In fact, this is Dave Vellante of wikibon.org. I'm here with that Stu Miniman, who's also with Wikibon. This is theCUBE's Silicon Angles flagship continuous production of Edge. We're going wall to wall. This is day two. We're here live at the Mandalay Bay Hotel in Las Vegas. Deepak Advani is here. He's the general manager of cloud and smarter infrastructure at IBM. Relatively new name for a group and with expanding portfolios. Congratulations and welcome to theCUBE Deepak. Thank you very much. It's my pleasure to be here. So it's good to see Edge have folks attend, participate from IBM like yourself. So it's not just a hardware show, not just a storage show. We heard on Bush Goyal yesterday say, you know, it's not about storage. It's about the data and you know, we like that message. But so talk about why you're here and what you're doing, what your message is to customers. Yeah, I know it's great to be here. It's great to interact with my colleagues and with clients and with partners. And you know, at the end of the day, what I'm here to do is to convey to our clients our point of view on things like cloud. You know, the world is getting smarter. Infrastructure is getting instrumented, it's becoming smarter. And how do our clients create value by partnering with IBM in the areas of cloud and smarter infrastructure? Yeah, so let's unpack some of those a little bit. So cloud is just this, I've been watching this since day one and you know, the Berkeley paper came out and the IT industry in general, the enterprise IT players said, hey, we can play in cloud too. Yeah, yeah. And so you had the CIO's head was in a vice. You know, it was coming at him or her from all angles. And generally they've responded pretty well with the help of companies like IBM. But so where are we in cloud? I mean, most of the emphasis has of course been on the private cloud. And that's starting to extend, most customers will now, first of all, I'll tell you, cloud is real. It's no longer a buzzword. The second thing they'll tell you is, we're just, we're looking at hybrid models. We're not just going to put it all in AWS. We want to have some kind of hybrid and they're starting to affect those strategies. Where do you see the customer base at in terms of cloud adoption? Yeah, you know, I think it's really important to sort of step back and look at why is cloud really resonating, right? I mean, more and more you're seeing CIO's and IT departments are under tremendous pressure to do more with less. You know, the budgets are going down year after year. The percentage of the budget that goes towards keeping the lights on keeps increasing every year. And at the same time, line of business is saying, do things faster for me. You know, and so they're under a lot of pressure and cloud really enables the IT organizations to do a couple of things. One is optimize their infrastructure and by freeing up resources and capacity and bandwidth they can accelerate innovation which they're being asked from the business to do. So creating this balance of optimization and accelerating innovation is something that cloud is enabling them to do. And that's why I think there's so much interest and is it private? Is it public? Look, our point of view in this is the world is hybrid and world is going to remain hybrid. And you got to come at it from a workload point of view. Some workloads are ideal for a private cloud. Why? Because maybe you've got some security or compliance reasons why you don't want the data to leave your enterprise. Why? Maybe because there's enough scale you have within your enterprise that you can achieve all the economics of scale and efficiency and utilization and rapid provisioning, all those benefits by having a private cloud. And in some cases, public cloud may make more sense because maybe the data you're trying to analyze is machine to machine data or social media data. It's data that's born outside the enterprise to begin with. Makes sense to analyze that in a public cloud. So we strongly believe that you got to look at and understand the workloads and you got to pick a model that is supporting of the hybrid world. So you support public and private workloads and provide the ability to migrate workloads from private to public, public to public. And that's where standards come in. Yeah, well, and so you guys just made the big acquisition or announced anyway, you're going to acquire a software, two billion dollar acquisition. Actually, the number wasn't reported but that's what everybody else was reporting. I think you guys mentioned it, but, and that's not part of your division. However, that brings opportunity for you to orchestrate across those various different clouds, doesn't it? Absolutely it does because, you know, but software, you know, they're the largest privately held public cloud infrastructure as a service provider. 21,000 clients, 13 data centers and radical ease of use and simplicity and they cater a lot to companies that are born in the web. So it's a really great solution for them. And when you couple that with the strengths that IBM has with their smart cloud offerings, focusing on born in the enterprise type workloads and companies, it really provides their clients the ultimate choice. Because at the end of the day, some clients are saying, you know, I've got hardware, I've already made a decision on that. Give me the software that enables me to build private clouds. Well, we've got our offerings called smart cloud foundation that lets you do just that. Other clients are saying, well, I want time to value. Can you package in your expertise around workloads in an integrated system? So we have a family of systems called pure systems that lets you do that. Others will say, look, just host everything for me or give me a cloud offering, public cloud offering and for that, we have a soft layer. So we've got a very broad set of offerings. Well, the other soft layer is amazing automation. I mean, they are the best at automation. And interesting, I don't know if a lot of people know this, they do a lot of dedicated hosting. They do a lot of bare metal, which is somewhat surprising, right? And so, but a lot of the clients are saying, hey, we just want the dedicated hosting, whether it's for security reasons or maybe even performance reasons. They don't want the multi-tenancy piece, which is kind of fascinating in this day and age, a company who's grown very nicely with that model. It gives people a choice.  You know, depends on the client, depends on the workload, but you've got the ultimate choice. And the automation piece, again, is key. We watch, of course, I'm sure you do too, the hyperscale guys and what they're doing and the innovations that they're bringing. And that's clearly seeping into the enterprise. And again, I think it's a great opportunity for a company like IBM. Well, it goes down to, you know, people are looking for efficiencies. If you can run and optimize your infrastructure that frees up resources that you can then apply to accelerate innovation. And by standardizing standard operating procedures and automating, think about it. I mean, used to be an administrator would tweak the system and, you know, change some configuration parameters, set things up and that institutional knowledge would be stored in their head. Now you have the ability to capture that. As Joe. As Joe, as Joe knows. But now you have the ability to capture that, whether it's using, you know, de facto industry standards like chef and chef recipes or building your own pure patterns. But the ability to capture that expertise and automate it is how you get efficiencies that enable you to accelerate innovation. Yeah, so Deepak, question I have for you is if we look at kind of the cloud discussion that's been going on for at least the last five, six years. Management and security have kind of been the two challenges that people have said, kind of holding back some of that adoption. As we said, clouds much beyond kind of the buzzword. But, you know, where would you grade the industry? How do you think kind of IVM and the industry as a whole are doing it solving, especially that management challenge?  I think things are starting to accelerate in a big way when it comes to that. And I think a lot of it is because of standards. Because at the end of the day, I think industry is doing a decent job of coming up with proprietary solutions that solve a particular problem with certain asset of clients. One of the things that we have very deep conviction in is the support of open standards and partnering with the community. And that's really how you accelerate things. So, you know, when we announce the support for OpenStack, you know, OpenStack to us is a really big deal because it enables our clients to have a choice of hypervisors first and foremost. It gives them that choice. But it also gives you the ability to do things in a really industry standard way. Build a software-defined environment. You know, for compute, for storage, for networking. And what that enables you to do is do a much better job of automation and also orchestrating your workloads. So one of the key products we came out with, Smart Cloud Orchestrator, enables someone to do an outstanding job of defining a workload, not just only how to provision it. But if you want to do things like failover and other type of things, how can you automate the orchestration of that workload so it truly is optimized? That type of innovation is possible now because of supporting standards in a big way. Okay, the other thing I want to poke on, you talked about Chef. And if we look at things like Chef and Puppet out there, you've got a certain class of customers that are going to be comfortable with having people that can program their environments and tweak it and play with it. And then there's the other set of environments that just, they want a solution. They want you to do the automation. You know, where do you see those lines in the industry? Yeah, so basically what we think is, you know, when you have things like Chef and you know, Tosca, another emerging standard from Oasis on defining these patterns, the reason this becomes really important is because you've got the practitioners that are creating content. You know, so whether they're cookbooks or whether they're patterns, you're creating this catalog of automation that others who just want a solution can now pick and choose from. And the power of bringing open source and communities together is everyone then starts to develop a set of, you know, sort of packages, if you will, that are very reusable. And it sort of starts serving both of the markets really well. On one hand, you've got practitioners capturing their expertise in a catalog. On another hand, you can start packaging it together in a turnkey solution that gives others time to bag. Okay, I'm wondering if you could bring that home for us with pure systems. There's that expert integration knowledge. How does that play with what you were just talking about? Absolutely. So you know, so one of the key things that we do in the lab is we've got some pretty smart people at IBM. And we've got the ability to tune and optimize these workloads. And what we've been asked for years by our clients is can you bottle that expertise up? So I don't have to reinvent the wheel every time I buy something, right? So pure is the ultimate manifestation of not just simplicity and time to value, but there's whole notion of understanding and optimizing the workloads and capturing that expertise in a pattern that there's a lot of work that happens in the labs to develop that IP. But that enables our clients to get optimized systems and get them up and running quickly. Yeah, so OpenStack is obviously very exciting. You know, for years you guys obviously participate in adding value to whomever's hypervisor was out there, but you couldn't directly participate in the code. And you guys have hopped on this with others. But IBM is one of the top contributors. Red Hat, obviously, HP clearly, and some others. But talk about how that changes the competitive dynamic in the industry. Yeah, it's very interesting, right? Because at the end of the day, we've got 500 people working with communities on cloud-related open source projects. Half of them, around 250 are around OpenStack because that's the best replacing. And what OpenStack really does is, you know, we believe that it's a good thing to have choice of hypervisors out there.  That's great. And the same thing with storage. You know, you can have a choice of hypervisors. So OpenStack enables you to have that choice, which at the hypervisor level is good. But also what OpenStack enables you to do is to start building out the software-defined environment on top of which you can start doing things like orchestration and automate a lot of management. And that's very, very powerful. Yeah, so, obviously, you got big contributions. So where do you see this coming? OpenStack seems like it's got a lot of momentum. It's still a little bit early. Most of the deployments are within, you know, cloud service providers. But, you know, we love open source here at SiliconANGLE Wikibon. We always have said open source long-term wins. So how do you see that evolving? Is this a 10-year journey? Do you see, you know, major deployments in nearer term, like within the next 18 to 24 months? Absolutely.  It's like, you know, three years ago, people weren't talking about OpenStack a year ago. People weren't talking as much about it as they are now. And so the momentum is accelerating for sure. We actually have already shipped a product that includes OpenStack, that bundles OpenStack. It's called Smart Cloud Orchestrator. And we have dozens of clients, early clients, who've been looking at it and kicking the tires on it. My prediction is this is going to accelerate faster than any of us realize. And the reason for that is because the value is there. You know, the economic value that's being driven to clients is there. And because of that, it's just going to take off. Yeah, awesome. All right, T-Pack, we're getting the hook. I really appreciate you coming by, sharing your perspectives. And it is an exciting time. And good luck with the initiatives. Thank you very much. Thanks for having me. All right, guys, keep it right there. We will be right back after this word. We're live from Edge. This is Dave Vellante with Stu Miniman. We'll be right back.


Hi there! Thank you for allowing me, Monica Wahee, to assist you as you proceed along your journey into SAS On Demand for Academics, otherwise known as SAS ODA. In this video, I'm going to show you how to add where criteria to your PROC-FREAK code when you run frequencies in SAS. Okay, here we are in the SAS On Demand for Academics, or SAS ODA, Environment. If you need help setting up your free account in SAS ODA, you can take my free online course with a title that says it all, Getting Started with SAS ODA. I'll link you to it in the description. Okay, so assume you got started with SAS ODA, and now you can open a program window and copy and paste code into it. So, go on to GitHub and get this code and paste it into your program window. Now, this video is about using where criteria with PROC-FREAK. If you don't know how to get this data set we are using into SAS, then you better watch my video on that topic. And if you are unfamiliar with PROC-FREAK, you better watch my video on that topic. I'll link you to those in the description. As in those previous videos, we are working with a data set we read into SAS and named BRFSS underscore A. We use the name BRFSS because the data set contains data from a health survey in the U.S. called the BRFSS. Actually, it just contains data from three states because I had to make the data set small enough to fit into SAS ODA. If you watch my PROC-FREAK video, you'll remember that we ran a frequency on the underscore state variable, and we saw that there were only three values, 12, 25, and 27. So, what's different in this video is we are going to filter by one of these states. In other words, we are going to run a one-way frequency, but this time instead of running it on the state variable, we are going to set criteria on the state variable. So, because we are setting criteria on the state variable, I had to pick a different variable to run our one-way frequency on. I have chosen the variable Diabetes 3, which stands for Diabetes. The respondent can give a bunch of different answers like yes, prediabetes, and yes, but only during pregnancy. So, there's a lot of potential values, but the point of this video is this where statement here. Remember how I said that there were three states, 12, 25, and 27? Well, this where underscore state equals 12 is how we filter by just the records where the state is 12.  I added a title. You can put a title statement in most prox and sass, and it doesn't usually matter where you put it. Like, I could have put it right after the prox freak line. The state of 12 stands for Florida, so that's why I made the title prox freak with Florida only. Okay, let's run all this and see what we get. Okay, this looks a little like what we'd expect. At the top, we see our title printed out prox freak with Florida only, and we see multiple levels of Diabetes 3 in its column, so nothing suspicious here. Let's look at the log file. Okay, this is what really convinces me, what is written in the sass log file? I'll read this aloud. There were 15,242 observations read from the dataset work.brfss underscore a. Okay, but now let's look at the next line. Where underscore state equals 12? Now, if you watched my last video where we ran a one-way frequency on underscore state in this dataset, you'll recognize that the state with code 12 had 15,242 records. So, this is what actually makes me believe our where statement worked, and sass honored our criteria in our frequency procedure. Remember, in sass, the log file has the last word. If you want to get a better understanding of sass, I encourage you to take my free online course in how to use sass on demand for academics. All these lessons are based on my plain-spoken yet eloquent book, Mastering Sass Programming for Data Warehousing. I'll link to these in the description. Thanks for watching and have a transformational day!


And today I'm going to talk to you about how to use various tools to understand gene regulation, particularly gene regulation within some data set that you have. So at the end of this workshop, you should be able to understand challenges in predicting transcription factor binding, be able to identify binding sites for known transcription factors, and in the lab, we will delve deeper into discovering transcription factor binding motifs in genomic regions with MIME chip and other tools. So I have a brief overview here. This should be in your notes, and let's just dive in at this point. If you have any questions in the middle of the lecture, feel free to ask. So I'm going to start with an introduction to how transcription works in eukaryotic cells. So here's a really, really simple model of this, which is that you have... Yeah, so that there is a particular transcription factor binding site in the promoter of a gene. So here we have a transcription factor binding site. It looks like CCA, GGG, TAT, and there's a transcription factor that is shaped to interact with that particular binding site. And so that means that there is a good binding energy between the transcription factor and the binding site. The transcription factor is bound there. And then the transcription factor is where it recruits RNA polymerase 2, RNA polymerase 2. So it comes along and transcribes DNA downstream of this transcription factor binding site and transcription start site. So there's actually a lot more to it than that very simple model, because you can have a number of transcription factor binding sites within a proximal regulatory region or promoter. There are also things that you'll find in distal regulatory regions, both upstream and downstream of the transcription start site. And so there are a lot of different components to the taxonomy of transcription initiation. And remember, all we're talking about here is regulating the rate at which transcription is initiated. And of course, gene regulation in general has many downstream components that can be regulated, such as mRNA splicing or decay or other post-transcriptional regulation and even post-transitional regulation. And all of this has made it a bit more complicated because this textbook model of a promoter is something transcription factors influencing transcription at a promoter is slowly giving way to a more complex model where we see that the genome is folded around itself in three dimensions and you can often have important effects of distal enhancer regions that are nearby in three dimensions, but perhaps not very close along the one-dimensional measurement of the genome. And all of these things can affect how much transcription occurs at a particular place. So one nice thing is that now we have developed methods for measuring or observing these various elements that can affect transcription factor binding. So we have RNA-seq, which I'm sure all of you are familiar with, but there are also techniques that we can use to measure specifically five-prime ends of newly transcribed RNA, like cage and also use things like row-seq. And then there are techniques that we can use to figure out where transcription factors are bound to figure out where various histones have been modified in particular ways. So we can do that with chip-seq and we can do chip-seq both at promoters, at transcription factor binding sites, and also at some of these distal regulatory regions. So all of this can give us a more complete picture of what is actually going on in the regulation of gene transcription. A lot of this data has been collected by various large projects, so things like the ENCODE project, which has literally thousands of data sets of these types, Roadmap Epigenomics project, which again has many more. Those are both focused on human with some mouse, and in the case of the ENCODE project, there's a mod ENCODE for fly and worm data. And then there's a lot more data that you can find, say, in GEO from various smaller-scale experiments. People have done with these. So the take-home for this first part of the talk, the lecture, is that transcription can be very complex, and I just briefly touched upon the fact that there are now many measurements or observations of the various complexities of this. But in the next part, we're going to go back to a simpler model of how transcription factor binding works, and we're going to see if we can teach a computer how to find transcription factor binding sites. If I go on, any questions or comments on that in the first part? OK. So as I showed you earlier, a transcription factor has a cognate DNA sequence that it likes to bind. So in this case, we might represent some transcription factor by transcription factors binding motif or the site it likes to bind with this string, A-G-T-T-A-T-G-A. The thing about transcription factors is that their recognition code is degenerate. So things don't have to be exactly like this particular binding site that we've displayed here for transcription factor to recognize it. And often there's not an offer on for transcription factor. There will be lots of different sites or lots of different sequences that the transcription factor will bind, and it will bind some of them with higher energy and some of them with lower energy. So here you can see a number of different individual binding sites that this imaginary transcription factor we're considering has. And you can see this degeneracy because in some positions, like the first position changes quite a bit, the fourth position is always T and so on. So you can represent this amount of degeneracy with a sort of wildcard DNA alphabet. This is called the IUPAC ambiguous DNA alphabet where you can replace certain letters with other letters that represent their wildcard nature. So for example, this T, it's always T, so it says it's T. This T gets replaced by a W, which stands for weak because it means it's A or T. So if you look in the fifth column, you'll see that it's almost always T, except down here it's A. And then there are other things like V means A or C or G, D means A or G or T, R means A or G or so on. So there's a whole string full of letters for people like me to memorize. And for you not to because we have better methods for doing this. This particular method is a nice start, but it doesn't really represent all of the data that you would have in a set of binding sites. So for example, this fifth column here, I think every single one of the rows is a T except for the last row, which is A. So there's a lot of information there that's thrown out if you say this is A or T. In reality, we want to say that it's almost always T. And the further you get away from T, the less likely the transcription factor is to bind. So finally, I'll introduce a matrix, a matrix model that we can use to understand how this transcription factor interacts with its motif. And this is called a position frequency matrix. And what we do is we have one column in this matrix for every column in our aligned binding sites. And we have a row for each letter in the alphabet. And then we will simply count out the number of times that symbol occurs at that column. So again, fourth column, 21 T is always T. The fifth column is 20 T is 1A and so on. And so you can use this to have a slightly more nuanced understanding of what the motif is. And this can also be transformed into this sort of sequence logo, which I'm sure many of you have seen before. So the sequence logo is a graphical representation of a matrix model for transcription factor binding. And where you can see, for example, let's look at this fourth column again, there's a T here. The T is taller than the other symbol because the height gives you the amount of information that you essentially get from seeing that particular symbol there. And the next position, there's still a tall T, but it's a little less tall because sometimes one out of every 21 times this transcription factor is known to bind even when there's A there. So if you've ever seen these in an article or a presentation somewhere, it's really a representation of an underlying matrix much like this one. Any questions so far? So that's one matrix model. There are actually a variety of different matrix models, and the one that is used most frequently is called a position-specific scoring matrix or position weight matrix. There are a variety of different formulations for PWM or PSSM. So unfortunately just saying that doesn't tell you exactly what it is, but generally it is something like this. So for a position-specific scoring matrix like this one, what we do is we start with a position-frequency matrix, and we transform it into something that can be used more universally and something that includes a model of the background frequencies of the nucleotides within the genome we're interested in. So we have here FB, I apologize for the formatting, I think. This would be better if we have the PDF version up here. Is this the PDF? No, this is the PowerPoint. Yeah. There's two slides. Yeah, that's fine. I'll just, totally fine. I think it'll just be easier if we can see the, no, it got messed up in the video. Is this the PDF I sent or did you remake it? Okay. That's fine. It's just a Mac PC font issues. Anyway, it's no big deal. So what we have here is the FB, I here. That represents the number from the position-frequency matrix. So B is a base, A, C, G, or T. I is a column here, 1, 2, 3, 4, or 5. So we take that and then we add this SN, which is a function of the sample size used to produce the PFM. So intuitively, if you do an experiment to figure out a transcription factors binding motif, and you get six samples, and from that you infer that this position is always A, and the fourth position is C, 80% of the time, and T, 20% of the time. There's less information there than if you did this 100 times. So if you did this 100 times, you might be able to distinguish between this really being 80%, 20%, but really this being like 99% and this being an occasional error that you get. So this SN factor that you add here is something that allows you to account for that when creating the position-specific scoring matrix. The second thing that you do is you divide by the nucleotide frequencies in the genome. So a particular nucleotide, it will be surprising relative to what you would normally expect in the genome. If you're working in a genome that's very, say, AT rich, and you see an A or a T in your motif, that will be a lot less surprising than seeing a CRG. So a CRG would supply you with more information. And the final thing that we do, so we take the F, so the PFM result, we add this sample size factor, we divide that by the nucleotide frequency correction, and then we take the log of all of this. And the reason we do that is because it makes all of the math a lot easier. So you can transform what essentially this model represents is a probability, and we have to multiply probabilities together for each column, but if we can, instead of doing that, we can take the log of each probability, and then we can add them. So computers are a lot better at adding than multiplication. This may seem surprising, but it is still true. Even in 2015 they're better at adding, and it is a lot faster to add a bunch of numbers than multiply a bunch of numbers, and you'll get fewer other errors involved. So when we do this, we end up with a PSSM like this, so this is all log transformed. And you can see, for example, this first column where it's 5 for A, there's a positive number 1.6 for A, and you get a negative score for any other symbol, and so on. So if you want to score TGCTG, you would just look at the appropriate cell. So for T column 1, it's minus 1.7, G column 2 is 1.0, and so on. You just add up all of those numbers together, and then you will get a probability that this matches the particular motif here, or really a score. It's not really a probability because it's been log transformed, and so this score is 0.9. So how can we apply this to the genome? I'll show you a quick example here. Here we're looking at the, yeah, please. Yeah, so there's some typographical issues. Can I erase something here? So I was just asked to clarify what this actually is. So it's the PFM, the score from the PFM, and you add the sample size correction. You divide all of that by the base probability of the nucleotide in question, and then you take the log of all of it. So I'm sorry about the way this showed up. This always happens in the PC MAC conversion. Okay, so now that we have, so let's take the SP1 transcription factor as an example. So here's a sequence logo, all right? So when I said the PFM is, the sequence logo is a PFM, often the sequence logo is more likely to represent a transformed PWM or PSSM instead. That's usually what you'll find. So this is the SP1 motif from the Jasper database, which is a database of transcription factor binding motifs. Here's what the sequence logo looks like. Here's what the actual numbers on the matrix look like. Okay, so you can see this has 10 columns, just like this guy. And we can score any particular sequence in the genome in the same way as I showed you in the last slide. So we want to score this position, sorry, a motif starting here. GGGGGGGGCGGT, we just look for the appropriate cells within the matrix. This, this, this, this, this, this, this, so on. And we add them all up, okay?  So this is a raw score, so it scores 13. Does anyone know what that means? I don't know what it, I don't know what 13 means. So it's important to take any sort of raw score like this and to convert it into something that, that you can both use more intuitively yourself and also compare it against other motifs. So we will convert this to a relative score. And the way we'll do that is we'll find the maximum score that you could get from this particular matrix, all right? So if everything hit on, we have the maximum score in every column, we get 15.2. And the minimum score, if we got the worst possible score in every column, it would be minus 10.3, all right? Then we can create a relative score, which is simply our raw score from before, minus the minimum score, divided by the maximum score minus the minimum score. So this is a relative measure of how close your motif is to the best possible motif for this transcription factor. So in this case, it's 93%, which is, which is not bad. The other thing we can do is we can convert this to a p-value by taking this relative score and we can also come up with a, a frequency map for every other possible match to this motif, all right? And we can see how much of the area to the right of where this relative score is compares to the area under the entire curve, all right? And that's what will give us a p-value for this. And I imagine that this has a okay p-value that might not stand up too well after, after you do some multiple hypothesis testing correction. There are any questions about this? So, you know, much of it in the next part will kind of rely on this. So I mentioned Jasper. So Jasper is a database that you can use to get lots and lots of transcription factor, finding profiles. So it's, you can go to Jasper.genered.net. It's got lots of sequence logos, matrices to download, all sorts of other stuff like that. The data comes either from, data comes from published papers mostly, and they got their data usually through in vitro methods like CLEX or protein binding microarrays, although sometimes there are motifs that are learned from chip seek data. All right, so we have this, this model for looking at transcription factor binding motifs. So how does it work? The good news is there have been a number of tests that have shown that this works really well. So some trench and colleagues tested 50 predicted transcription factor binding sites with an in vitro test found that 96% of them were bound. And Gary Stormo found that the sort of information theory approach to PWMs that I told you about where you treat each column as, by looking at how much surprise you get out of it actually correlates very well with this sort of binding energy that you would expect from physical chemistry models. So these models should work really well. The bad news is that other people have found that these methods, a method based on position specific scoring matrices would predict transcription factor, every 500 base pairs in human DNA sequence. And here's the ugly, if you look at, say, the afton gene with a bunch of profiles, you will find a lot of binding sites. So the whole gene will be covered by transcription factor binding sites, certainly not just the promoter. There's a question of how meaningful this actually is, and that brings us to the so-called futility conjecture, which is that these transcription factor binding site predictions from these methods across the whole genome are almost always wrong, which is a bit disappointing. But we have methods that will help us with that, yes. The models typically are tested against in vitro. So in vitro, you can get a little bit of enforcings to find the one or another that you sometimes see. So the question is whether these, are you asking whether the data, the validation comes from vitro studies? Well, essentially what we're showing here, what I showed you here is that the validation works very well in vitro studies. And you can assume that they did appropriate negative controls, right? Then any sort of force. I wouldn't assume that's my point. Okay, well, you can read the paper and then you can make your own judgment. But I think the problem here is not due to a bad laboratory technique. It's due to the fact that there is a lot of stuff that is not accounted for by an in vitro assay. So the sort of complexity I was telling you about before, that's going to be the main problem here, okay? So our method gives us a score, right? And one of the first things people thought to do when they, one of the first things people thought to do when they encounter this is, let's just increase the threshold which we use to decide whether something is a real transcription factor, binding site or not, okay? As it turns out, that doesn't help very much, okay? So you can increase the threshold. The factors that are causing this model to fail in vivo are not related to inherent properties of this model. So you can't just increase the threshold that you use and expect a better result, okay? The problem is that the binding sites, again, are defined by some of that complexity I talked to you about earlier. The fact that the genome is in a three dimensional structure. The genome is also wrapped around itself in a lower level chromatin structure. And there are only going to be certain regions where transcription factors can bind. So we'll talk a little more later about ways to get around this. So what did we learn in the first, in the second part? So position specific scoring matrices can reflect in vitro binding properties of transcription factors, but it doesn't work so well in vivo. So we have to add additional information to overcome that, which we'll go over in a later section. Any other questions on this part? So before we go on and talk about that, I want to discuss how the transcription factor binding sites, where these models actually come from, all right? So in the beginning, I showed you a list of aligned, I think they were 9-mers, right, 21 9-mers. And it was a really simple matter to get a column score for every column within that group of 21 9-mers and turn that into a PFM.  So if we want to discover motifs in the first place, we have to figure out which sub-sequences within the potential transcription factor binding sites are actually responsible for binding, okay? So this is called the motif discovery problem. Given a number of sequences that you know or you have information that they are bound by some transcription factor, really this is a more generalizable problem. So you can use this for proteins as well. Set of sequences that interact with some other sequence recognition factor. You want to find a motif, which is a set of sub-sequences that is an in common modular that degenerate nature I've told you about before. So you want to find how many motifs there are. You want to find the width of each motif and you want to find the locations of these motifs. So this is a surprisingly difficult problem because the input sequences can be really long. And again, the motif, if you were just looking for short exact matches between all of these sequences, it would be relatively easy. But the fact is you have to allow all of that nuance that you have in a position specific scoring matrix or sequence Lego, right? Things can maybe be only slightly similar, right? So here I'll give you an example of how you might run into this problem in the lab. So let's say we're given a set of promoters from co-regulated genes.  This was identified by RNA-seq and a bunch of different conditions. You find that these guys are always correlated in their gene expression. When one is on, the other five are on and vice versa. So we know or rather we suspect that there's some transcription factor that is responsible for this behavior and we want to figure out what it is. So it binds to positions that are unknown to us and they could be on either DNA strand. So let's say we know or we're trying to look at this with hindsight and understand the problem. We don't know this from the start. But let's say that the binding sites are here, here, here, here, and here. One of the additional complexities is that it might be on the plus strand, in which case it's A, A, G, T, C, A or it might be on the minus strand where you have something similar that runs in reverse, right? So this would be the reverse complement of this, A, A, G, T, G, A, G, T, C, A, right? So we'll assume that the binding motif can be described by a position-specific scoring matrix. So I usually say position-weight matrix. It makes it less likely I will trip over my own voice. So our problem is simply, not very simple, but it's to discover the motifs and the sites just given a sequence. Not any of the other information that I showed you before. So generally, what we will use is, so it's really hard to do this exhaustively. There's not enough compute power to try out every possible position-weight matrix that you could think of. So we use an alternating approach. And this is something that is often done within machine learning or other sorts of predictive modeling. So what we will do is we will start by simply guessing an initial-weight matrix, believe it or not. So we will have some sort of algorithm that we use to make an educated guess. But essentially, we'll start with what is a guess. And then we will use that guess-weight matrix to predict individual instances of the binding site. And then we will use what has been predicted there, maybe with some dropping out, as I'll show you in a minute. So predicting new weight matrix, and then we'll repeat the process over and over again. So by this, you essentially start with some guess and then you refine it and you make it better until you can't really make it better any further using this method. And then you can set this up so that the quality of the matrix always increases through one run through an alternating approach. The problem is you always have to worry about getting stuck in some sort of local minimum. So it's impossible to find the global maximum. And I should say you don't want to get stuck in a local maximum, right? But you can find, from some particular starting point, you can find something close to the local maximum. Any questions? Yes? Is this based on the knowledge motifs in Forehand or no knowledge whatsoever? Question is, is this based on knowing motifs beforehand? No, this assumes that you know nothing except for the sequences. So you're going to discover this totally de novo. The guess does not come from previous information like that. So I'll show you one particular way of doing this to make this a little more concrete and understandable. So let's say we want to discover a motif in these five sequences. We'll start. Here's the random guesswork. We don't just make the PWM totally out of thin air. It will be better to pick a number of sites and then take whatever and those we will pick randomly and to identify the sequences at these sites and then to create a position weight matrix from that. And then what we do from that, we'll define our technique more specifically. I'm going to tell you about one very simple technique called the Gibbs sampler where in steps two and three, instead of just refining it straight on, what we'll do is we will drop out one of the particular sequences and define the position weight matrix based on the remaining sequences. And then we will pick a new sequence based on the probability distribution from the previous step. So this is something that should reduce problems from overfitting because you're throwing out a little of your data in each step and you're kind of repeating the process. So you'll probably go through this in a cycle, eliminating each one of the sequences in turn and learning from the rest. I think all, this is a diagram that shows how this works in a little more detail, which will be in your notes and I won't go into it in much further detail. So a variety of other techniques like Gibbs sampler, the most commonly used tool for this sort of problem is called Meme, which uses something called expectation maximization instead, which is a little more difficult to explain, but you can think of it as conceptually similar. So at the end of this, you will end up with a position weight matrix. And then question is, what do you do with this position weight matrix? So here's where the data from the database or previous understanding motif can come in. So we've discovered this query motif to Nevo and we can use a technique called a tool called TomTom to search for matches to this motif and a motif database like Jasper and then it will give you this nice little report showing us that our query motif here is a really good match for motif 795 and Jasper, which is also called the optimal motif. So that's one way to go about figuring out what is responsible for a coordinated transcription regulation instead of genes. So, yes, question. So in reality, I usually do things both ways. So sorry, I should probably repeat the question for the recorder. So the question is, why would you discover this DeNovo and then compare your DeNovo model against the database? Why not just search using the models that are already in the database? So, yeah, like I said, I would normally do this both ways. The problem with just using what's in the database is you will bias yourself towards what's already known, right? So if there's something new, you won't discover it. In actuality, how many are in this database? So the database has along the orders of hundreds of transcription factors. And in humans, they're estimated to be 1400 to 1900 transcription factors. So there are a lot that are still missing from these sorts of databases. Unfortunately, I know people have various people working on ways to exhaustively get motifs for all the transcription factors that we can get them for. But some of them are a little more difficult to get than others. So before I go on further, are there any other questions? So I'm gonna talk here a little bit more about how to go beyond looking for sort of insight you can get from understanding sets of transcription factor binding sites. And this is sort of the first thing that we'll talk about. It gets around the futility conjecture I talked about earlier, right? Which is that we can use more information than just whether particular binding sites occur or not, okay? So we'll reintroduce this problem. Let's say we have a series of microarray experiments and we can get some sets of co-regulated genes and also some negative controls, all right? So here's a set of co-regulated genes, co-expressed genes and some negative controls. And if we look for a particular transcription factor motif here indicated by the sequence logo or position specific scoring matrix, we can find the examples of that in the co-expressed genes. And maybe we won't find it much in the negative controls, all right? So essentially what we want to do is we want to see whether there is any enrichment for a particular motif within the co-expressed genes that is missing from the negative controls, okay? And we can do this with a very similar technique to what we would use for finding go term enrichment, okay? There are a couple of different ways that we can look for this. One is we can look for in the positive set, foreground set, we can look for more genes that have the transcription factor binding site for a particular transcription factor than the background genes. Or we can look for more total sites. This is often a better approach because as I mentioned, you'll have the transcription factor will, binding site will often occur all throughout the genome, right? So even in a set of supposed negative controls, you're still going to have binding motif instances for any particular transcription factor. But what we're looking for is a concentration or enrichment of these binding sites at, say, the promoter of some gene or enhancers connected to it, and that will give us additional information that we can use to further that transcription factor is important in that gene's regulation. So these two different modes for understanding whether there's an enrichment of transcription factor binding site, you know, one is to use Fisher's exact test. So that's the looking at the number of genes, so I showed you on the left side. The other method is the binomial test, which is looking at the number of occurrences. And thankfully, there are, you don't have to, say, re-implement this yourself in R. There are other tools that exist to do this for you. So there is a website called opossum, which will allow you to take a set of co-expressed genes. You can feed it in a gene list. You can feed it in a set of sequences. If you feed it in a gene list, it will retrieve sequences from ensemble. It will optionally narrow down the sequences you look at to only those regions that are well-conserved evolutionarily between this and some related species. So you might look for sites that are conserved between human mouse, between mouse and rat, human and dog, or something like that. I'll mention, I'll describe this a little more in a second. Then it will detect transcription factor binding sites using the same sort of scoring methodology we talked about earlier. Come with this method, a description of the statistical significance, and then you'll get some putative transcription factors. One thing I want to talk about real quickly is this idea of phylogenetic profiling. So around when we were collecting lots of genomes of different organisms, this was really in vogue. And this was seen to be the best way out of the futility conjecture, which is just look for those regions that are really well-conserved. If evolution has conserved them, they probably confer some selective advantage. And they are probably quote unquote real. I think this has fallen a little out of favor in the intervening years. Now that we've started to develop the sort of, say, chip-seek data and all of those other data sets that I told you about at the beginning of this lecture. Because what we revealed is that that really makes things way too stringent. Is there a lot of real binding sites that you cannot identify with methods like phylogenetic footprinting? So I would be cautious in its use. And personally, I never use phylogenetic footprinting. Looking for evolutionary conservation is something I would do afterwards when I was attempting to describe the implications of particular sites. And not something I would use as a pre-filter. All right, so let's look at a set of results we could get from running opossum. So here we have a set of genes that, so we have the microwave experiment I showed you before. And there are some muscle-specific genes and some liver-specific genes. And we identified these various transcription factors in these particular gene sets. So SRF, phylogenomial test, Z-score, has a really good score, so does MEF2, so does C-Mib, so does MIF, et cetera. As it turns out, especially these guys with AM at the beginning of their names are known to be muscle-specific. So this shows you that you can identify valid transcription factor enrichment within these sorts of data sets, and you can do the same sort of thing with these liver-specific genes. These are known liver-specific genes. And the other ones may or may not actually exist there. So you also see some amount of false positives. These have not been experimentally verified. So in general, this is something that will give you some information that you would use to plan future studies.  Binding sites, you might want to confirm that further, potentially with chromatinone precipitation or some other method. So think of it as something that gives you additional understanding but is not necessarily the final word due to those futility problems I told you about before. So Apossum said you can find it on the web. I think there are links from the wiki. There are a variety of different options here if you want to try this. I would start in the first row, and you can pick your species here. The species are just if you have a gene list, it makes it really easy for you to just put in the gene names there. If your species is not one of these fives, you can use a sequence-based version instead and just supply the FASTA files for your putative promoter regions or whatever, which you can extract from Galaxy or from somewhere else. Are there any questions about this part? Yes? Yeah, so the question is how well would this work for a single gene rather than a set of corregulated genes? So you can identify putative binding sites and say the promoter of a single gene that most of them, back to that futility conjecture again, most of them will not necessarily be meaningful. So in this method, we've relied on statistical power we get from enriching over many different data points. That's what doing this sort of analysis gets us, so you have much more confidence. Within a particular gene, you can use, so you wouldn't use this, you would use something called FEMA, which is part of ImmuneSuite, and we'll use FEMA in the lab, but you would want to be really, really cautious, much more cautious about interpreting results from a single gene. Better for a single gene if you're working in an organism where there is good epigenomic data available, like chip-seq data or DNA-seq data, is to use that data and use that to interpret what's going on in your favorite gene. And you can also sort of combine those things. So if you know that say there's a DNA hypersensitive region upstream of your gene, you might want to focus on motif search just on those, and that would be another thing that would increase the likelihood of it hit. Just one more question. Yeah. Does it allow for tissue specificity to come in a useful way to restrict to relevance for searching factors, to not express in your tissues, say deliver specific work with muscles in the tissue? It would be cool to allow the ones that are in the wrong tissue. Yeah, so the question is whether databases like Jasper include information known about cell type specificity of motifs. It's not there in a structured format. So usually if you go to Jasper, there will be a, if you're looking at one transcription factor, there will be a series of, besides the PWM, there will be links to the papers where it was described, the papers and that will tell you about it. There will also be links to the gene identifiers for the gene that expresses that transcription factor protein. And from there, you could infer this sort of thing. But again, I think it's personally, I think it's a better approach to do something like this without sort of biasing yourself towards the expression in one particular transcription factor. And then use that sort of information post-hoc to understand what's going on, then to use it as a pre-filter because you're going to miss things that way. And then to use it as a pre-filter because you're going to miss things that way. And an important thing to realize is that there's a lot we don't understand. So say we have some particular transcription factor that so far has only been seen expressed in liver. That doesn't mean that if you're working on some sort of muscle cancer that it won't show up there that it's invalid. So it's important not to reinforce assumptions that are made by the happenstance of where we've identified things so far. Yes. Meaningfulness, so you mentioned two database, one gene, are we going to look into that link? I'm going to talk about it a little more. So there are links at the beginning of the lecture which you should have a copy of to the ENCODE project and roadmap of genome epigenomes project. And they also have excellent tutorial publications online. There's a lot of data in there so it's not necessarily sometimes so easy it's just clicking on different things if you don't know what you're doing yet but they do have documentation and tutorials that makes it easier to understand that stuff. Yeah I could probably talk about those particular websites for two hours or so and I have in the past although they're not here in CPW. So let's talk about one other wrinkle here which is that we'll often have families of transcription factors with transcription factor binding sites that are indistinguishable. So here for example we have the ETS family. There are a variety of different ETS proteins and they all seem to bind to very similar DNA sequences. So how do you pick which one? If you're in this particular set of circumstances and then there are software called top gene which can be used to understand this particular problem further but most of the time this isn't something that you will run into and things like as just mentioned on the slide zinc finger proteins even though they're all in the zinc family they have a broad diversity and the motifs that they recognize. So any questions on this part of the lecture before I go further? All right so now I'm going to in this final part talk about some other techniques that you can use to bring in some of those other data sets and to understand how transcription factors influence gene regulation and maybe not necessarily right at a transcription start site. So one thing that I'll mention here is a technique called Segway. Segway is software that has come out of my lab and it allows you to integrate lots of different data sets, trip seek data sets and boil them down to a simple annotation of a genome at every base on a genome. So Segway will say take the human genome and it will assign some sort of activity to every part of the human genome and some cell type. Okay so they'll say this is a promoter, this is a transcribed gene, this is an enhancer, this is a repressed region, this is regulatory, this is heterochromatin and so on. And so if you're looking at say encode data using something like Segway, if you're using human encode you can get to Segway right from any major genome browser. So it's built into the UCSC and Ensembl Ensembl genome browsers, you can just turn on the right track and then it will tell you generally what's going on in a particular region and you can use that to narrow down the regions that you're interested in and analyzing further. So you limit yourself more to a transcription region rather than a say repressive region. There's another really cool tool which is called GREAT, which you can use if you've had some experiments say that it's giving you a number of sequences or regions that aren't necessarily correlated to a gene. So GREAT solves the problem, if you have a gene list it's relatively easy to do go enrichment or similar sorts of functional enrichment. If you don't have a gene list, if you instead have a set of sequences or you have a set of regions in the genome which may occur within energetic regions you can use something like GREAT instead. So it'll take input a bed file with all of those regions and it'll give you output that looks like this and if you drill down further it will tell you say which pathways seem to be enriched within the particular regions you talked about. I rather you input it. So most of the way this works is by taking those regions and associating them for example with the venerous gene. So it does do something similar but it solves a lot of problems and has appropriate statistical treatment that you would not necessarily get if you decided to say write a script that just found the venerous gene for any particular set of regions. So most of what I've talked about up until now has been looking at particular single transcription factors but often transcription factors work in concert so they will cooperate with each other and you will often find two transcription factors so they're often found in a pattern together. This is another sort of thing that gets around the futility conjecture. You see additional information and supplies from having two transcription factors that seem to be really close to each other and there's a tool called SPAMO which is part of the MIMS suite that allows you to do sort of dual transcription factor analysis. You can use it to find whether there are two transcription factors that are near each other that are spaced in a very particular way. So finding these two sites that are always say, almost always eight or nine base pairs away from each other will be a release and you can see something like that in these histograms here. We very strong signal that this is a real interaction. So there are a lot of challenges in this field of understanding transcription factor binding and making predictive models of transcription factor binding had. So first is the holes in these databases. Like I said, there are maybe 1,900 transcription factors. So first people aren't even really sure that number, right? So I usually say between 1,400 and 1,900 transcription factors. We haven't validated all of them. We certainly, we don't have in vitro data on what all of them bind to. We don't have in vivo chip seek data of how that actually plays out in reality. So that will be a big challenge that, I was hoping would be solved, more solved by now that these things, getting the last bit of a project like this can actually be a little stickier. Understanding how genetic variation and transcription factor binding sites affects how those binding sites are effective. I think will be a really important challenge in the years ahead. A lot of the people are doing, say, human genetics. They've focused a lot on exome sequencing, which often ignores the variation that you would find in other non-exonic regions that affect gene regulation. And it's likely a lot of these are important in defining various phenotypes or diseases. Integrating different data sources, such as chip seek data, will be a, or DNA methylation data, will be an important challenge as we go ahead. And finally, these models that I showed you, these position weight matrices are really convenient, but even at the in vitro level, even if you're working with naked DNA, they're probably better models that we could use. So people are thinking out shifting to energy models. There's work in understanding how changing the DNA sequence actually affects the shape of the DNA helix. So not just where the base, what bases are sticking out to the minor groove, but actually different series of bases will cause torsion on the DNA double helix in different ways or cause twisting in various ways. And people are starting to understand that more and more. But mainly, as somewhat of a biased person, I think a lot of this complexity is really coming from, I say I'm biased because this is what I study in my lab, the interactions between lots of different factors and lots of different things that can be measured in the epigenome. And we're going to need to understand how all of these things tie together a lot better to make really good predictive models or to improve them further.  So first is we have models for transcription factor, binding motif discovery and analysis, which work really well in vitro, but in vivo, finding one of these sites with these methods alone has very little relationship to some sort of vivo function. But using additional methods on top of that, such as looking for clusters of transcription factor binding sites can be helpful. Conservation can be very helpful, although I'll also give you some amount of false negatives and looking at interactions with epigenomic data can also be very helpful. And I think that will be a big work there in the future.


Earlier this week, Beltrami County Sheriff Phil Hodap announced his retirement later this year, at the conclusion of his third term as sheriff. Hodap has been instrumental in reducing crime and improving safety on area roads over the past 11 years. During his tenure as sheriff, Hodap has worked to pilot alternatives to incarceration of mentally ill individuals. He hopes to continue that work even after his retirement. I've got people in the sheriff's office here that are going to step into my shoes to help finish the work that's being done there, but I want to keep working on that project possibly even after I'm gone. To help continue his vision, Sheriff Hodap endorsed Beltrami County Chief Deputy Ernie Beidle who announced his candidacy for sheriff earlier today. Beidle says he was honored by the endorsement and if elected hopes to continue the work, Sheriff Hodap started by focusing on key areas. Being more efficient within the sheriff's office, continuing fine efficiencies, the mental health crisis and we really need to continue to throw more of our efforts at that. We need to get the drugs off the street. Beidle is the only person so far who has entered the race to succeed Hodap as sheriff. The election is set for next November. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


You, whose day it is, get out your rainbow colors so that it may be beautiful. You, whose day it is, get out your rainbow colors so that it may be beautiful. You, whose day it is, get out your rainbow colors so that it may be beautiful. whose day it is. Get out your rainbow colors so that it may be beautiful. There's no long time ago there was no Columbia River. If you believe science and talk about the Missoula flood, for a glacier melted above Missoula, Montana, from the rivers and lakes of the Northwest. But that science, want to know what does Indians say? Coyote took a big stick to the top of the mountains and dug insane. A powerful song. If you don't know, our culture is not a spectator sport. Laska come to a powwow by our beadwork, by our fry, but eat it, it's really fattening and tastes good. We haven't asked you to dance, so everyone get you a stick out and help me say it. Everyone get you a stick out. Coyote took a big stick to the mountains and dug insane. A powerful song, help me say it. Coyote took a big stick to the top of the mountains and dug insane. A powerful song, a little bit louder. Coyote took a big stick to the top of the mountains and dug insane. A powerful song. Coyote took a big stick to the top of the mountains and dug insane. A powerful song. Coyote took a big stick to the top of the mountains and dug insane. A powerful song. Coyote took a big stick to the mountains and dug, and sang a powerful song. Coyote took a big stick to the mountains and dug, and sang a powerful song. And the water broke through, forming what Indians call enchijuana, the big river, which most people refer to as the Columbia River. Coyote was proud of himself. He made enchijuana. He was walking up and down the banks of enchijuana to see what he had made. The animal people moved down to the riverbanks to be closer to the water. Coyote saw they didn't have that much to eat, so Coyote walked clear to the ocean. And he saw many salmon swimming in the ocean and used his power, told the salmon, to swim upstream. That's how Coyote made enchijuana. And that's why the salmon swim upstream. And that's what called the legend. Thank you very much.


tomorrow and they are travelling down to Dingle. There's no big deal in travelling down to Dingle this journey I've made a few times in the last year. But the thing is, they're doing it on horseback. And they're raising money for the Ronald McDonald house and the Keynes kennels. The two are Shane Barry and John Doherty. John just out the road and cool boy and Shane from Slaggo and they're doing it in memory of baby Aria Rose, daughter of Shane and Tracy. So gentlemen, you're both welcome to the show. Thanks for coming in for a wee chat. Thanks for having us. It reminds me that is it the rubber band? It's in the horse outside. There's no horse outside. No horse outside today. Tell me about the background. Tell me about Aria Rose. Well, Aria was born with some complications and a severe syndrome. She had six holes in her heart, polymagic gyro, hydrocephalus. But basically we spent 16 months in hospital all our life and most of them spent 16 months we spent in Ronald McDonald house which was it was a great place for us because it was somewhere for us to go like yeah yeah and that's the house that's the house for families of people who need long or children who need yeah in Cromland okay and so there you were for the first 16 months you and Tracy and the dog two dogs that we have Yogi and Francie Keans Kennells are another part of Cromland that have just recently got going so I think we were one of the first people that they kept long-term usually a short-term say it stays to bring the dogs up to visit the children and they come to your house collect the dog bring it to Dublin DSPCA check it out make sure everything's alright and then to take it over to the hospital and they can spend the day with the kids and then they'll take it home again for you so they provide a great service and this case it meant that you guys were able to be near your dogs and take that out for a walk yeah we got to go out it was once a week twice a week sometimes we got to go we could go anytime we wanted but with things in the hospital it was usually once or twice a week we got to go and get out at the hospital for a couple hours take the dogs for a walk and just meant we weren't relying because the neighbors are great at home but it wasn't fair on them to be relying on them for 16 months looking after them so Aria Rose's life was short but you wanted to try to you know give something back hence raising the money for Ronald McDonald House and Keans Candles and doing it in an unusual way and well how does John come into the picture here John's just a good friend of ours and we were I think it came about maybe last January or February we're out together and we said we'd like to do something different so I came up with this yeah and I thought at the time I thought I know it sounds like it sounds okay I didn't really think it would get this far you know a couple of fellas are chatting like you know love to do something and she and kept coming back and kept coming back and I got stronger and there was more things being organized and more thing and I was like this is happening and you know it's it's a big undertaking but the thing that I keep saying is every time you say it to someone they said oh that this can't be done early and it brings it back to Aria Rose Aria Rose proved the doctor's wrong time and time again that she wouldn't know she wouldn't make another week or another day so what she and come up with is something that's it seems unachievable but what's the plan and then all the organization is going to happen it's going to happen you know started as an idea has now become a reality yeah so you're you're heading off tomorrow on horseback you know to do a to do a run or a cycle or whatever or a motorbike rides you know relatively straightforward and comparison having to take two horses all the way yeah I mean that that entails for a start they're they're overnight yeah where are they we have yeah we have all accommodation sorted the whole way yeah accommodation and rest days for us and for horses and relief horses and stuff like that just kind of things that makes it easier because we're not athletes we're we're we're just too ordinary Joe Soaks is gonna you know around the road and like Shane's an experienced horseman I'm just relatively new this in the last couple of years so it's a it's a massive thing for me to do you know but horses are unbelievable you know and if you ever get a chance you should you should give it a go yeah well I'll do another note but I certainly don't take one to Dingle no so you mentioned their replacement horses so it's not gonna is it gonna be the same two horses or that will be the two horses starting maybe finish yeah we have we have four horses so yeah we want to probably the horses to start they'll be there to finish you know so kind of give them a break along way and and even like it's all I presume it's all mapped out now and places the stage you mentioned there and it's mostly back roads is it yeah mostly back by roads and not there's no more ways and we'll have to go the main road the whole way to go with it and then we're going down along the coast true clear into Curry and on to Dingle why Dingle well it's just usually we go to Dingle every year for the horse racing and it was just I don't know I just came up with an idea don't you go to Dingle yeah one end of the road yeah yeah and and what about saddle horse well huh I bought myself a pair of cycling shorts of a bit of extra you might need to pair yeah we have a contingency yeah we have a we have a team behind us yeah we couldn't really do it without them we have Tracy is going to be she's our support support yeah and without that without without Tracy doing that we couldn't do this like we if we didn't have people supporting us and along the way at least you know you're not on your own you know you have that kind of backup if you need it you know and and you do need the support I mean it was just something simple as you know getting the horses fed I'll get them yeah getting them watered and yeah at night whenever we're putting everything away like I just can't just jump off the horse and head into the pub as I would like to you know and where will the horses be overnight and have you managed to source places where the horses yeah well she and she very resourceful when it comes to that kind of things very resourceful right with a lot of contacts yeah a lot of car isn't the horses for years so like throughout the country there's not a lot of I'm kind of shocked at kind of the way this has come together because I'm like there's no way but yeah not it's pulled out to stop some we've kind of got a lot of and there's a lot of places when to stop along the way to you know and meet up with certain people you know horse groups and stuff and you know so it's not gonna be a gallop the whole way then oh definitely not not there'll be a few water and holes to stop on how long how long are you expecting to take we're looking at two weeks and that's including the rest days and we'll have checks along the way we have Keynes Kennins has a vet Emma Plant she's from Longford she's going to tie in with us along the way and we have our blacksmith David Timlin and he has other blacksmiths throughout the country contacted so if we need shoes or anything that he'll he'll be there on hand to get us out and yeah because it's a lot of miles for them so who's you know you them things do you have to be checked the horses is the number one priority like we we can we can manage it so we know when we're not right they'll be they'll be better after maybe yeah yeah I can guarantee it yeah yeah and you head off what time tomorrow we're leaving Swinney Spire tomorrow there from 12 get yourself set up there's gonna be face painting for the kids and they can meet the horses and all of the all of the above you know so hopefully the weather is as good as today I'd be brilliant yeah yeah hopefully it'll hold out because weather's been so bad yeah this last month so you'd be hoping that you know to get better yeah yeah we put this way we've stocked up in the wet gear and could well be needed yeah and all the way I've either you'd ever done for something like this you know like maybe in the form of a cycle or nothing really though but I don't have marathon there a few weeks ago as my big it's the longest kind of thing I've ever done you know and I think that was manageable and comparison and comparison to this you know yeah yeah well you know there's been a long lead in and I'm sure a lot of talking a lot of planning so and now you know now that it's come to be and it's happening tomorrow how do you feel? I haven't really talked about that but my feelings I've asked today to get definitely getting excited both you know also your concerns you're making sure everything you have everything with you I have dogs at home and getting them locked after and you know you're not gonna be there so and plus I have my own business so making sure everything's okay there before you go and but now when it comes to the Reddit excitement more than anything you know what that's what yours yeah excited now yeah I suppose there's a lot of planning and even as John bought myself John work full-time so we had to have we have other help on the side without them we couldn't have achieved this because they were helping to exercise horses all the time and looking after them so and all in memory if your little daughter so how could I can people support this is there is there a go for me page or a website right we have an I donate page goes directly to Ronald McDonald Key and Kenan's it's I donate areas don't he go to Dingle Trek and we also have buckets we've buckets along the way likes the Sweeney's and we'll have buckets collecting as well and that's all it's on so on Facebook as well area roses areas adventures at areas adventures and it's on all of our shame Barry's Facebook page and my page and Tracy's page well so there's plenty of places they can look up on Facebook and I think just click in the link and they can get there and actually it's a full-back story on area rose and their journey as well so it's nice we read up on that as well so it starts tomorrow morning and you head off from Sweeney's in Balirain here now to Kenny yeah whatever the weather's like whatever you go we go heading down to Dingle yeah all right well listen I wish you both the very best and and hope it all goes goes to plan and hope you raise a lot of money along the way thanks for coming in for me thanks for having us a brilliant


Like, you know, I'm just on the swear.  How busy it is? There's no way, if that means all these people have a season pass. Yeah, it's crazy. This is the busiest season pass I've seen. Ever. I've only been there maybe two or three, but this has been the worst one. Wow, not the worst one. It's crazy. I'm like, I thought he was... For a game? It's a line for a game. It's not like a sickening, it's just like a muscle pain. Yeah. You just want to be careful with your appendix. I could be wrong. Comment in the comment section. Perfect, perfect video content. Yeah, lower right. I thought gummy worms in the tree. Indeed it is, yeah. You can eat, but there's more indication that people around here need stuff to do outside. I've seen the park like this though. I know, it's beautiful. This isn't how we hung. That speaker just scared me. It's just like fast lane. Line for whatever this is. This is where all the money is. Really? Chasing after Surya. Expansion. Here's the key line too. Oh my God, this is for the bumper cars. Think about it, they already have those stands. It's a beaver. It's a muskrat or something. Something left in there. We kept this one closed. Look how in pieces it is. Even if you were to opening day. People waiting in the line. Games. Yeah.