 Thanks very much, Roderick. And first of all, I just also want to make kind of a shout out to all the people who are attending these conferences and doing science in the face of all the dynamic commitments of home. So not just children running in but, you know, having to do things that you wouldn't otherwise have to do at a conference. So, I want to talk today about comparative regulatory genomics and how encode has helped enable extensive studies of regulatory evolution by technology, both computational and experimental. So, one of the things that has been shown is that transcription factor binding sites when you look across evolution. They change rapidly and in fact the change is exponential with evolutionary times the drop off is really very quick. And this is true. Here's an example of transcription factor binding CBP alpha in liver and you can compare this with long branch lengths or short branch lengths and the drop off is actually relatively quick in the transcription factor binding site locations. And indeed, work from triaticers group a few years ago showed that the rates are indistinguishable regardless of whether you're looking at mammals or insects embryos or adult tissues, or different factors. This is just a common way that transcription factor binding sites change over evolutionary time they change rapidly. Now, at the same time gene expression evolves slowly and these changes are essentially linear. This is also from the combined analysis that came out of triaticers group. And we can see that we end up in this situation where we see an evolution of transcriptional regulation and multiple things are happening simultaneously gene expression levels are evolutionarily stable tissue expression profiles are maintained. But at the same time the tissue specific transcription factor binding is neither stable nor maintained. So if we are out looking for those sites of interest of regulatory regions for doing genome interpretation, we need to take this into account. And trying to understand more of how we can, we can decide these together and make sense of this has been a focus of research in my group over time. So one of the things we showed a few years ago is that in looking across 20 different mammals and mapping promoters and enhancers that conserved regulatory regions are really very rare. And these tend to be promoters. So if you look from the point of view of human there's roughly 41,000 human regulatory regions in the way we did the assay. And the next are roughly 1200 promoters and 30,000 or 12,000 promoters 30,000 enhancers. But of those, only about 10, or only about 20% of the promoters are highly conserved should shared across many species, but a very small number of these enhancers are highly conserved only about one 10th of 1%. This gives something to look at. The other thing that we noticed is across these 20 species about half of all the active enhancers and liver are found in a given species are found only in that species. And so we called those recently evolved. So the vast majority of, when you look across species the vast majority of the regular is in this recently evolved category, or the majority of the, the regular is in the recently evolved category and only a small amount is in the conserved category. So you can look at the origins of these, of these various categories and especially the recently evolved ones. As it turned out the recently evolved promoters tend to arise from young DNA. So, young DNA here means DNA that's not present in the, and the multiple alignment of these species, generally added in various places through duplication, or in some cases, transposable elements. The enhancers tend to arise from ancestral DNA that is DNA that was present in the multiple sequence alignment, but gets captured in only one of these lineages in an enhancer form. So there's apparently enhancer potential in the common ancestor, but it's only being instantiated down one of the lineages. It's important to note that the scales on these two are very different. There's many more of these recently evolved enhancers. There's 10 times as many as the scale. So this means there's actually thousands of, of enhancers that arise from ancestral DNA. And most of those, as you can see, or many of those arise tend to arise from transposable elements, which I think provides a very interesting avenue of future research. We've spent some time trying to connect these regulatory, the regulatory evolution to gene expression. You can see an example off to the side of, of gene expression and regulatory landscape for 10 species. There's a number of characteristics that are required to be addressed in this situation. So we address two of them, which is gene expression level and expression stability. So the expression level is relatively simple. It's how much the gene is expressed. But the stability is, is also important for evolution. It's how variable it is across species. Now these characteristics are confounded, highly expressed genes are more evolutionarily stable. Of course, the assembly quality, which was a larger concern when we were doing this study than it is today as, as more high quality assemblies are being produced also impacts the analysis. And we end up, you end up basically seeing that we need to take apart these these details of expression level and expression stability to get at how we can connect regulatory evolution to gene expression. And what we found from this is that the number of regulatory elements is the primary driver of both expression level and expression stability. What you see is that the gene expression basically works with promoters as if it's a switch from zero to one, the gene expression turns on there's relatively little additional expression with more promoters. But as enhancers are added the gene expression and indeed gene expression stability or the correlation, both increase. We had to do this I'm not sure if my pointer here we had to do this with a finding controls in various places that allowed it to to match various gene gene situations where we would match for expression level with different numbers of promoters expression level with different numbers of enhancers to determine these results. And this gets back to the point of regulatory complexity. If the number of regulatory regions is the primary driver, then more complex regulatory landscapes those with more regulatory regions are the ones that are under greater regulatory selection both from an expression level and from an expression stability point of view. So we did look into the effective evolutionary conservation. And although the primary driver, again, is the number of enhancers, those enhancers that are conserved are slightly stronger. So genes with placental conserved elements are more highly expressed. And here we did this by doing a control where we had the same number of enhancers and conserved versus non conserved. And genes with conserved elements are more stable. Again, in here we looked at conserved elements and double matched for expression level and number of elements. We also show that recently evolved enhancers are slightly weaker. And so, again, you can add a recently evolved enhancer and you can see that it does affect the gene expression, it does expect affect the gene expression stability. So we put a bit of evidence that we find from this that at least overall the recently evolved enhancers are functional. And they add, again, to the overall regulatory landscape, which leads to expression level and expression stability. And then investigate some details about how tissue specificity and different tissues come into play. Now I mentioned earlier that most of these recently evolved enhancers are present in the multiple sequence alignment, suggesting that they had enhancer potential in the ancestral sequence. We hypothesized that what might be happening is that the enhancer potential is being captured in different tissues down different lineages. And so to try to get some insight into this, we mapped regulatory elements in four tissues and 10 species. And, and try to make sense of this. Some of the results that come out of this is the tissue specific regulatory landscape is actually very consistent across species. You get approximately the same number of active promoters enhancers and prime enhancers in these four tissues, these four tissues are also quite distinct over time. You will notice some differences in the numbers that we are getting for active promoters and active enhancers. We sequence these libraries much deeper and use some different. We used an additional histone modification in our definitions to do this. But the results are largely comparable to the previous ones. So one of the things we see from this is that our initial hypothesis. So the initial observation of tissue specificity of these regulatory elements does play out a large promote portion of promoters are active across tissues, but the vast majority of enhancers are predominantly tissue specific and occur only in one tissue. So you can see that the, the gene expression activity up here in black largely follows the pattern of promoters as to how many tissues that they are formed in. Now this is taking from a one species at a time point of view, even though this is all species, some together so there's no exact evolutionary analysis here this is about tissue specificity and tissue shared activity. So what we could look at and this asked the answer or goes some way to answering the question of the hypothesis that that I suggested is that because there's enhancer and ancestral enhancer potential, whether or not we can see switching of this regulatory identity down over evolutionary time. The first thing we looked at was how often within a species promoters became enhancers in another tissue. This actually happens relatively rarely. It's also true that the active enhancers and the prime enhancers switch their roles relatively rarely across these four tissues. I think this calls into question some of the, some of the, the theories that suggest that promoters and enhancers are really on a continuum. They look more distinct from this analysis, although obviously there's more to do. Now between species. It's a different story regulatory identity changes are common between species, 20% of the promoters change identities and the enhancers and the the prime enhancers almost look like they're an evolutionary equilibrium. We also found these dynamic promoter enhancers. These are those up here that switch within a given species. These are not maintained by evolution. We're also looked into some characteristics of these inter species switchers. So regions are more likely to switch identity over longer evolutionary time, which I think is something that would be expected. We use the fact that we had a nice evolutionary structure with two in group and out group analysis to estimate directionality of of our switches. And what we can see from this is that promoters basically tend to stay promoters. And again, we have this kind of dynamic change between the prime enhancers and the active enhancers with enhancers likely to become promoters. So we're able to look at the tissue specific profiles of evolutionarily dynamic regions. And these are less typical than their non evolutionarily dynamic counterparts. The promoters that are evolutionary dynamics are the ones that are changing between species. They are less likely to be tissue shared and the enhancers that are changing between species to something else are actually more likely to be tissue shared. So, finally, we looked into some of the things that might be driving this tissue specificity versus this tissue shared versus tissue specific axis, as well as the inter species dynamic regions. And what we found in both cases is that lines. So retro transposons and line L2s, which are the more ancient of the lines seem to serve as a reservoir of regulatory potential. And this is true both for these those that are dynamic between tissue shared and and tissue specific, as well as those that are inter species that change between species. If you're interested in this, we do have a preprint up on bio archive that you can go and take a look at. And just before I'm done, I wanted to kind of make one comment about some of the things that we can build off of from here and and some of the future opportunities. So one of them is instantiated by a group of projects in Europe under the Fang consortium. So that's the functional annotation of animal genomes to essentially encode like analysis for farmed species. The European Commission has funded three interconnected projects aqua fang bow reg and gene switch, focusing on aquaculture species, beef and dairy cattle and chicken and pigs. And the goal of these projects is to generate functional annotation and do comparative genomics across them. And I'll just give you a quick example from aqua fang. So here are the six species that they're looking at. The goal is to create body maps of multiple tissues, developmental maps for these species and immunomaps to try to understand how the, how these things work together. You'll see from looking at the aqua fang assays. These are very familiar to all of us who have used and worked in the encode data and the types of samples that are being looked at are also actually very ambitious. And so to conclude, a million tissue regulatory profiles which we can see from doing comparative regulatory genomics are globally similar across species and somatic tissues. Within a species it turns out to be relatively rare for a regulatory region to change between a promoter and enhancer, but this is a common thing across species. L2s appeal to be something of a Swiss army knife of regulatory potential and regulatory genomes, and there's a host of exciting opportunities for genome interpretation within and across species as we move forward. And so finally just to acknowledge the people that did the work and the various results that were presented and our funding. So thank you very much. I'm happy to take questions if there are any. Yes.