Sometimes when I talk about natural selection
I talk about it as being like gardening. Right?
Like you can imagine a plot of land where
you've got some flowers growing, right?
And seeds are randomly falling in that plot
of land.
Let's say dandelion seeds or other weeds,
right?
And if you don't care– if you're the sort
of gardener who doesn't care about weeds in
your garden, you just let those seeds sprout
and grow and, you know, you'll have some mixture
of dandelions and your cultivated flowers
in your garden.
Right?
Natural selection is like a gardener who weeds
those seeds out.
And so if you look at two gardens.
One of them has a lot of weeds and one of
them doesn't, and they're in all other respects
matched to one another, right?
Then you can say something about how diligent
the gardener is by comparing them.
And that's essentially what we do in the genome.
So we look for regions of the genome where
the new mutations have been carefully eliminated
by natural selection, and those are regions
where we can say selection is strong.
We've actually played this game a lot– over
time we've played the game of using these
signatures of natural selection to identify
particular regions in the genome that might
be important.
What we're doing here was slightly different.
We were trying to say, "how can we use that
in order to get a global measure of how important
each one of these epigenomic datasets is?"
Right?
So, rather than pinpointing specific region
of the genome which are important –and some
of that comes out as a byproduct of the synopsis
anyway– rather than that being our main
goal, our main goal was to say, "how much
do we learn genome wide by doing an attack-seq
experiment or a chip-seq experiment or from
RNA-seq data?"
