03
species party in this entire. Um a got
00:30:08
up too three yeah hopefully we will get
00:30:11
it the sort of the whole colour
00:30:14
coverage in the a couple years time but
00:30:17
we are are trying very hardest really
00:30:20
labour intensive. It's quite
00:30:23
interesting okay so but from that we
00:30:28
are trying to look into do to develop
00:30:30
kind of cocktail solution so when
00:30:33
people come to be jointly sequence the
00:30:35
cup of tea or is the MP we give them a
00:30:37
or pale. And and probably sometimes a
00:30:41
card or other blue or any of those
00:30:43
traditional Madison so along try to
00:30:45
help manage or else. Okay eventually a
00:30:49
soul everything just has to be done in
00:30:52
which people are talking about a
00:30:54
collecting samples canonical samples
00:30:56
hundreds of samples there. But it's not
00:30:58
enough rain we as I say at the very
00:31:00
beginning we did meet with the meetings
00:31:02
and so we need more T a mixed data for
00:31:06
one meeting each at least I mean that
00:31:08
stuff I mean is the start if you look
00:31:12
at the cost but you know you've already
00:31:14
seen. It's you can solve more small. So
00:31:18
long as ten solid and your starter
00:31:19
people talking about once on your
00:31:21
stomach but we this right in ten years
00:31:24
you would be just one dollar just
00:31:26
really the way it is free I mean
00:31:32
everybody to sequence the in a minute
00:31:34
you know whatever for free. I think BTI
00:31:37
will all three four three couple of use
00:31:39
because the value of the data would be
00:31:43
much more than just generating the
00:31:46
data. So when you process rush. And
00:31:50
when you know exactly how to use the
00:31:51
data apply today to sequence the data
00:31:54
to generate data would be free and
00:31:58
obituaries on the way to to towards I
00:32:00
think next year we was started to
00:32:02
provide services to do a whole genome
00:32:05
sequencing at the level of several
00:32:08
hundred dollars you have dot how would
00:32:10
you know sequence with like a an almost
00:32:14
can have cafeteria sequencing at I
00:32:17
don't twenty thirty dollars whatever.
00:32:20
So everybody can afford to it. And
00:32:23
there would be a very interesting are
00:32:27
sort of interface we call voyager to
00:32:30
really demonstrate all these data. And
00:32:32
sort of make a community of people so
00:32:34
people if I have a one mini users in
00:32:37
these systems. So people could call
00:32:39
friends like they're competing with
00:32:40
their running competition so the
00:32:43
recorder friends to say okay so now we
00:32:45
are you need to do this steering group.
00:32:47
So to see how the but here it changes
00:32:49
and Ellis study would be done in is
00:32:52
that if you have enough data users. And
00:32:56
this is what PJ I will do in the next
00:32:58
two three years. And from that's
00:33:02
hopefully we will get in the first a
00:33:04
database a minute you know meeting all
00:33:07
makes database for well for the follow
00:33:09
up study. And from there I hope we can
00:33:12
have some complete picture of whole
00:33:15
picture of something at that time if I
00:33:18
was still get invitation from this
00:33:19
conference I may just tell you the real
00:33:24
sort of whole picture not by hypothesis
00:33:26
is true but actually dated even see
00:33:29
conclusion that I can show you but
00:33:31
anyway there is a lot of people join
00:33:34
the work as you can see just go and
00:33:36
Frederick has already give that talk to
00:33:38
a lot of others thank you very much for
00:00:00
"'cause" you just get started right
00:00:05
okay so one singular much for the
00:00:07
introduction to think you on what the
00:00:09
you're sort of the the meeting
00:00:11
organiser all you cannot hear noise
00:00:17
okay okay okay good. Um voice you
00:00:23
saying okay okay so I think in the
00:00:25
meeting all basically the opportunity
00:00:27
on the present well we've done in PGI a
00:00:31
I start to feel bad that I sort of
00:00:33
change to my talk from yesterday to
00:00:35
today it seems like become more and
00:00:39
more challenging to to give talks I
00:00:41
don't really have anything new to say a
00:00:44
after so many a fantastic talks
00:00:46
enquiries but I'm fine I so on a you
00:00:51
see these people of of course well what
00:00:54
I'm trying to present is actually to
00:00:56
use a systematic not skill mix way try
00:00:59
to see if we can sort of softball apply
00:01:02
this kind of you encouraging issues
00:01:04
that we've been discussing over
00:01:07
previously and this morning there on
00:01:10
polish has become really information by
00:01:14
people really sort of choice like blind
00:01:17
men try to are touched then if I guess
00:01:20
what the and if I'm not back are smart
00:01:23
people will have some cats may see mine
00:01:26
may not oh I'm actually a morse I don't
00:01:30
actually have a mine are well written.
00:01:33
So long as the problem. Um I remember
00:01:36
like almost ten years ago. I keep a
00:01:39
call to you know when nature sort of
00:01:41
interview me I said I don't really have
00:01:43
a pretty high wanna have muscle on but
00:01:48
actually few years ago in nature select
00:01:50
BSD the ten sinus matters that year. I
00:01:54
said I if you really don't have a brain
00:01:55
you don't know how to move the muscle
00:01:57
but yes it's actually trying to
00:02:01
demonstrate the problems of the current
00:02:04
particles starting is trying to get
00:02:08
something interesting on you a high
00:02:10
process is something based on
00:02:12
incomplete data set any we actually far
00:02:16
from doing. So are you know to to
00:02:19
really to look like sort of you can
00:02:22
what the and it look like you basically
00:02:26
need the whole picture. So you
00:02:28
basically stealing of very early face
00:02:31
collecting data. So you become that
00:02:33
very sort of important for PGI a which
00:02:37
is trying to do is can we started and
00:02:40
forget about hypotheses forget about
00:02:42
all the models conclusions knowledge
00:02:45
whatever on you control from those data
00:02:48
just collecting the data I mean just
00:02:50
collect whatever data I mean forget
00:02:52
about draw a conclusion from hundreds
00:02:54
of examples whatever how about the
00:02:57
meeting samples so how about getting a
00:03:00
multi a mix data well one meeting
00:03:02
individuals from all kinds of layers
00:03:06
contracting collecting. And worry about
00:03:08
all these hypotheses as you is made are
00:03:12
so this is basically a lot ideas there.
00:03:15
So we started to economics data back to
00:03:18
I mean the melody angst data back to
00:03:20
almost ten years ago. Um together with
00:03:23
just goes in every project we made a
00:03:25
great contribution there for these room
00:03:28
and whatever cover a story at that time
00:03:31
would be first time assembled are the
00:03:33
three meeting human opportunities
00:03:36
instant know objectively genes from
00:03:38
shop rates which is only thirty five
00:03:40
whatever fifty basically there but you
00:03:42
don't really believe that we can do
00:03:43
that job where we actually simple that
00:03:46
oh you have to be really prove for
00:03:48
success and people really use that as a
00:03:51
reference jean catalogue for many of
00:03:52
those studies they are we know it's not
00:03:55
enough you we probably give you the
00:03:57
most upon that one in the european
00:03:59
populations there is you far from
00:04:02
complete so we keep connecting are
00:04:05
Charlie samples American sample since
00:04:07
you know a sample more and doing more
00:04:10
analysis other publish this paper this
00:04:12
year together with again dominican
00:04:14
social. So we got a meeting jeans but I
00:04:18
don't think it's really enough real. So
00:04:21
of course no presentation is today it's
00:04:24
talking about thirty meeting jeans. I
00:04:27
don't know how many genes that there
00:04:28
but from this ten meeting jeans we've
00:04:30
constructed we've done the kind of
00:04:33
assessment there. Um is already cover
00:04:36
something like ninety five percent for
00:04:39
most of those sort of common jeans you
00:04:41
can see from individuals. And if you
00:04:44
look at from this remaining two ten
00:04:46
meeting genes are most of those genes
00:04:49
we discovered are kind of know what
00:04:51
currency in jeans which really
00:04:53
contributed most to expensive science.
00:04:55
So once again it's they're those other
00:04:58
re jeans and the the rear categories
00:05:01
you're a human got categories if you
00:05:05
look at all these functional difference
00:05:07
between those commenting reached on
00:05:09
individual specifically reached jeans
00:05:11
there. Um as you can probably guess
00:05:14
consult the you're commenting supply
00:05:16
functions essentially forcing by right
00:05:19
off but then this would be just
00:05:20
specific genes that we at all you know
00:05:22
recently on the reflected that station
00:05:25
to be a hosting immune systems admiring
00:05:27
factions in the body treatment you know
00:05:29
all these challenges that you actually
00:05:30
interact with the environment. And
00:05:33
tarzan whatever. So this is really not
00:05:37
really special case but then we started
00:05:39
to put all these data into a database
00:05:41
right so this is what you can download
00:05:43
the data you the data. And whatever so
00:05:45
one is constantly updating so is still
00:05:48
every so twenty four hours we had more
00:05:50
genes and functions whatever species on
00:05:53
their samples well but this is the the
00:05:57
first thing in the G level stuff right
00:06:00
all based on that people have been done
00:06:02
a lot of association study like the
00:06:04
chinese that that we do study with with
00:06:06
like you years ago european type two
00:06:08
diabetes study we you know the
00:06:10
obviously study and many more of those
00:06:12
isuzu studies. And that I need you know
00:06:16
a developed do not acknowledged just
00:06:19
started from the show reason to
00:06:21
assemble that you know the species that
00:06:22
stuff because I mean it's not a three
00:06:25
meeting meeting in columns crises this
00:06:27
silence species that people want to
00:06:29
study on I'm people also move onto this
00:06:33
is all americans comics Amanda proton
00:06:35
except. There's also lots of this kind
00:06:38
of sort of projects ongoing there. I'll
00:06:43
but we know then imitation of the
00:06:45
technology right you all sequencing all
00:06:48
assemble just from a strong reason to
00:06:50
it you know level or whatever MLGOMG
00:06:54
whatever you're talking about this

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Conference program

Introduction of the Session 1 : The Gut Microbiome: Facts and Figures
Josef Penninger, Institute of Molecular Biotechnology, Vienna
23 Oct. 2014 · 9:07 a.m.
The role of commensal bacteria in the gut
Willem de Vos, Wageningen University, The Neterlands
23 Oct. 2014 · 9:31 a.m.
Q&A : The role of commensal bacteria in the gut
Willem de Vos, Wageningen University, The Neterlands
23 Oct. 2014 · 10:29 a.m.
Gut microbial richness impacts human health
Dusko Ehrlich, INRA, Jouy-en-Josas, France
23 Oct. 2014 · 11:07 a.m.
Q&A : Gut microbial richness impacts human health
Dusko Ehrlich, INRA, Jouy-en-Josas, France
23 Oct. 2014 · 11:44 a.m.
Cross-talk between the mucosal immune system and environmental factors
Hiroshi Kiyono, The University of Tokyo, Japan
23 Oct. 2014 · 11:56 a.m.
Q&A : Cross-talk between the mucosal immune system and environmental factors
Hiroshi Kiyono, The University of Tokyo, Japan
23 Oct. 2014 · 12:31 p.m.
Introduction of the Session 2 : Host - Microbiome Interaction
Susan Suter, University of Geneva, Switzerland
23 Oct. 2014 · 1:41 p.m.
Mechanisms of cross talk in the gut
Annick Mercenier, Nestlé Research Center, Lausanne, Switzerland
23 Oct. 2014 · 1:55 p.m.
Q&A : Mechanisms of cross talk in the gut
Annick Mercenier, Nestlé Research Center, Lausanne, Switzerland
23 Oct. 2014 · 2:34 p.m.
Relationship of diet to gut microbiota diversity, stability and health in older people
Paul O'Toole, University College Cork, Ireland
23 Oct. 2014 · 3:52 p.m.
Q&A : Relationship of diet to gut microbiota diversity, stability and health in older people
Paul O'Toole, University College Cork, Ireland
23 Oct. 2014 · 4:27 p.m.
Gut microbes and their role in malnutrition and obesity
Rob Knight, University of Colorado, Boulder, USA
24 Oct. 2014 · 9:16 a.m.
Q&A : Gut microbes and their role in malnutrition and obesity
Rob Knight, University of Colorado, Boulder, USA
24 Oct. 2014 · 10:01 a.m.
The gut metagenome - your other genome
Jun Wang, BGI, Shenzhen, China
24 Oct. 2014 · 10:19 a.m.
Q&A : The gut metagenome - your other genome
Jun Wang, BGI, Shenzhen, China
24 Oct. 2014 · 10:53 a.m.
Fecal transplant to mine for novel probiotics
Max Nieuwdorp, Amsterdam Medical Center, The Netherlands
24 Oct. 2014 · 11:04 a.m.
Q&A : Fecal transplant to mine for novel probiotics
Max Nieuwdorp, Amsterdam Medical Center, The Netherlands
24 Oct. 2014 · 11:25 a.m.
Introduction of the Session 4 : Nutritional Interventions
Keiko Abe, The University of Tokyo, Japan
24 Oct. 2014 · 12:46 p.m.
Interactions between gut microbiota, host genetics and diet
Liping Zhao, Jiao Tang University, Shanghai, China
24 Oct. 2014 · 12:56 p.m.
Pediatric intervention - what works and what doesn't work
Hania Szajewska, The Medical University of Warsaw, Poland
24 Oct. 2014 · 1:47 p.m.
Q&A : Pediatric intervention - what works and what doesn't work
Hania Szajewska, The Medical University of Warsaw, Poland
24 Oct. 2014 · 2:15 p.m.
Perspectives for nutrition and the gut microbiome
Nicholas Schork, J. Craig Venter Institute, La Jolla, USA
24 Oct. 2014 · 3:02 p.m.
Q&A : Perspectives for nutrition and the gut microbiome
Nicholas Schork, J. Craig Venter Institute, La Jolla, USA
24 Oct. 2014 · 3:46 p.m.