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[Music]

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[Applause]

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hi I'm olive I'm undergrad at Carleton

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College working with Mika Anderson and

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my talk is about the silver pen genome

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and its evolution in deep sea

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hydrothermal vents so I would just like

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to reiterate how important it is to

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study hydrothermal vents system

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hydrothermal vents are one of the most

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ancient continuously inhabited ecosystem

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on earth and some research researchers

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believe that hydrothermal vent was the

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place libraries and if that's true them

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live diverse into these two lineages as

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we know bacteria and archaea and they

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invented new metabolic pathways and also

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they out the bacteria also and and

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everywhere like they also occupy new

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niches so that they spread spread out

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throughout the entire earth until now

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and so along that idea I would like to

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study microbial evolution and I think

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there is no more interesting place to

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study it and then in hydrothermal vent

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because this one of the key habitats in

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life's earliest stages and I have a few

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questions about microbial evolution and

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deep sea hydrothermal vents the first

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one what is the genome make variation

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that exists in this system and then why

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what drives this evolution is it

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challenge or or drift or is it necessity

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which is selection and lastly how do the

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microbes in hydrothermal vents diversify

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so first most of this talk is going to

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be about population genetics and when

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I'm thinking about population genetics I

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always think of the four phenomena or

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evolutionary forces such as selection

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which is the necessity mutation

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migration drift and which is the chance

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and how it creates an in tracks to give

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rise to this variation that we see in a

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population

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unlike in multicellular organisms in

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microbes the variation can take a whole

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new meaning so not only that their

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sequence can be diverse the genomic

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content can also vary between this

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and a specific species so some genes are

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shared by the whole species which is the

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core genes and some genes are not which

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is the accessory genes and there are

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multiple hypotheses how this accessory

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genes are acquired including horse no

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gene transfer and also large-scale

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deletion but I'm more interested in how

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this accessory genes are maintained

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throughout his evolution so there has

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been some literature debate on this

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either it is true selection which is the

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necessity or some paper also says that

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it is true drift which is chance or a

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little bit of both and to tie it back to

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our extreme engine environment I would

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like to study the span genome evolution

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in the deep sea a dream event and these

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are my two study sites the first one is

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the MIT caiman rice which is in the

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Caribbean Sea and these samples were

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taken in 2012 2013 by Joey Bruce group

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and the second one is the actual which

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is close to the hoenn de Fuca plate and

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these samples were taken in 2013 2015

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but also by Julie Hoover's lab so

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because a lot of these microbes were on

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Uncle Teva bull's eye we turned to

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metagenomic sequencing and I followed

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like the basic metagenomics against

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workflow so that assembly mapping and

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all that stuff and finally I did the

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pinning which is the interesting part

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and basically I've been my context based

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on the GC content and coverage mostly

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and then I also found like what taxa

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they belong to one talk set of pins

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belong to and for this purpose I'm

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mostly interested in the most abundant

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axon or at the genus level that I found

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in my samples which is sulfur ovum so

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Bravo miss sulfur oxidizing bacteria in

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the theatre among fans and from the bin

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recover meta silver atom genomes that I

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had I created a pan genome profile

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which is what you see here so each of

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this layer is a software from genome and

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then each of this sorry each of this bar

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represents like the gin grip so if the

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gin group is there then the bar X is on

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that layer and vice versa so now I'm

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interested in like what kind of genes

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there are in the cell phone pen genome

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so and also I'm interested in how those

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functions are distributed across the

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gene frequency so first here that each

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data point is the gin grip and the color

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bar basically means the gene function

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and on the x axis I have the gene

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frequency from gene containing only one

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genome to the one in 22 genome which is

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the core genome basically and on the XY

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axis I have the proportion of that gene

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function across a column so the most

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important trend here is that R is this R

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increase in proportion for translation

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coenzyme metabolism and amino acid

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metabolism functions across the gene

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frequency and the takeaway here is that

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the housekeeping functions are basically

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more enriched in the core genome versus

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the accessory genome which makes sense

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but the opposite is also true for the

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environment related signaling genes such

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as signal transduction and so on so here

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I like to point out that the accessory

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genome acquire acquisition and

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maintenance seems to be not random based

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on functions and so that this kind of

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like points out toward the selection

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rather than the chance case then if been

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genome evolution is really driven by

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selection I would like to know what kind

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of selective pressure exists in this

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environment and I would also like to

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know like if there is any local

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adaptation of this pen genomes so here I

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realized that there are two environments

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that my samples came from the mid chemin

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rise vent and the actual vent on so and

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they're really separated by the

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continent so

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separate and so I calculated the

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proportion for each unit calculated the

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proportion of that being found in only

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actual genome and then I sorted them

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from lowest to highest and this are

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basically the least represented genes in

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the actual genome so they're mostly

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represented and only made came in rice

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genomes and most of just genes belong to

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the blue category which is the ion

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transport metabolism categories and most

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interestingly they're also mostly

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related to phosphate uptake and

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regulation which means that phosphate

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related genes are more represented than

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in the mid cameron rice genomes versus

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the actual genomes this is interesting

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because in the atlantic ocean where meat

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came in rice is the phosphate content is

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lower than the passive sorry in divisive

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than in the pacific ocean to me this

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means that microbes that live in mate

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caiman rice could potentially have to

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innovate due to this phosphate like an

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environment by maintaining the accessory

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genes that they got through horizontal

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gene transfer and this result is

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actually pretty similar to what more in

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common found and the prochlorococcus in

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the surface ocean and just like i would

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just like to try out throw it out there

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because i also found a lot of arsenate

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related genes in the mid commander eyes

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compared compared to actual genomes and

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this is also about the prochlorococcus

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paper found in the surface ocean and i

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we also looked at the PNP s ratio which

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kind of like suggests the strength of

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evolution on each gene I looked at um so

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basically if the P NP r--'s ratio is

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higher than 1 then just somewhat

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adaptive evolution or positive evolution

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and if the Pampas ratio is closer to 0

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then negative selection or purifying

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selection which is more of conservation

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then change happens on that gene

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what I found here is that accessory

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genes tend to have higher bnps ratios

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than the core genes and some of these

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genes also have been passed ratio higher

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than one but I'm not really entirely

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sure

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statistically here because this were not

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you I didn't really calculate defense

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base ratios using a maximum likelihood

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modal but just like kind of like point

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estimate for each gym so like this

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higher bnps ratio for accessory gene

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might be due to either adaptive

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evolution on these genes or that that

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this genes are less likely to undergo

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negative selection or purifying

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selection so in conclusion we could have

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saw that man genome evolution is

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selective and we had several evidence of

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this the first one is that different

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gene categories were enriched in core

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versus accessory genomes which was the

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first one and we also saw some local

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adaptation potentially due to phosphate

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on content difference between the mid

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came in rice in the actual environment

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and then we also saw that there was

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higher probability of adaptive evolution

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in the accessory genome compared to the

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core genome or that there was some

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different evolutionary scheme of

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accessory genome compared to the court

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genome and finally if though I didn't

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really mention it here we saw some

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evidence of gene specific sweeps which

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kind of points a selection that happens

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in these microbial populations and some

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more of bigger-picture conclusions we

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saw that necessity was the key factor

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and pan genome evolution in hydrothermal

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vent compactive chance and third is

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still an open question how important

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necessary was in early life the genomes

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of easy today have been molded by

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evolution from geum's back then so we

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would infer some connection through that

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and finally I would also point out that

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when genome variation

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the importance of study pen genomic

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variation in addition to just single

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point polymorphisms because pen genomic

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variation is really widespread and it

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also takes into account the one

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evolutionary force which this original

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gene transfer that is not really taken

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into account by just single point

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polymorphism and by that I would like to

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thank the Andersen lab at Carleton and

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all the crews that did the same thing

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and the funding thank you okay do we

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have any questions for all of you can

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yeah so yes so the question was the

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connection livened extremo file and

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genome evolution part yeah like when I

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was getting to the project I didn't

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really care about extra part because I

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was really looking into the evolution of

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pan genomes and so it wasn't necessarily

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like into just like specific to a pen

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genome and I I realized that like

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studying XML files not probably the best

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like place to do like you know fusion

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study but yeah that was the data that I

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great talk I'm I'm all I'm a postdoc at

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Ames drift the chance drifts is usually

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a pretty strong function of population

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sides yeah I wonder if there's a way for

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in your data set to estimate population

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size yeah so I so first we kind of like

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had some like the time series data set

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as well and we kind of like I guess like

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I saw that there was like a decrease of

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like the coverage from year to year

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but I don't really know like the the was

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it called the absolute population size

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or like the aphid population side for

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the populations that I have so yeah

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probably

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Dhar like some ways to estimate the

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using just coverage but yeah sure thank

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you very much thank you everyone for