What's up everybody Jason for jazzy's case breaks calm. We just sold out 2023 top stadium club baseball hobby a box half case break PYT numero uno guys, and it is from a fresh case fresh case So One two three for the left side. We'll use here four five six to the right side. We'll use here depending on what it lands on one two three four five six and It's a one So we're gonna go to the left side this here We'll be for the next kick your team Which like I said could technically do it if it sells out, but if not for sure with Joe at three o'clock He has wanted to do that prison one box. So that one's down to six.  I personally prefer that now that they make stadium club chrome more of a chrome guy but always some great photography and stadium club and you get two autographs in Stating club and usually pretty much always on card. So it's even better There's the oversize there for the Yankees. Some of these will be autographs too. So I have to pay attention a little bit And we'll rebuild in four boxes at a time too That way we can kind of Breeze through this Now sometimes it's kind of hard to catch the variations. I'm not sure if there's like a collation Try our best, of course to try to spot them, but everything does shift. So But if you guys in the chat, I've seen this spot something Let me know One more box to read and then we'll go through the cards guys. We go. Let's take a look here guys Well, we'll take the first box a little slow You know just to kind of see what we got here And then we'll kind of speed it up just a little bit. Don't hold their brain you read Some James Altman That babe Ruth card right there. It is crazy. I cut and there's our first autograph Tommy Henry For the Arizona Diamondbacks when he a Adley-Ruchman Michael Massey There's a chrome Ted Williams David Wright. Oh, right there. Oh, they can say go right there then Nice hair brand new me one. Oh, we got a redemption Going yard Juan Soto How about that one there for the Padres? Justin Martin. There you go, man. There's a little Adley. I don't remember if there's any guaranteed numbered cards per box I know sometimes see like you can tell right away if they're numbered or not, but the reds are common But I'll double-check them right now Mullen Gorman Cuz I feel like usually I always get at least one numbered card right per box you just get one chrome parallel one Trumb our trump irates or something like that would do So I don't think it's guaranteed to get a numbered card, but I'll double-check it because you can see it right away from the front but Just in case I'll flip them backwards and see if I see any stamps. I'll sleep up these rookies here. I thought this one would have been numbered But I guess not. All right, let's go to the next box Sexy-looking cars in here, especially the holes I can say go Justin McKenzie, there's that baby. Yeah, just common Jackie Robinson, that's pretty cool. You got a Henderson little Bryce Harper and Michael Massey is the first auto the second box Kansas City Royals going to Kev Yeah, going yard going yard That one's number it's a 199 George Brad. Look at that You ever seen true detective season one with him in it and Woody Harrelson. It's really good so it's not like Blasters where every pack it was like the middle cards were backwards. Yeah, nothing's been backwards yet That's I was like, I don't know if you know if I'm gonna catch any sharpers. They all look like they're short prints to me It's either I just soon the sharpener might be like a player. That's not an action like like the older vets I guess but I haven't seen one that Matt Walner For the Minnesota twins going to Robert Runkle That's Corbin Carroll Two boxes down guys not some fun stuff here guys. That's always great. What's for the photography? We'll love that stuff. I'd assume though guys all the older veteran stuff is usually the or the legendary stuff Like babes and stuff is like a non baseball one. So I'd assume Probably like see he's not playing baseball I'd assume like the variation if there's what it really means would be him in action rather than like him trying to sign an Autograph and then for the baseball players, it seems like all the baseball player photos have been in them in action I'd assume it'd be the opposite. Yeah So like look at this Yogi Barrow and Tyler Freeman for the Cleveland Guardians Gardeners is Robert Runkle. Like look at that. How's it go? Look at that hair. There's a Francis one door chrome history release Chuck E. Robinson Cincinnati Reds Shayna that one All right, last one here, and then we'll go to the next four. There's a blue. Whoa That one got eaten, bro. Look at that one cut off the cut off wrong out of the printer probably But Rafael Devers numbered to 50 for the Red Sox when a Karen sorry about that Karen Obviously out of our control, but we're gonna hobby and look at that Freddy Freeman Cromer factor. Let's say Standing Club Cromes don't look really nice this year drew waters For the Royals and a cap Griffey Chrome What I sanga So red Roundy and nice Joey Votto Shame with the Reds. All right, so far first Four boxes Joey Votto drew waters Chuggy Robinson Tyler Freeman Matt Walner Michael Massey Going yard redemption of Juan Soto and then Tommy Henry Let's go to the next one. Got a trade Turner box topper Gunner Henderson Salvador Paris No, that's how we're the first our last box here to reverse So again, I by the time I get done with this guy, so I'll have like 30 minutes guys You guys wanted to like try to potentially do that prison football has done a six. That'd be fun. I Can quickly do something like that. It's James Altman for the Dodgers You're gonna Richard Miller It's a good auto to start us off That's an orange wholesale 2-way to 1.99 almost missed it there, but it is a color match and I'm a shadow One more autograph here and Joey Manessi's for the nationals going to mark Nelson Riley Chrome our next box here guys nice Tautis Cromer Proctor. It's a freaking badass photo Get that autograph would be nice King Griff Shohei and Jordan Grosjeans for Miami, but a J Carnazio for Miami another one there for J Miami autograph box. All right two more boxes there guys and a Vladdie refractor Chrome Shohei and Ernie Banks Riley Green Chrome and Lenion Sosa for the Chicago White Sox when a Michael and Donnie Sands For Detroit's Yeah, two Jim Holmes All right last box here guys two more other guys hiding in here and Ezekiel Durand To start us off for the Texas Rangers Winner Richard Miller Orange Jordyn Alvarez there in the 1.99 for Houston Gonna Aaron Chrome of the Aguil And Logan will happy for the angels going to Gary All righty guys and there you go. So I'll top load all these autographs right now a little fun break You know, we didn't pull the biggest rookies per se, but some nice names Obviously that Juan Soto is one of the bigger ones. We've got James Alvin, which is nice as well. Joey Vado You know, so that second half could be pretty nice guys who knows I don't know how they really pack in this but again, that was just the first half of Stadium, Columbia team number one Jasper's case breaks.com guys. Thank you


Welcome to another edition of Haiti. Haiti is a series of videos I do where I respond to viewer questions and comments. These viewer questions and comments, they typically come from the comments on the videos posted on YouTube and Odyssey. Sometimes these questions and comments come through social media, such as Mastodon Reddit, sometimes through email. And the very first comment that I want to respond to comes from my reaction video to the Linus Tech Tips Linux gaming challenge. And this comment is simply, Linux software supported? That's not written on the box. So what this comment is in reference to is on my video I said that when you switch from Windows or Mac over to Linux, you need to make sure that your hardware is supported on Linux. And I'm talking about the hardware you purchased from that day forward. When you go buy a printer, make sure that the printer has a Linux driver, that it actually supports Linux. Because some printers do, some printers don't. If you buy a printer that doesn't have a Linux driver, it will never work on Linux. It will be just a dead paperweight at that point. You need to go take that printer back to the store and buy a different printer, one that actually supports Linux. And he's saying, well, there's nowhere on the box that says Linux support. Yes, there is. The problem is, as a Windows user, Windows users have gotten kind of lazy because almost everything works on Windows. I mean, Windows being the dominant desktop operating system, you typically don't have to worry about printers and keyboard and mouse, things like that, peripherals. Typically, they're always going to support Windows most of the time. That's not always true. I mean, you should still read the box. If you're a Windows user, just verify that it does work on Windows. But for the most part, you can get away with never looking at the box. But when you switch to a non-Windows operating system, such as Mac or Linux or whatever, you know, you actually do need to read the box on the hardware you buy, make sure that it actually supports Linux. And most printers will actually have this on the box. Well, you know, just as an example, I know anytime I pick up a HP printer or a Brothers printer, you actually look on the box, it'll tell you, you know, supports Windows, Mac, Linux, and it'll have the logos, even have the Tux logo on there. So there's no doubt when you pick that thing up, it will work on Linux. So the idea that this information is just impossible to find, like these hardware manufacturers don't put that on the box. Yes, they do. Maybe there's a few out there that don't. Maybe you'll have to do a 30 second Google search. Oh my God, that's going to take so long. But for the most part, you don't even have to do that. Most of the time, you can actually read on the box for all kinds of things. I mean, I buy external SSD drives and things like that. And many times they'll put a supports Linux on the back of those things, even though you just assume something like that is going to be supported, no matter what operating system you plug it into. Still, they take the time to actually put that stuff on the box, actually read the labels on the stuff you purchase. And the next question is, hey, DT, you never did a video on Cubes OS. Is it because you're not interested in it? If so, why is that? No, it's not that it's I'm not interested in it. I think Cubes is a fascinating operating system. The problem with recording a video about Cubes OS is Cubes OS requires a lot of system resources because it's essentially everything's run in virtual machines. That's basically how it works. The problem with Cubes OS is that Cubes OS can actually be installed inside a virtual machine itself. So I can install Cubes OS inside Virtual Box or Vert Manager and then record that virtual machine because Cubes OS just won't install inside a virtual machine itself, right? So I would have to install Cubes OS on physical hardware. That hardware would have to be a beefy machine to run Cubes OS properly. I only have one beefy machine, my main production machine. I can't install Cubes OS on that. And then how would I record Cubes OS on this machine? Well, I need a separate machine to actually record to stream everything to that machine would also have to be a very beefy machine, you know, to stream video from one computer to another and capture all of that. I don't have two computers that could do that. So that's why you've never seen a Cubes OS video from me. And that's probably why you don't see much Cubes OS content on YouTube is just because of the challenge of actually recording that thing is system resource requirements and the fact that it doesn't run properly in a virtual machine just makes it hard to capture it and get a video on it. The next question is, hey, DT, I get this error message with your DTOS script. DTOS is my Arch Linux post installation script that gets you my Xmonad desktop environment. And he says, it always says a error recompiling Xmonad. What exactly is the problem here? So the problem with my DTOS script is one thing that doesn't give good error messages, because anytime the script craps out at all, it fails at some point, it automatically skips to the last error message in the script, which is the error recompiling Xmonad error. So that doesn't really tell us exactly where it filled. But I can tell you 95 times out of 100 where the script actually fails is when it starts installing programs through Pac-Man. And I will actually give you a command on screen here. And what you need to do is run this single command CD into the DTOS directory that you cloned and run this one command here. And this is just the sudo pac-man install, the list of packages for DTOS command. And at some point, you're probably going to get an error. It's going to be a conflict, a package conflict. So if you're installing this on, you know, Endeavor or Garuda or whatever distribution Arch Linux chances are they have a package that conflicts with some of the packages I am installing. And if there's an error during that process, the script will fail. What you need to do is figure out the package that's causing the conflict and see if you can get around that in some way, maybe uninstall the conflicting package and then try to rerun the entire DTOS script again. The next question is, hey, DT, what do you do to protect your posture and general health when working at a computer or a desk all day? What do you recommend to us? Thanks. Okay, well, you have to understand that I don't work at a computer and a desk all day. I mean, I work at a computer, obviously, this is my job, you know, these days making YouTube videos. And it does require me sitting at a desk for a few hours a day, but it's not like an eight hour a day, 40 hour a week kind of job that some people do have that. So I'm not at a desk that kind of amount of time. Typically, the only time I'm really sitting at this desk is when I'm recording. And usually, when I'm recording a video, I'm kind of moving around and active, you know, because you know, doing hand gestures or whatever looking around. So my posture is pretty good when I'm actually recording a video where my posture gets kind of crazy is editing the video. So this video, for example, I may take a couple of hours to edit this video when I'm done. And when I'm sitting at a keyboard staring at a screen, you know, hunched over leaned over. Yeah, that's bad. What I like to do is take a break.  As far as general health and posture, you know, I can give you health tips. I can't really give you posture tips because I really I'm not that focused on it. I know when I start getting hunched over at a desk, I get up and move around a little bit or I just stop for the day. General health tips, though, good general health tip would be to actually be active.  You don't want it that's so sedentary, you know, just sitting at a computer that you're guaranteed to actually be overweight, out of shape and potentially be unhealthy. So, you know, walk 30 minutes a day and that really, you know, I would say sign up for a gym membership and actually really begin seriously working out. But honestly, if you go walk 30 minutes a day, you would be surprised how much better you feel and you'll probably actually lose a little weight. We're not talking about running. I'm not saying go jog. I'm saying walk for 30 minutes a day.  And I think you'll be amazed at how much better you feel. The next question, hey, DT, I feel Kate and live is the worst video editing software. Whenever I cut clips and bring them together, they create a screaming effect of audio, which I really hate. Okay, so I can't really comment on his particular bug. I've never experienced that. So I can't give you any tips on how to fix that. I will say Kate and live is very buggy, very flaky. Kate and life has a lot of problems. And I'm a big proponent of Kate and live, you guys know, I edit all of my videos on Kate and live because it's by far the best free and open source video editor we have. Nothing else comes close. I mean, I've tried shot cut and open shot and but TV and I live in flow bleed and probably have a dozen others. I've tried them all. They're all either really light as far as effects and plugins and features, you know, to the point where they're almost unusable or now scrubbing through the playhead is really bad. If you can't even scrub through the playhead smoothly and actually watch your video while you're editing it, then your video editor is almost useless at that point. And Kate and live does a lot of the really basic stuff. Well, the problem with Kate and live is a lot of the more advanced stuff, the effects and the compositions, the plugins, the color grading and all that stuff. It's really fantastic when it works. The problem I have these days is, you know, Kate and live sees a lot of new releases every couple of months. It seems like there's a new release of Kate and live and things that were working effects or compositions that were working for me that I had grown used to using all the time. All of a sudden they don't work on the next version. They're broken for whatever reason. I don't know. And then they'll fix it in the next version, but then some other things that I use on a regular basis will be broken. So it's a constant rotation. I never know what's going to be broken in the latest release of Kate and live. And of course, I'm on a rolling release distribution. So I'm always on the latest release of Kate and live.  A lot of people really preach how you use the app image for Kate and live because it seems to be more stable because it has all the dependencies. That's not necessarily the case with the app image either because the app image still has the same kinds of problems, you know, from release to release, where the new version will break things that were working in the previous version vice versa. And it's frustrating. It's one of those things that those of us in the free and open source community that want to use a free and open source video editor to produce content, many of those people talk bad about Kate and live and really frustrated about Kate and live. I've never really vented my frustrations of Kate and live because I do want to promote it because I think it's a great project. But I understand the frustration. It is really, really hard to not use a proprietary video editor just out of sake of convenience. All right, because on Linux, I mean, I could use DaVinci. It's available on Linux. I could use Lightworks. It's available on Linux, but for ideological reasons, as long as I can get my videos done with a free and open source editor, that's what I'm going to use. I'll put up with all the pain. I put up with the pain, the heartache, the frustration as long as I can get it done with free and open source software. But I understand most people are not like that. And if you're not like that, DaVinci and Lightworks are available. What I've been doing here recently, though, I haven't told you guys about this on camera, the last three or four videos I've made, I actually edited them in a video editor that is not Kaden Light. And it is a free and open source editor. I'm trying something different out and you will probably get a video about that particular video editor very soon. So the next question is, hey, DT, how do you make your thumbnails? So I make all of my thumbnails and all of the channel artwork, the YouTube banner, the channel header and all that. Heck, even, you know, some of the logo designs for my t-shirts and things. I do most everything inside GIMP. GIMP is the GNU image manipulation program. It is our free and open source alternative to something like Adobe Photoshop. GIMP is available on all Linux distributions. It should be in every Linux distributions repositories. GIMP is also available on Windows and Mac, I believe. It's cross-platform. GIMP is actually one of the most popular free and open source programs on the planet. I've done at least two or three videos on GIMP. I did a specific video on how I create my thumbnails in GIMP. So check out that video if you're specifically interested in thumbnails. If you're new to GIMP, I did like a 30-minute introductory video on GIMP showing you guys the basics and all the tools in GIMP and that is a really great video for those of you wanting to get up to speed quickly on how to use GIMP. Now, if you were looking for more than just, hey, what program to use, maybe you were looking for tips on what makes a good thumbnail, well, what makes a good thumbnail is going to differ from person to person. Every YouTube channel is different. Everybody's audience is different. What works for one person is not going to work for the other. Nothing is ever a one-to-one copy. You can't do exactly what I do and get the same results. I can't do exactly what this person's doing and get their results. Everything's different, but I will say in general what you want to do with your thumbnails, you want to have a lot of contrast. You want to have a lot of saturation because really high contrast, really over saturated images, that color and that contrast that really pops. When you're viewing search results in YouTube, those really highly saturated, high contrast images really stand out from those boring, just a normal, somebody took a picture on a phone and really didn't doctor it in any way. If you take the time to take a picture of yourself, maybe on a phone and then put it into GIMP and really over saturate yourself, you're going to really stand out.  That's what a lot of people do. One thing I often tell people, people ask me about my thumbnails when they all look the same as far as the color scheme I use with the text, the text that's wrapped in various boxes that are black, white and purple. I do that for consistency sake and I strongly urge everybody that creates thumbnails to be consistent with their thumbnails. That way when you're building an audience, they know when they see a thumbnail, oh, that's that person's thumbnail. And the next question is, hey, DT, have you ever considered getting professional help? So obviously being a Linux user, I'm kind of a do it yourself or kind of person, right? I don't need to hire professionals to do anything because typically if I have a problem, I'm going to fix it myself. That is what we do in this community. So no, typically I don't bother with professional help unless it's in one of two areas. If I need plumbing work done, I don't have the tools that a professional know how to do that, especially if there's actually a serious problem with water leaking in the house. I'll call a plumber and the car. I don't work on my car. It's just not that I couldn't work on my car. It's just for sake of time. If it's a big project, yeah, I'm taking that thing to the shop. But beyond that, professional help, I don't need it. The next question, hey DT, is there a distro that does not have a dock at all? Has the super key search thing and shows the running apps in the top bar for easy switching between apps? Or can we create the same in Papa West in some way? So if you're looking for a distribution that does not have a dock at all, has a super key thingy, which he's talking about, you hit the super key and it brings up a menu and you can start searching for your apps. And it shows the running apps in the top bar. Okay. So here's the thing. I'm assuming this person is kind of new to Linux because is there a distro that has all of this, you're not necessarily looking for a specific distro. You're looking for a specific desktop environment. So and this could you can run desktop environments on any distro, all of them run on any distribution. So it's not a Linux distribution problem. So whatever distribution you're on, keep running it, just install a different desktop environment. One that I would suggest looking at is KDE Plasma because KDE Plasma has a panel. You hit the super key, it brings up the menu and you can search through the menu, just start typing after you hit the super key and all of that. The panel by default is typically at the bottom of the screen, but you can move it to the top. Actually, you move it to the left or to the right. In any side of the screen, you won't put it at the top. So you got your top panel, you got your super key thingy and shows the running apps in the top bar. Yeah, KDE, the panel has many widgets that will allow you to have a task manager or quick launchers kind of like a dock bar thingy where instead of having a big button for your task manager to show windows that are open, they'll just be iconified. You can do all of that in KDE Plasma. And I'm pretty sure that most of the big desktop environments, maybe not GNOME, GNOME is kind of limited, but I'm pretty sure XFCE probably can do this. Maybe LXQt can do this as well. I would be surprised if Budgie couldn't do this. Mate, I'm sure could do this as well. So it's probably a situation by his question. He's on POPOS and POPOS uses the GNOME desktop environment, which is rather limiting. So unfortunately, he probably just picked this as his very first distribution and his very first desktop environment. And that's not where I would have directed somebody. So especially coming from Windows, if that's where you're coming from, KDE Plasma is probably a better place to start. And the next question is, in a response from a video I recently did, one of my backyard boomer vlogs, I was talking about the Linux community and the Linux ideals and things like this. And in a little box out to the side of my head, I said, right now I'm making Linux sound like veganism, as far as a lifestyle change, a way of life. And this person writes, Hey, DT, being a vegan means making an ethical and moral decision to not contribute to hurting animals or exploiting other sentient beings, i.e. animals. What's your problem with that? I didn't say I had a problem with that in this video, right? I said nothing about my thoughts on veganism other than the correlation between Linux being a lifestyle change, a way of life and veganism being like a major lifestyle change and a way of life. That's the only thing I said. I never said that there was a had a problem with veganism or that I love veganism. I made no comment on this at all. So here's one of the things being a YouTube content creator is so many times people try to start arguments where there's not one. Sometimes they just make stuff up. One of the really interesting things over the years I've noticed is people assume political identity with me. I'll make a video and I've had several videos where I've posted something and I guess, you know, it leaned a little left, right? According to some people's minds, I was like, man, you're you're one of these leftist liberal yada, yada, yada, cancel culture people.  And then I make a video the next day. And all of a sudden that same person will be, oh, you're one of these far right wing extremists, yada, yada, yada. The same person called me a leftist the day before calling me this right wing person the next day. And I didn't say anything political on either video as far as I never have I've never once on any video mentioned political parties that I belong to anybody I've ever voted for you've never not one single time on any video have gotten anything from like that from me. So it's weird people just assume this stuff or they just make this stuff. I think some of these people just want to have an argument. So they start something like this vegan person, right? I didn't say anything about veganism, but he's going to try to put words in my mouth like I did hoping that I'll come back and have an argument with him. No, I'm not going to do that, right?  That's why I canceled my Twitter account. That's why I canceled my Facebook account. I don't do that. I have stuff to do. And honestly, you guys should have better stuff to do. If you're one of these people that are constantly looking to argue with somebody on the internet to the point where you have to make stuff up to have an argument with somebody, you know, just to feel better about yourself, you really need to have some introspection. You really need to take a darker, deeper look at yourself and figure out why that's the case and try to resolve that. Now, before I go, I need to thank a few special people. I need to thank the producers of this episode. And of course, I'm talking about these guys right here. Devon Gabe James, Matt Michael, Mitchell Paul, Scott West, Akami Allen, Lennox Ninja, Chuck, Commander Edgar Kerr, Diokai, David, Dylan Gregory, Heiko, Costco, Lee, Maximite, Nitrix, Erion, Alexander, Peace, Archon, Fedor, Polytech, Raver, Red Prophet, Steven and Willie. These guys, they're my highest tiered patrons over on Patreon. Without these guys, this episode of Haiti would not have been possible. The show is also brought to you by each and every one of these ladies and gentlemen as well. All these names you're seeing on the screen right now. These are all my supporters over on Patreon because I don't have any corporate sponsors. I'm just sponsored by you guys, the community. If you like my work and want to support me, look for DistroTube over on Patreon. All right, guys. Peace.


Alright, welcome back everyone if you're watching this on Twitch or if you're watching this later on YouTube. Part number two of the sequence alignment lecture. So let's just continue, right? So we talked about pairwise alignments a little bit. So a pairwise alignment is when I have two sequences I want to align them together. So I want to know how these two sequences are similar or how they are different, right? So the question is, if I have a sequence of interest and I have a sequence with a known function for example, what is the similarity between these two sequences, right? Just looking at them I can see that there seems to be some similarity, right? This one has three Ts, this one has three Ts as well with an AC, with an AC. So the last part is more or less identical but the part before is not, right? But had the hypothesis here is that if these sequences are similar then we can also infer that there might be a similar function of the protein or the microRNA that is being produced. And here we have the concept of sequence identity. So sequence identity is the amount of characters which match exactly between two different sequences, right? So that is the definition of sequence identity. So you can have a sequence identity of 10 or 15. It's not a percentage, it's just a number. So there are two major ways of aligning sequences together. The first way is to have global alignment and a global alignment attempts to align every residue in both sequences and you use global alignment when you are aligning as two sequences which are more or less equally long. So equally long means one is 100 base pairs, the other one is 110. But if you have a very big difference in the length of the sequences, for example, we have a gene which is 5000 base pairs long and we want to align this to a genome, right? So we want to find the best optimal position for this in a big, big sequence. A genome is generally billions of base pairs long. Then we need to use local alignment. So local alignment is the alignment describes the most similar regions within the sequences to be aligned and short sequences are compared versus a larger one or a longer one. So how does this look? Well, imagine that we have a sequence S1 and S2. Then when we do a global alignment, we try to match the entire string. So we try to match S2 to the full length of S1. Well, if we do a local alignment, we try to match the optimal substring. So we try to find the region in S1 where most of S2 aligns and this generally means that we are ignoring the terminals of S1, right? So in this case, you can imagine that S1 might be a genome, billions of base pairs long and S2 is just a 500 base pair strand of DNA that we want to look for. So how this works is that in local alignment, you kind of move the sequence that you want to find against the sequence that you are searching against. While in S2, you don't do that. A global alignment always tries to start S1 and S2 at the exact same position. And then it tries to optimize the alignment by inserting gaps into S2 and to still have enough matches or as many matches as possible. But this is the major difference between global alignment and local alignment. So local alignment is optimal substring while global alignment tries to match both strings together from the start of S1 and all the way up until the end of S1. So there are many, many different ways of aligning, right? Because like we need to have a scoring function to say, well, this is a good alignment and this is a bad alignment, right? So if we think about scoring functions, then the very simplest score that you can come up with is just the percentage of matches. So if I have my first alignment where I align like this, now I'm matching five out of 17 positions. While if I have alignment two, I'm matching 11 out of 16 positions. And this is not entirely perfect because it actually changed the font. This should just be a equally wide spaced font. But now we can have a way of determining which alignment is better. So we can just say, well, if I look at the percentage of matches, alignment two is better than alignment one. Of course, this is the most basic scoring function that you can have. But people thought about this a lot. And in like 1980s people came up and they said like, no, just looking at the percentage is not good enough, right? What you want to do is you want to look at the similarity of two sequences, right? So you have a similarity score for the aligned letters saying that well, if S1 at position X matches S2 at position Y, then we give a plus one. Well, if there is no match, then we just do a negative one.  Additionally, what they say is, well, if you have these sequences, right, and you do the alignments, then sometimes you have to introduce gaps, because it's better to introduce a small gap in sequence S2, so that the rest aligns better, right? So what they then did is make something which is called a gap penalty. So there's a penalty for introducing a gap. And so you look, so you define the score as the sum of the similarity, plus the number of gaps that you needed to introduce times the gap penalty. And the gap penalty is usually set to minus one, because if you have a minus one gap penalty, that means that had the more gaps that you introduce, the more of the score you subtract from the alignment. So this is the most simplistic scoring function which is used in biology to score similarity between two sequences. Of course, there has been a lot of improvement in recent years. And one of these things is that nowadays we have additive scoring with something which we call an affine gap penalty, because in biology, having a gap is, so if you look at how sequences mutate and how insertions and deletions occur in sequences, then there is something which is very common, right, which is the size of the gap. So if we look at a gap, right, and if we have a gap, then it doesn't really matter how long that gap is. But introducing the gap in the alignment is kind of a big negative penalty, right. So that's why we, that's why the next step, what people did is say, well, we have this number of gap positions times the minus one. But of course, it matters if you have 10 gaps introduced at 10 different positions in the sequence, or if you have a single gap, which is 10 base pairs, right. Biologically speaking, a single gap of 10 base pairs is much more likely to occur than 10 deletions of a single base pair. So that's why they have a fine gap penalty. So this affine gap penalty means that you have a gap opening penalty, which is relatively high. But then you have a gap extension penalty and the gap extension penalty is relatively low, right. So that means that introducing a gap of size five, or a gap of size 10 is more or less very similar. But opening five gaps in the alignment, five different places will actually lead to five gap opening penalties. So having five gaps across the whole sequence is more penalizing to the alignment score than having a single gap, which is five base pairs long. So this is nowadays more or less what people are using a variant of this is generally used. So again, the similarities then defined as the sum of how many base pairs match or mismatch, right. So plus one for a match, minus one for a mismatch, then the number of gap openings that we have get a GOP score. So a gap opening penalty, which is relatively high, so minus five or minus 10, but the gap positions times the gap extension penalty is generally very low. So a gap extension penalty is in the order of minus 0.1. So and this makes that the alignment of the read is penalized for having a lot of little gaps, but not so much penalized for having a single big gap. And this is more in line with how biology works, because if you think about an insertion, right, if we have 15 base pairs which is inserted into a sequence, then of course, when I have the sequence without the insertion and with the insertion, and then of course, these 15 base pairs are more likely to be next to each other. So that is the difference between the linear gap penalty where you just say no, every gap that I introduce gets a minus one, compared to the affine gap penalty where you say no, opening a gap is relatively expensive, but extending a gap so making it bigger is a relatively cheap operation. So I don't penalize too much for that. So when we think about alignments, of course, then we have to realize that on the DNA level, the alignment can have a very different answer than on the protein level, right? So if we look at S1 and S2, right, and if we assume that S1 and S2 are both coding for proteins, right, then of course, we can have this alignment of these two sequences. If we now calculate the percentage of matches on DNA level, right, then we would say, okay, so this one matches, that one matches, so two out of three match for the first codon. Here we also have two out of three matching, so that's already four out of six matching. Then here we have no matches, so that's zero, so then it's two mismatches out of nine base pairs. And then we have another three base pairs of which one to match. And so in total, on the DNA level, we have six matches out of 12, right, so a 50% identity between the DNA score. However, if we then look at the protein level and we have to go and we compute for each codon the proper amino acid. And so in case of CAC, so we start with C-A and then we go to C, then we code for histidine. But if we look at C-A-T, which is C-A-U, that's also code for histidine, right, so there's a perfect match of the first codon. Then if we do the same thing for the second codon, then we do G-C-G alanine. G-C-A also codes for an alanine. So on the protein level, there is again a perfect match for the second codon. So you can see that generally the matches on the DNA level are not as good as matches on protein level because of the fact that the third base pair in the codon is degenerate, right. So if we continue here, then two out of four amino acids match and then we do TCC, so UCC, which codes for a serine. And then we have AGT, so we have AG and U, which also codes for a serine. So again, at least three out of four amino acids match on the protein level. And then the last one, again, the only difference is in the third base pair, so these also are similar. So they would also produce a similar amino acid. So that means that on the DNA level, we only have a 50% identity, so only 50% of the base pairs have a direct match. But on the protein level, we have a 100% match. These two sequences code for the exact same four amino acids, right. So when you are considering alignments and when you are looking at things like evolution or how far two proteins are apart, then there might be a big difference if you compare two animals on the DNA level or if you compare them on the protein level. So keep that in the back of your mind that DNA alignment is not going to give you the exact same answers, protein alignment because of this third base degeneracy, so the wobble base in the sequence. Not just that, but if we look at DNA, then DNA doesn't, if you have an A changing to a G, right, because of the biological structure of the base pairs, it is more common for an A to turn into a G, which we call a transition. These are very frequent in DNA. So if we look at DNA, then transitions are very frequently observed. While a C changing to a G is biochemically much harder. So that doesn't happen that more often. So transversions, so going from a C to a G or from a G to a T are very rare. And when we do the alignment, we of course want to take this into account. So instead of saying two sequences match, I give a plus one. So an A matches an A that is a plus one score. If you have an A matched to a G, then the penalty for this should be lower than when you match an A to a C. Because matching an A to a C is just much more uncommon in how biology works and how biochemistry works. Just having a random more or less mutation, which occurs because of chemicals or whatever, chemically speaking, purines, adenine and guianine are much closer together. So it's chemically much easier to change a guianine into an adenine as it is to change a guianine into a thymine. So not only do we consider the fact that we have matches and mismatches, for the mismatches we also have to consider the substitution probability. So the substitution probability is the probability of an A turning into a G, which is a better probability is it has a higher likelihood to occur than an A being substituted or being trans or being transformed into a T. And so the original code where we looked at that we say a plus one for a match, a negative one for a mismatch, that this should also be adapted and we should not score always a negative one for a mismatch. Sometimes we should score a negative half and other times we should score minus two, for example, because of the fact that transitions are frequent and transversions are very rare in the genome. I hope that that's clear. If we talk about amino acids, this is even more complicated, right? Because amino acids are, some amino acids are very similar to each other, while other amino acids are very different. So here we have the Taylor diagram of amino acids, which is just a Venn diagram of how closely related amino acids are, right? So this tells me that substituting an isoleucine by a leucine is a very common, commonly observed thing in biology, right? If I have a protein sequence, then at amino acid position 44, having an isoleucine or having a leucine is very common. But if instead of an isoleucine, I all of a sudden see a proline, then that is of course a big difference because the distance between the isoleucine and the proline is much bigger, right? So there's a much, the chances of seeing an isoleucine change to a proline is much less. So when I align two sequences, I of course want to have, score a mismatch between an isoleucine here and a P, I want to give a bigger penalty than saying I have an isoleucine in the one sequence and a leucine in the other sequence, because these are very close together, right? So similar amino acid substitutions occur with higher probability and then if we align two protein sequences to each other, we need to compensate for this. So we need to give more, for a mismatch, we need to be more relaxed when it's an F to a Y compared to when it's an F to a Q. And of course, this figure, the Venn diagram of amino acids, this Taylor diagram, of course, a computer cannot understand this. So for a computer, we have a different representation. So for computers, we have two different types of representations which represent the substitution probabilities for protein or for amino acid exchanges. And the first one that I wanted to tell you guys about is the POM matrix. So the point accepted mutation matrix and the point accepted mutation matrix has here the mismatch penalties for when you align two protein sequences together, right? So on the diagonal, you see the score which is given to a match, right? So if a cysteine matches with a cysteine, you increase your score by 12 points. If you then see that at an other position, the first sequence has a proline, but the other sequence has a leucine, you score it negative three. However, if you see a proline turning into a tryptophan, then you score negative six, right? So these give these the scoring matrix, right? So a computer can use a scoring matrix and it can just look up, okay, so I'm aligning, now I have these two things which are mismatching or which are matching, what type of a score do I need to give it, right? And then here, the POM 250 matrix is based at similar amino acids are substituted with higher probability, right?  Besides that, you nowadays almost no one uses POM anymore. Nowadays, people always use the Blossom matrix. So the Blossom matrix is very similar to the POM matrix. It has the same kind of goal. But it is called the block substitution matrix, right? So amino acids in the table are grouped according to the chemistry of their side chain. Each value in the matrix is calculated by dividing the frequency of occurrence of the amino acid pair in the blocks database. So the blocks database is a big database of protein sequences. And they just look at all known protein sequences, they align all of these protein sequences together, and then they see how often substitutions occur. So a score of zero in a Blossom matrix indicates that two amino acids were found aligned and the database was expected by chance, right? So there's no positive of negative score that you would want to do this, right? So this is a penalty of minus one. But a positive score in the Blossom matrix indicates that the alignment was found more often than by chance, right?   Negative scores in the Blossom matrix indicate that the alignment was found less often than by chance. So this is a very uncommon substitution. So this uncommon substitution should be higher penalized. So instead of minus one, you say I do a minus two.  Since the Blossom matrix is just built based on the occurrence of the amino acid pairs in the blocks database, you can say, build me a Blossom 62 matrix, right? So when I build my matrix, only consider sequences which are no more than 60% or 62% similar, right? I can also build a Blossom 80 matrix, which means that I build the matrix and the input is not just the 62% similarities, but I also allow proteins which are more or less 80% related. And I can have a Blossom 45 matrix. And that allows me so I'm building this this Blossom matrix now based on the fact that these that I also include distantly related proteins. So which Blossom matrix you use when you align two sequences together is dependent on how far away the two sequences are that you are aligning. So if I have two sequences and I know that both of them are for cows, one for cows from Europe, one for cows from America, then I'm going to use a Blossom 80 matrix because these two because cows and cows are very similar. Then of course, I want to have a more related protein. So I want to kind of look at related proteins and see what is commonly commonly observed for substitution frequencies for more related proteins. Normally, if you don't know this, if you don't know how far apart your your species were that you used it for, you use a Blossom 62 matrix. So the Blossom 62 matrix is more or less the standard matrix. But if I'm doing alignment, and I know that the sequences that I'm looking at should be very related, because the animal that I got the sequences from our related animals, then I use Blossom 80. If I'm comparing, for example, proteins from mouse to proteins from ants, right, then there's a big evolutionary distance between these two species. Then I generally tend to use the Blossom 45 matrix, which means that I want to look at substitution probabilities. But I also when building it, I want to build this matrix on very dissimilar proteins. So it allows me to to have different substitution probability matrices for, or less, very recent evolutionary events, kind of unknown evolutionary events, because I don't know how far my species are, are very unrelated evolutionary species, right? So it has to do with evolution and how further apart two species are, the more likely that the proteins start also differing. So I want to compensate for that. So fun fact about Blossom 62. It is the default matrix when you do protein blasts. So when you blast protein sequences to other protein sequences, you use the Blossom 62. And it was used for many years as the standard. But the problem is, is that in the Hennikoff and Hennikoff article from 1992, they make a calculation mistake. And this calculation mistake actually caused this matrix to not be the way that they describe it, right? So they had this algorithm. This is how you build the matrix. Then they built the matrix using their algorithm, made a computation mistake. And surprisingly, this computation mistake actually improved search performance. So it actually made proteins, protein comparison better. Just a little fun fact. Okay, so when we compare pump matrices compared to Blossom matrices, then these are more or less the differences. So a pump matrix, so point-acceptant mutation matrix is based on global alignments of closely related proteins. Well, the Blossom matrix is based on local alignments, right? So if you have sequences which are very different length, then you use Blossom. If you have sequences which are very similar in length, then you can better use pump. So the pump one matrix is the matrix calculated from comparison of sequences with no more than 1% divergence, right? So pump one is the matrix which looks at very, very closely related evolutionary species. And then Blossom 62 is the matrix calculated from no more than 62% identity. So they are each other's inverse, right? So Blossom 99 matrix is similar to a pump one matrix. So all the other pump matrices are extrapolated, right? So they only do one computation. They compute pump one and then pump two is computed from the pump one matrix. But in Blossom, that's not true because every Blossom matrix or Blossom 62, Blossom 40, Blossom 80, they are all computed based on observed alignment, right? They are not extrapolated by the pump matrices are more or less a mathematical generated from the pump one. So higher numbers in the matrices naming scheme denote higher evolutionary distance. While for Blossom it's the other way around, larger numbers means that there is smaller evolutionary distance. All right. So finding the optimal alignment was more or less solved in 1981. So if I have two sequences, I want to align them together and I want to find the best alignment, then this is a solved problem, right? You don't have to study this anymore. You can do a PhD in finding the optimal alignment because this was solved once and for all mathematically by Swiss modern man. And in 1981, they published their algorithm for finding the optimal alignment. And this under all circumstances will find the optimal alignment between two sequences. It works for global and local alignment. And the way that it works is that it's a systematic construction of all optimal solutions. So it's an algorithm which is guaranteed to give you the best alignment given two sequences. The big problem with the Swiss modern man algorithm is that it takes time proportional to the product of the sequence length. So the longer your sequences are, the longer you're going to wait for the optimal alignment. And of course, this is not always possible, especially as Swiss waterman works very well when your sequences are very short. So if you have a 100 base pair sequence, which you want to compare to a 50 base pair sequence, then Swiss modern man will work. But now when one of these sequences becomes very large, like the size of a genome, like 2 billion base pairs, then the runtime of the algorithm is not exponential, is multiplied by the difference. So aligning a sequence which is 2,000 versus a sequence which is 100, compared to aligning a 2 billion sequence compared to the same length will give you a million times increase in runtime. So it will take a million seconds or a million times longer to do it for 2000 versus x versus 2 billion versus x, right? That's the multiplication in there. So the way that the Swiss waterman algorithm works is more or less a visual thing. So it creates a dot plot and then tries to find the minimal path through the dot plot. So I did my best to kind of show you guys how this looks. So here we have the human myoglobin sequence. Here we have the mouse myoglobin sequence. And here we just what we do is we just have all of the letters from zero to x and here from zero to x. And then for each letter we write down if it matches or if it doesn't match, right? So if you zoom in a little bit and we want to find a path, for example, we want to align Paranas with Parana, right? Then what we do is we just write down the letters and we say, well, the P matches the P, the A matches the A, and it also matches this A, the R matches this R, the A again matches both positions, the N matches this one, the A matches again both positions, right? So we just make a matrix and every time that the row name or the row letter matches the column letter, we say this is a perfect match, right? And of course, when using DNA or protein sequences, we don't write a perfect match like a one in the matrix, but we write the score using the substitution matrix, right? For DNA, we take the the transversion transition probabilities. And when we use a Blossom matrix, then we take the value which is located in the Blossom matrix and then put it here. So how do we do this? Well, the optimal alignment will be the alignment which you get when you go from the source, the green dot here, all the way to the sink, which is gone from the slide, but that's the red dot here. So here there should be a red dot. Is it still in the previous one? No. So the sink is over here, right? So any pairwise alignment can be represented as a path through the matrix and we find a path from the source to the sink. So every time that we are here, when there is a perfect match, we always take the diagonal. When there is not a perfect match, we can do three things, right? We can say, well, we introduce a mismatch or we introduce a gap in sequence one or we introduce a gap in sequence two, right? We can do a mismatch or we can go from, or we can introduce a deletion from P to A, so a deletion in sequence two or a deletion in sequence one. So what Swiss Waterman algorithm does, it just walks all possible paths from the beginning to the end and then it, for each path, you get a score because introducing a gap or introducing a mismatch just as a negative score. So for each of the paths through the matrix, you get a score, right? One of the ways of aligning would be we just say that these two sequences don't match altogether. So we just go from green, we walk all of the way here. So we introduce gaps in sequence two and then we just go here. So we introduce gaps in sequence one and then we're at the sink. But this is a very bad path because this will get a very low negative or this will get a very negative score, right? But this is how it works. It just goes and computes all of the paths through the matrix and then just takes the one with the highest score and that is the optimal path. This algorithm is very time-consuming. So in 1990, Allshoots at All published a paper in Journal of Molecular Biology, which is nowadays one of the most cited papers in bioinformatics and is one of the most popular bioinformatic programs. And this is called Basic Local Assignment Alignment Search Tool. So it's called BLAST, right? So the tool aligns two sequences together and it is the most popular alignment tool. Why? Because it speeds up searching by orders of magnitude. So if you would want to compare one sequence against all of the like 12 billion sequences that are in GenBank, using Swith Waterman, you would wait until the end of the universe before it comes up with the best alignment, right? But by using BLAST, you can have one sequence searched against all of the sequences that are in the GenBank database. So I think it's like 12 billion or something. And you can do that within minutes. And that is because it uses smart pre-processing. So only sequences with similar substrings are being compared. So instead of taking your whole sequence and then going through each of the sequence in the database and aligning them, what it does, it just chops up your sequence in like words. So it says like, no, I take the first five base pairs, then I take the next five, the next five, the next five. And I only compare my sequence to sequences in the database, which has a match with one of the substrings that I'm computing. And of course, this quickly excludes many, many possible alignments, right? So if there's no match between a substring of your sequence compared to any of the other, and this is of course a perfect match, then the whole comparison is discarded. Funny fact is the Journal of Molecular Biology, and let me say it correctly, is a journal with an impact factor of five. So one of the most popular bioinformatic tools used by millions and millions of people across the world every day was published in a journal with an impact factor of only five. And just to remind you guys that not everything published in nature is great. Also stuff published in low impact journals can be awesome. So BLAST provides many different ways of aligning sequences. You can use BLAST N, and if you want to align a DNA sequence against a DNA sequence, if you align a protein sequence against a protein sequence, you can use BLAST P. If you want to align DNA sequences against proteins, of course this uses the amino acid translation table, right? So it uses six frames. So six frames means that you are taking six times three is 18 base pairs. So you can compare 18 base pairs of DNA, six frames at a time against proteins, then we use BLAST X, right? So I can take a DNA sequence blasted against the protein database and still find the protein which has the highest similarity to the DNA sequence. If I want to do it the other way around, if I want to align proteins against the DNA database, I can also do that. Then the tool that you need is called TBLAST N. And of course I can also do the same thing. I can compare DNA to six frame translation against DNA via six frame translation. And then this is called TBLAST X. So this is based on the fact that of course, if you compare two DNA sequences, you want to make sure that they are coding for the same protein, right? So that's why TBLAST X exists because of the fact that the third base pair is the wobble base pair, which we learned when we had the RNA lecture. Good. So when you do BLAST, every alignment gets a quality number and this quality number is called the E value, right? So the smaller the better, the closest to zero is the best, right? So an E value of zero means that two sequences are identical. An E value of 50 means that two sequences are completely different in a way, right? So and the E value represents, so the E value in the alignment, BLAST alignment is very similar to a P value that we compute with statistics, right? So it tells you how many alignments of that quality or better are there in a random database of sequences, right? So if I get an E value of 0.02, then you can more or less use this as an interpretation, right? So we say that E values which are lower than 0.05 are considered homologous and E values higher than 0.5 are not considered to be similar in a way. Besides the E value, it also tells you the percentage of identity, so the higher the better, right? So it's just the number of base pairs that match out of the total number of base pairs that you have and it also tells you the length of the alignment, right? So the largest matching substring, so how many base pairs of sequence 1 were aligned to sequence 2? Because of course it could be that the alignment is really good, but only when you chop off the first 10 base pairs of sequence 1. So when we talk about homology, then the rule of thumb for homology is that the E value should be lower than 1 times 10 to the minus 5. There should be a continuous stretch of around 100 base pairs matching in the two sequences that you're searching for the amino acids when you are doing protein search and there has to be an identity between the sequence that you're searching for and the sequence that you found of around 70% on DNA level and when the identity is 25% on protein level, we still say that there is homology, right? But this is really a rule of thumb, right? There's nothing to say, two sequences are homologous, but generally when we annotate genome, so when we have a sequence in an unknown genome, then we blast it to the database and then when we find another sequence which is 1 times 10 to the minus 5 similar and it has a stretch of 100 base pairs and it is 70% identical, then what people do is they take the sticker of the known gene and then put the same sticker on the gene in the new genome, right? So for myostatin and this would be that, okay, I have a sequence in an unknown and you just put it on, but this is the rule of thumb. All right, so that was everything that I wanted to tell you about pairwise sequence alignment. So the next step is, of course, multiple sequence alignments. What do we do when we have more than two sequences, right? So multiple sequence alignment is one of the most essential tools in molecular biology and so the aim is to find highly conserved subregions or embedded patterns of a set of biological sequences and this is because conserved regions are generally regions where stuff happens, right? It's where transcription factors bind, they have a very similar sequence across different species at which they bind. But also you can think about SARS-CoV-2, right? The reason why we designed the original vaccines against parts of the spike protein is because of course these sequences were found to be homologous, right? Because in many different coronaviruses, all of the coronaviruses more or less had the similar amino acid sequence at this position, so that's why we used it to develop an antibody against that. Not only that, but what multiple sequence alignment allows us to do is that it allows us to estimate the evolutionary distance between sequences but also between species and we can use it in the prediction of protein secondary and tertiary structure because if we know that two protein sequences are highly similar and are highly homologous, then we can also assume that the 3D structure of these two proteins is very similar. The first practical method for multiple sequence alignment was developed in 1987 by Sankov and before 1987 they were constructed by hand. So if you were a bioinformatician in 1983, the year that I was born, then around 70% of your time was spent on multiple sequence alignments and the way that you would do it is you would have like these pieces of papers with these squares, right? You would write down the sequences that you have and then you would by hand start aligning these sequences. So move the little pieces of paper on top of each other and with your eyes you would look if there is similarity or not. Fortunately we don't do that anymore but that is because no one actually was able to do this with a computer and that is because in 1987 dynamic programming was still very expensive. So like having using a lot of memory and remembering all of the different paths through this graph, through this 3D space or 4D space is really hard but in 1987 the first practical method became available by Sankov. So the idea about multiple sequence alignment is that you perform successive pairwise alignments. So instead of having a single pair or instead of so you compare sequence one to sequence two, sequence one to sequence three, sequence one to sequence four and then you do the same thing for sequence two, right? And then the first thing that you want to do is kind of build a consensus sequence. Like where is there no variation in all of these alignment? And then of course further alignments are done based on your consensus sequence. So there are again very crucial parameters when you do multiple sequence alignment so which scoring matrix you use is having a big, big effect on your alignment. Your gap opening penalty and your gap extension penalties also make for a big difference in the alignment. So when you align more than two sequences you can use the same strategy as when aligning two sequences, right? So instead of hey if you have a dot plot so sequence one, sequence two, now instead of using a two-dimensional matrix you have an n-dimensional matrix which each axis represents a sequence to align. So when I have three sequences it ends up being a cube, right? So I have piranhas here, piranhas here and I have another piranhas or something on this axis. And again we do the same thing, we go from source to sync and we calculate all of the paths possible through this 3D matrix and then the path which has the best score, which hits the most matches and which avoids most of the insertions, deletions and this kinds of things, that is the sequence, the optimal path and the optimal alignment. So of course for three sequences of length n the computational time here is n to the power of three, right? Because it used to be sequence one multiplied with sequence two, the length of sequence one multiplied by the length of sequence two. And of course for three sequences it's just length of sequence one times length of sequence two times length of sequence three. So that is n to the power of three. But the problem here is when you have k sequences imagine that you're trying to align 16 different sequences together. Then you need to build a k-dimensional matrix and then the computational time becomes n to the power of k, which is just an insane computational problem. So dynamic programming approaches for aligning between two sequences is easily extended to k sequences but it is very impractical because of this exponential runtime. So in many cases, had by just using Swiss Waterman, algorithm on like five sequences of length a hundred, you already end up with a computational time which is like five to the power or a hundred to the power of five. And so this quickly becomes the amount of waiting time is more or less similar to like the time that the universe existed. Good. Yeah, let's do one or two more slides. So it is multiple sequence alignment is very useful for more distant alignments because you get a view of which positions are conserved within DNA or which positions are conserved in proteins. The most commonly used algorithm today is Clustel W or Clustel Omega, which is the updated version of the algorithm. The original Clustel W algorithm was written by Higgins and Sharp in 1988 and it is a progressive algorithm which first tries to identify the best high scoring pairs. So which two sequences are aligning the best together and then to identify the closest pair of pairs. So you have to first you identify which sequences pair wise are most similar and then you in the next step you then see which of these two groups are similar to each other. And then you continue this. So you do this iteratively. So you take one sequence, you add the sequence which is most similar and then you add the next sequence which is most similar and the next one and the next one. So you're building up kind of an alignment tree. And then you do this until all the sequences that you want to align are aligned. So it's a multiple alignment scheme where corresponding amino acids are in one column. So how does this look right? So if we look at the myostatin gene of 10 different animal species, so human, pig, mouse, rat, sheeps and stuff, and then this is how the multiple alignment looks like, right? So what is multiple alignment? Well, given k sequences, we want to align them in a way that minimizes the path through this 10 dimensional matrix. So for these sequences that we had, it looks kind of like this. But what does this now tell us? Well, it tells us for example which residues are conserved, right? You can see that across all of the species that we looked at, there is always a C at position number five. There is always a W at this position. So this means that this C and this W are important for the functioning of the protein that we're looking at, right? Because otherwise, there would have been changes in looking across like a million years of evolution, right? And that's kind of what you do when you compare like humans, pigs, mouse and sheep together. You're looking at the more distant species that you add, the further that you're looking back in evolution, right? So if you find that something is conserved between humans, mouse, rat, sheeps and all of these things, then you know that for like how do you call that? Not marsupials, but we are people, animals that have a womb. I forgot the word, but for all of the animals together. But if you then include for mammals, that's it. So if you see that all mammals have a C, all mammals have a W, then you can more or less infer that this is very important for mammals. Not only can you see which residues are conserved, but you can also see which regions are conserved, right? So in the first four species that we looked at, we found that all these four species have QLPG, right? And all of the other species that we included in the alignment do not have this, right? So this is a conserved region and it's not conserved throughout the whole evolutionary tree. It's only conserved throughout a part of the evolutionary tree. So of course, when this happens between... So if we are aligning like mammals and reptiles and marsupials and these kinds of things, and we find that all of the marsupials have this common pattern, but this pattern is not shared with the mammals and the reptiles, then we can infer that, okay, so this part of the protein might be very important in marsupials, but it's not that important in other species like mammals or reptiles. Not just that, but we can also look at patterns, right? We know that amino acids are sometimes very similar to each other, right? So here we see valine, leucine, and the other ones, right? And these are all hydrophobic residues, right? So it's not important which amino you have, acid you have at this position. It is important that as long as you have a hydrophobic residue at this position, then the protein is probably working, right? So put all of this together, using multiple sequence alignment, we can identify regions of proteins which are conserved, we can identify patterns like this is always hydrophobic, this is always hydrophilic, and we can find these regions which are conserved regions, which tell us something about evolution and that this part of the protein might be very important in certain branches of the evolutionary tree. Good, so now it's three. I forgot the word mammals, so I'm going to take a break, just to kind of clear my mind, and after that we will be back with more multiple sequence alignment. So if you're watching this on YouTube, see you in the next episode, and this then.


Welcome to Toffee TV. This video was taken from the Daily Extra, the video where we delve behind the headlines on the Everton News Daily. To watch the full video, get over to Patreon.com forward slash Toffee TV EFC. We've got 10 exclusive videos on there every single week for our Patreon subscribers. So get over there and see what we're doing. Stephen Kelly says, I trust Marcelo Marcos. This was four or five years ago. We probably end up with Charlie Austin and Gary Kale. But I personally will be happy with the centre forward and the centre back. Will Silver continue to mix in long balls to dominate tactics from Tim Whittle? Yeah. Why wouldn't you? Yeah. The best football is a hybrid of everything. I think it's a I think don't get bogged down by 800 passes to get out your half. But I've that ability to do that, but also have the ability to turn, play, run very quickly. The same as the other lot do. They play loads of long direct balls, but it's effective. It's not against the law. But you do whatever you do to win games. But if you can mix it, then you're difficult to play against, aren't you? United, you should be brilliant at that day under Ferguson. They play any which way. They play short. They play long, wouldn't they? Yeah. And that was what it was about. But you just open the two deals in terms of outs at the moment, which you're currently ongoing through high profile outs, which obviously Adam O'Loughman and Idrisha Magai. Did you watch the video saying it? From Jeff, this song goes out to Idrisha Magai. What are you calling Magai? Anyway, are you hoping for a swift resolution in both of these things? Just get them done and dusted. I mean, who wants a long resolution? No, but you know what I mean? Would you just rather go, they're both gone tomorrow and we move on? Yeah, definitely. Because the Ghana thing, I want to keep Ghana ball. Yeah. If he wants to go and then they'll go shitting, then you've got to get it done, haven't you? I think he does want to go. And therefore, for me, get rid of them. Yeah. You know, and the same at Loughman, they come out the door and then we can full steam ahead with getting people in the door. Because I know that's still, they're both going side by side. They're twin-tracking both of the things. But I just think we don't want players in our squad who don't want to be here getting rid of them, especially if they're in the squad. People like McCarthy and that is a different thing. I just want us to get someone in now that, like, ticks a box that we really need, just so that we can, it's done. I think if we got a chance of forwarding, I think everybody would. Yeah. I think everyone would come and I think everyone would be buoyed, especially if it was like Moise Keane's, right, because they were, everywhere were after him. If he could get that deal done, I think everyone would go up a level then and the excitement to kick in. And then I think people would be patient and get a centre back in and we might be able to get him in. I think the longer it runs on each day, it cranks up again, doesn't it? And we all need a striker. We know we do. Stephen Kelly, 15 years ago, since Tim Kayle joined the club, your favourite Tim Kayle moment. Mum. Sunday last minute? No, a new castle for me when we finished fourth. Then he scored, and he just went. And that was it. We knew the game was done. Then they were giving it all up. But there was loads, I loved Timmy, every load of goals he scored. He was brilliant. He had come up big in so many games, Ampfield last away off the art set a free kick. There was loads he was just there he was a player who always turned up in the big games for you always


Hello everybody, Hoodacore Commander788 here and this is a special one-time only reaction video, me trying these Canadian snacks. These snacks that Jaren sent me in the box, I just did an unboxing video, he sent me some GI Joe stuff, this is not GI Joe stuff, these are all Canadian snacks, I've never tried them before. You wanted me to try these out, so what I'm going to do is I'm going to take a bite of each one of them and I'll give you my reaction as to what I think of each one. Let's give it a shot, I've got some water, I think I'll need that and we're going to start with Postus Hickory sticks.  Open them up and let's see, they are the original and what do they like? Yes, made of potato, crunchy, they actually have like a bacon-y flavor, sort of, and salty, nice tasty, this actually has a really nice flavor. I'll finish eating these, these are good, yeah, so original Hickory sticks, they're basically short little crunchy potato fries basically and they're pretty good, so that's one Canadian snack down and we're off to a good start, I like these a lot. Now let's try Caramel, Caramel by Cadbury and it looks like it is caramel and chocolate which is a combination that I like, so I should like these, let's give this a try, see if you like it. It comes in sections, so I'm just going to break off a section and give it a shot. That's good, it's similar to a Caramello but it's a little better, the chocolate's better on it, so Cadbury Caramel, also a winner, also a Canadian snack that I've never tried before, these are good, I will also finish these. This is Cadbury Crunchy, Cadbury Crunchy it says sponge toffee, so let's give the crunchy a try, so I'm not familiar with that at all, so let's try it, let's give it a shot, let's try it. I thought this was going to be like a Butterfinger but it's not, it's kind of a butterscotch flavor, I'm not a big fan of butterscotch but it's not bad, I'll bet Susan will like this, I'll let her give this a try, okay let's now try Hawkins Cheezys, these should be cheesy and they kind of look like Cheetos, let's see if they're anything similar to good old fashioned American Cheetos, let's see, that is, that's a lot like a Cheeto, what's the difference between this and a Cheeto, these might just be a little crunchier and maybe the cheese flavor isn't quite as strong as what you would get from an American Cheeto but not bad, so this is something that I would eat, it's a pretty good snack, real cheddar cheese, so that's the Hawkins Cheezys, let's move on to the next one, coffee crisp, coffee crisp, I like coffee flavored things so this should work pretty well, makes a nice light snack, alright so this is a light snack, that's good, so let's try coffee crisp, if I can open it here, alright, looks like it's a traditional candy bar, I'm just gonna break off a piece, there we go, that crumbled a little bit, let's see let's try it, it's a strong coffee flavor but it's not too strong, I mean you can definitely taste the coffee and I agree, this is a good light snack, it's not heavy at all, it's kind of like a wafer inside covered with chocolate, this would be a good snack, it would be a good snack in the morning, this is a good morning snack, yeah I approve of coffee crisp, alright I had to clean up my mess a little bit after my coffee crisp crumbled but we're ready to move on to the next one, the next one is Nestle Big Turk, Turkish Delight, really, really, so will this make me betray my siblings to the white queen, is that what this is gonna do, alright, Big Turk, Turkish Delight, let's try that, looks like it is a chocolate covered bar, it's in segments, I'm gonna break off a segment, oh it's very, it's very chewy, okay, alright I got a segment broken off so let's give this a try, it's a unique flavor, it's not exactly licorice, it's a little too subtle for that, I can't say I've had anything Turkish Delight flavored before so I don't really have anything to compare it to, I mean it's not bad, it's probably a bit of an acquired taste, so this is one I'd have to get used to a little bit but but the flavor isn't overpowering so it's not, it's not bad, but yeah this is definitely one that I have to get used to a little bit before I ate a lot of it, next we have Glosset Raisins, looked like chocolate covered raisins, best ever it says best ever, well let's see, let's see if it's the best ever, let's give it a shot, give it a try, pop this open, yeah it looks like chocolate covered raisins, let's give it a try, these are indeed chocolate covered raisins, that's exactly what they are and I love chocolate covered raisins, it's hard to screw that up and it tastes exactly like what it sounds, it's raisins covered with chocolate and the last thing we have Nestle Smarties, now we have Smarties in the US so I'm wondering if these are the same Smarties or if these are a different type of Smarties, so let's open up the Smarties, it says make them last here on the box, I make no promises, I can't say that I will make them last, so but let's see what they taste like, open, open, open, ah okay it's got like three segments inside the box and looks like just some random colors in there and they're definitely not the same as American Smarties, so all right I just spilled some of the Smarties, so I'll cut that part out, I'll edit that part out, I just wanted to get a few different ones so I could try different flavors, so here's a purple one, oh they're they're like crunchy on the outside like chocolate like M&Ms, I thought they were like different flavors, I thought they were like different fruit flavor candy but they're not, the flavor is a little different from an M&M and the shape is a little different of flatter, well now obviously these are good, I'm gonna clean out my mess here but I'm definitely not gonna make them last, I will be eating all of these, well that's all of them, thank you Jaren, I hope you enjoyed this reaction video, hope everybody else did too and if reaction videos aren't your thing well then just skip it but if you do like kind of seeing this kind of thing I hope you enjoyed it, my family has just kind of been waiting in the wings here for me to finish this because they want to try all of these so now they'll get to try them too, but this was a lot of fun, thank you again, thank you for the other box and thank you for these as well Jaren, I really appreciate it, this weekend we will get back to regular real GI Joe toy reviews, I've got one already ready to go so I hope you enjoy that, thank you again Jaren, thanks for everyone for watching and I'll see you next time.


Hi folks, my name is Andy Schultz. I'm with the Maine Forest Service and I'm going to focus in on the left hand elements, the so-called live elements that Amanda mentioned before. So we're standing here in a mixed wood stand, mixed meaning there are softwood trees and hardwood, particularly behind me is a hardwood stand and that's a good illustration of several of those elements. Let me first mention though starting from the bottom up with the left hand and I'm going to go like this. That's the riparian area, that's water. We're standing on a hill so you won't see water here but water is important. Any number of bird species need the water, they need the vegetation that's around the water, the usually shady aspects of that. And by the way that shade that protects the temperature is also good for brook trout and other fish, which illustrates that when you're managing your forest for birds you're also managing it for other species as well. So this is really one way to work with wildlife in general by working with certain birds in particular. So moving up from the riparian area we have the understory which as you recall is the first six feet from the ground up. So if you look here you'll see that we have some sapling trees in that zero to six foot range. And when we assess this we tend to look, what we want to look at is see how much vegetation is in that layer. There's a term called closure so if you were to stand there and look up how much of what you saw would be covered by leaf material. In this case there wouldn't be a lot but there's some. The next layer up is the mid-story that is from six feet to thirty and you'll see that some of the trees here are in that range. Again there's not a lot of them so the closure is not high but there is some. And then of course the third layer up the overstory above thirty feet. As you look into the background you'll see there are trees in that strata and there's probably more of that still than the other layers right here. So not just some but maybe more than some. Now that's the quick and dirty assessment that you do with your handy tool. Sometimes though you might ask a forester to go out and measure that in a little more detail and put that in some sort of a plan or something like that. But for the purposes today we're just going to go with the handy method. So that fifth piece on the hand on the left hand is gaps and that would refer to the openings in that upper layer above thirty feet. We're standing in an area that is relatively small gap but still is going to be used by birds like flycatchers. Something else to think about when you're looking at the live stuff are your tree species and the mix and I mentioned this is mixed wood. What we have nearby is a softwood inclusion of pine and hemlock amongst a greater forest of mostly hardwood and certain bird species really look for that particular feature. So these are some of the things that you can walk through the woods and notice write it down or you can have a forester do a more formal assessment or inventory of these features. Another feature that you want to look for is what we call legacy tree. These are bigger older trees usually they have bigger fuller crowns. They were here at a time when they had more light they could open up. We're standing next to one here that provides yet another element of wildlife habitat in general and bird habitat in particular. So here's another example of the three elements of habitat that we talk about on the left hand the so-called living side and the three elements are the understory the midstory and the overstory. Understory is from ground level to about six feet and you'll see right here we've got a pine hemlock we've got a softwood understory filling in here. Basically somewhere where you can't really see through the woods that means that there's understory there blocking your view. Now if you look up above that six foot level six feet to about 30 feet that's what we call the midstory that would be the middle finger of those three. And you can see up here that includes some of the lower branches of taller trees as long as they have leaves on them and they're filling in that space. Then there is a layer there that some birds need or use. And you'll see that that's filling in a little bit in this part of the woods. And the third is the one that as far as I'm always looking at and it's really important to look all the way up. And that's above 30 feet so more than a three-story building 30 feet and above is what we call overstory. And there are a lot of birds that require either a very dense closed-in overstory or a mostly closed-in overstory with small gaps. So that overstory layer is also very important. Thank you Andy for that great review of the live elements of the forest that birds and other wildlife really depend on. And I invite you back for the next section where I'm going to be talking about how great dead wood is. Please join us. Thank you.