The next talk I will be by Dominic Gorghe and Mike April on Debian Edo's school linux. Yeah, welcome to our talk, thanks for being here. Yeah, as you already heard in the introduction, we will be talking about Debian Edo. Who of you has already heard about the Debian Edo project? Very cool. Okay, who of you has heard about school linux? Okay, for those who have an aura, can you use a small refreshment on what this is? Debian Edo is a project inside Debian. It's a pure blend and it cares about everything that has to do with the use of Debian in schools and in education. In several regards. First of all, Debian Edo offers pre-configured installation media that can be used to install and operate a fully featured educational network for schools, both for operating servers, including all the nice things like managing hosts and groups of computers, for computer labs, for example, users, access rights for the users, and stuff around that, like doing backups, having internal email for the users, stuff like that. And also, which is probably the most important part, network boot environment, so computer labs can be booted over the network using PXE or can be installed over the network. And on top of that, there's a desktop environment that can be installed pre-configured to work very well with this network, and it offers quite a huge set of educational software that can be used in the different subjects at school, starting from informatics, of course, but also an office suite, educational software for geography, biology, chemistry, and all that. I think, do you know how many packages are pre-selected? I'm not so good at statistics. I think it's something around 100 that are pre-selected. And of course, as it basically is just Debian, of course, there are tons of other packages for educational use available. Today, we want to mainly talk about what we intend to happen to Debian Edo this year, or in the foreseeable future, what has already happened, give a small update about what happened in the past, and to get started with that, we will start by telling a bit about different groups of people who are interested in Debian Edo, or maybe should be, or sadly aren't yet, who knows. Can you tell a bit about the development of Skolinox and what is special about it, what makes it special inside Debian and what developers do, what we care for? Yes, thank you. So I'm one of the Debian developers, the Debian Edo developers. And there are other people here in the room that actually receive our highest appreciation. One of them is actually Wolfgang, who has been keeping the development up for the last three years. Then it's Holger, who is sort of involved in everything, and also in Debian Edo, and is doing a great job as a release manager. So the main thing for the last two years we have to mention is that Debian Edo has really travelled back inside Debian. So it was some overlay thing with extra packages outside of Debian for quite a few years, and the release cycle of Debian Edo was sort of laid back in comparison to Debian. So at the moment it's like there is the first release of the next Debian stable, and with the first point release or the second at the latest, you have the Debian Edo system ready for installation. So I guess that's Holger and Wolfgang, who mainly earned the credits. So if you want to clap your hands, that's the point you should do. Thank you. And also over the years that I've sort of either contributed or observed the Debian Edo development, it's really like chasing a move and target all the time. So it's quite a hard job to actually sort of follow up what is changing in Debian over a release cycle.  So technical plans for development. So at the moment on the school level or the municipality level in different counties all over Germany, you have the challenge or they actually give the challenge to us to not set up an insular school system at one school, but they want to actually connect their schools, have a user identity management system across the municipality or even across the county. And we are not ready for that, but we are looking at it on a technical level to actually hook different scholarly nooks installation together, either with another scholarly nooks installation as a master server or a dual master server setup. Or what is quite common at the moment is that the Univention people with their UCS at school product, they actually have their foot on the county level. So the educational ministries, they sort of get in touch with Univention, get to know the UCS server and start deploying an identity management system for teachers and students. And they are interested and we are interested actually on hooking those two systems together, like the scholarly nooks system being a slave machine for a county-wide or municipality-wide UCS. Anything else development-wise that we should mention here? Okay, yeah, good. Thank you. So there is obviously one group of people who are interested in the scholarly nooks as users, which are teachers, and you know how that goes. Teachers have lessons to do, they have to prepare lessons, they run their lessons, and sometimes they want to do those new shiny, digital things. Sometimes they even do and don't only want to do. The most important thing for teachers is that they are offered a system that simply works. There's no time for digging around in the system, making something work, trying endlessly whether something will be fine at the end. They just want to basically enter the computer lab, sit a class down, boot machines, have them lock in and the system works. And sometimes there's quite a big difference between what developers think works and what users think works. Sometimes there's even a bigger difference in what teachers think works. So one of the important things at the point where we as a developing community come together with schools is finding out what they want, what issues they have and tailoring the system to the special needs. Yeah, students are using the system as well. They are mostly used to Windows desktops or sometimes macOS or their smartphones. There are some issues that are new to me, that there are new issues every year. I work with many children and adolescents in different projects. I think one interesting story is that 10 years ago when I started, or more, 12 years ago when I started giving lessons to children, there was half of a classroom that did not use a computer on a regular basis at home. Then two years later, everyone was using a computer on a regular basis at home. And they all knew their way around. Basically, they could use a mouse and a keyboard. They could basically write text in an office application. And in that regard, there was basically a refinement of this knowledge going on. And for a few years now, I have been stumbling across students in fifth or sixth grade or something, and they are about to type something on the computer. And they tell them, yeah, there are spaces missing in your code. And they ask, what is the space bar? And this is because they are used to their smartphones. They type in word, and it's just auto-correction and auto-completion. And the user interface is changing. And maybe this is also a difficulty that will become bigger. And in that regard, and in some more regards, it's very important to also listen to students when it comes to a system deployed and used at schools. So Mike will tell you about... I guess that's me. Well, that's the goal. Okay, so about the support system. IT at school used to be something for one or two teachers. If you were lucky, there were two of you who could set up a network, one server and some workstations. I think this time has passed. IT at schools, you wouldn't fix your sync. You would call a plumber, so if the computer lab is broken, normally these days you call an IT company. And promoting Scholar Linux in Germany means that there needs to be, at some point, we need to be really quick setting up a support structure. And in former times there was a Scholar Linux certificate. So if you wanted to become a Scholar Linux admin, an administration company, or admin on a personal level, you had to obtain your certificate. And there were coaching, consulting sessions, training sessions for those companies providing professional support. We are thinking about reviving that. We have, in Northern Germany, we have a couple of schools, and there was one company that was doing first level support that was supervised by me. And at the moment there are more companies coming in, and I am about to start a third level support concept so that you can book my company, or actually myself as a person, to support Scholar Linux. And I think that we need, if you want to facilitate people using free software at schools, we need to consolidate that on a highly professional level, in fact. And this is something that you might be talking in two minutes about as well. Yeah, so we need to think about professionalism here, and get people into Scholar Linux in terms of how to configure that system, because it's not Debian, it's different. I think it's for you. Yeah, that's me too. Brief history. So I came to Scholar Linux in 2011, and that was at a quite, well, it was a quite difficult point of time. Actually, there was a county-wide solution in one of the more southern counties in Germany. That's sort of where the overall result was. It failed because there was a lot of work done for Scholar Linux, or with Scholar Linux actually, and it ended up with none of that being upstream back to Debian. So, and we also had the observation that there was the international team, which was like the developers hanging around on hash Debian Edu on IRC, and on the Debian Edu English mailing list. And there was a, say, marketing group in Germany at least, not sure about other countries, that attended Linux days and set up a booth there and talked about Scholar Linux. And these two groups, they sort of got actually, they diverged. Well, there was a lack of communication at some point. And I think here, and also with previous meetings, we are about to change that because the Scholar Linux representation in Germany has just been moved over to the Techids NGO in Germany. And we have two developers, Debian developers there, one Debian Edu developer, which is Nick, so that what was sort of split apart a bit, has been split apart, is now actually rejoining again, which is really, really good. Okay, so, at the end of last year, yeah, things, I have to be honest regarding the use of Scholar Linux, Debian Edu, and publicity, the community support, community meetings, everything looked very sad. And that's why we start an effort to revive some things. And what happened this year, I'm running out of time, so I have to hurry a bit, sorry. The community coordination was transferred to an NGO, Techids, some of you might hear about it, there's also a lightning talk scheduled for later. And we care about many things concerning education and free software. And what we want to do is, what Mike already said, we want to bring these different groups, the users, people who support the system, people who present the software, the project at conferences and developers in Germany and other countries, we want to bring them closer together, which has started to work quite well. There have been meetings in the past in Norway that we attended and here at Depconf, and yeah, there's quite a bit going on. We have updated the website, there's a nice landing page for people who are looking for a product, because that's maybe one important catch word, because teachers or people who make decisions for the use of software in schools, they don't want a wiki and a mailing list and something they want to find a product, something that looks like a product. And that is one thing that we want to move the presentation to. And we started doing surveys with the companies who once offered support for DM&EDU and with some schools, how they use it, which is in quite an early stage. I hope that we will be able to present successful details about that at a later conference. Yeah, okay. For those of you who are interested in education and children and free software in general, you're welcome to attend the lightning talk where I will give a few details about what TechEds does together with children. We are very eager to integrate children in the free software community. Normally there would have been some kids around here as well, but this didn't work out because the travel and school and all of that, if you're interested, just attend the lightning talk. Okay, just a picture of all the children interested in Debian and free software at some conference. Okay. So, a goal for 2018, bring all the groups together. On a technical level, do we want to say repeat some words again? Yeah, so one goal I forgot and one person I forgot to mention is Phil Hans. Phil Hans is a DD as well. He is here at the conference and he's trying to get the Debian Edo installer into the Debian installer sort of workflow thing so that we actually, we used to build the Debian Edo ISO images on a separate machine and it was always a bit tricky. So we hope for the Buster release that we have that in the Debian installer build environment and that Debian builds the Debian Edo installer CDs. So that's one release, one goal for 2018. Another goal is that we want to promote the remote desktop part in Debian Edo a bit more so that one outstanding feature that other products at the moment don't have is that you can remote log on to your system from home as a teacher and test what you want to do in class in the evening before on the same system hardware software, especially compared to what you have in school. So that is one issue that's always breaking. They prepare stuff at home and have different network conditions and different operating systems and then they are in class and then it takes 15 minutes to set up what they want to show or it doesn't work at all. So that's one thing, remote desktop that we will integrate and it's an outstanding feature not available in other products at the moment. Anything else? We want to secure the file system level a little bit more. We will not talk about that here but there needs to be some work done. Okay, it's not yet ready. It needs work. Okay. So, most important thing every project always needs help both in the development parts but also in the community efforts. We need people who are interested in doing some support in promoting Debian edu in school news and even free software and education in general. If you are interested in that maybe just talk to one of us today or whenever you like. There are two mailing lists. One for the Germany efforts which is more support and community coordination and less development and the general English speaking this Debian edu is more about development and technical stuff. Also about support. If you are interested in the community side and the presentational side and promotion and community you will find details about how to contact TechEd on the TechEd's website. So, there are many ways you can help. One word about the German mailing list. We made the observation yesterday that the technical level on the mailing list was a bit, well, there were questions and no answers or people answering other people that did not really have insight in Debian edu which is okay but what especially I put on my agenda is that I provide support on the German list a bit more than I used to do so that we actually raised the Nivo on the mailing list and also the frequency of posts and the response time to posts from users. And the other thing is that it's also the channel for organizational stuff around ScholarInux now. So, and that's what Nick is doing a lot. For more ways how to support free software education in general, again a reference to the lightning talk and so times over I think so thanks for attending and listening and have a nice meeting. Are there questions or comments? Just one comment which is not so much about Debian edu but about other blends because we tried to build a Debian edu installation image and then that we might create the traditional way but the other questions unsolved from the stretch release is having a blends installer CD in Debian. There was some effort but then it was too late because it was frozen. Buster freezes in half a year so we have half a year to make blends installable in Debian. If somebody is interested please pick it up. So that is blends in general, all blends, okay. Any further questions? Yes, a general question in which countries is currently ScholarInux used nowadays because I know that there are some other at least there are some other educational institutions that were created years back. I don't know the status in different countries across Europe is so currently where is ScholarInux actually used more in Germany, Norway, other countries also? That's a good question in fact. There was on the old ScholarInux website there was a collecting database and schools could put their names on and then they didn't remove their names and in fact that's something we should put on how to do this to get an overview here. There are a few schools in other Germany that I know of. A few schools in North America's failure some in Rhineland files probably some in Oslo I know or they do like they learn from the techniques in Debian Edo but use a plain Debian system to set that up. So there's a lot of school and Linux stuff going on in Greece at the moment. So I guess did a talk at the Debian Conf in Montreal which was really impressing there. Lots of dots on the map were Linux with LTSP and Debian with LTSP was deployed. So, but I can't really answer your question. I'm sorry. So same kind of question about the support and the organization around ScholarInux. Do you know of any other kind of organization like the one you described like your company who is doing support in Germany and take kids? Is it German also? It's an NGO, yeah. So I'm doing that really as a business thing so you can contract me for third level, second level, first level support. The first level support I will hand over to a partner company. At the moment you do it on an NGO level so you sort of write invoices to the schools and then you use it for the association or for well maybe also have contracts with individual persons. If you're a school you can also contract or address me if you want a certain feature and you want that in Debian like a new software that is free software but not yet packaged then it's also, it could be an idea that you address me, either I package it for you or I know someone in Debian who's already working on that and we sort of, you know, dispatch that. Also there are some companies on the Wiki so maybe you want to mention them. There is, on the Debian Wiki there's an area for Debian Edu which is also linked on the German school news website and I think the school news NO and schoolnews.org point there. There's a list of professional support with quite a big list of companies all over the world who do support for Debian Edu. We are just working on cleaning on tidying up the list for the German companies because it is very outdated. I think this is also true for the other countries. We know of three companies throughout Germany who gave us feedback that they are or will be actively supporting the system on a professional level. The problem is if you have been a Scholar Linux supporter like four years ago and then now someone approaches you and says I want Scholar Linux support it's a completely different system different user management now with Kerberos blah blah blah I really want to invite those companies that want to support Scholar Linux to get in touch with firstly the developer group on the Debian Edu English mailing list or contact either of us and see how action interaction can happen. Just doing it and then promising Debian Edu will work for a certain customer and then it fails that it's not good for the project reputation. Okay, sorry, time's up. Thanks again.


All right, so welcome everyone. Welcome, welcome. If you're watching this on YouTube or if you're watching this on Twitch, welcome to lecture number nine, I would say, from the top of my mind. It's in the description, but I can't see the description myself at the moment. But yeah, today we will be talking about primer design. And primer design is one of these lectures that I love, especially because of the inventor. But of course, first, let me pull up my sheets and let me show you guys what we will be talking about. So the first thing that we will be talking about is polymerase chain reaction. And I will be talking mostly about what is a good primer, when is a primer and primer and some advanced primer design techniques, which I really like. So we will be talking about things like multiplex PCR, where you amplify not one piece of the genome, but multiple pieces of the genome, talk about things like universal primers and gasmers. After that, in part two, we will be talking about databases like genome browsers and basic functions of ensemble. Let me actually fix the typo.  Ensemble. That's it. All right. And then how to find a genomic location, export your sequence and then use a computer to the primer design, because of course, you can design primers by hand, but it's much better to have a computer do it for you. But of course, first, like always, we will have the answers to the previous assignments. So let me pull up the assignments. And the assignments is, of course, from lecture number eight, the phenotypes and QTL mapping lecture. So I actually forgot to put it on Moodle and also on my website over the weekend. So I only uploaded the assignments on Monday. So I hope everyone still had enough time to at least look a little bit into the assignments. Again, we were using R. So it's kind of a follow up to the R lecture, right? So to kind of have something to work on. So let me open up the R window for you guys and let me open up plus plus so that we can more or less switch to and from. Okay. So during this lecture, we will be using two different files. So one of the files we already saw. So one of the files was this file, which we already saw during the phenotype lecture, I think. And this has 24 different metabolites measured. And metabolites, of course, they come in a wide range, right? So you either have almost none of the metabolite detected or you have really high amounts. And of course, this varies for different individuals in the same population. So here we have a data set, which has in total got 162 individuals. Of course, some of them have nothing measured. And we have 24 different metabolites that were determined using mass spectrometry. Besides that, we have our genotypes. So as you can see here, our genotype file is nothing more than a whole bunch of markers. So PVV4 is the name of a marker. But we also have GD86L as a marker.  So a little bit more about the data set. I don't think that that was in the assignment, because in the assignment, it just assumes that you know what a genotype file is. But of course, this is a cross in Arabidopsis, which was made by Columbia, which is one of the ecotypes of Arabidopsis, which comes from Columbia, of course. And between Landsberg Erecta, which is a different ecotype, which I am not exactly sure where it comes from. I should know, because I did a lot of Arabidopsis work, but I forgot. But anyway, so here we have these two files. So on the one type, we have the phenotypes, so the metabolites that we measured in the different plans. And we have these genotypes, which at every marker in the genome, so we have a couple of 100 markers, I think, has a determination if for this individual, the piece of DNA came from Landsberg Erecta, or if it came from Columbia. So I think one is Landsberg, and two is Columbia. So individual one at the first marker on chromosome one has a one, which means that this part was inherited in this individual from the Landsberg Erecta. Well, for example, individual three has a two, which means that they have a Columbia origin for this marker. So first things first, I'm just going to read the question. So for this exercise, we use the phenotypes, which we use before. Additionally, we need the genotypes file. First, we want to find the amount of missing data per marker, right? Because if a marker has a lot of missing data, then we probably don't want to do the association with this marker. So we just want to throw it out. We can use the is.na function for checking if a marker has na's and how many na's there are. So the isna function will return a list of true and false values. We can sum up this logical vector to get the amount of missing data. Using a for loop, we can loop through the markers or individuals in our genotype data, and using the c function, we can create a vector of missing data. In this example, we need to add our own code to compute the amount of missing data. So there's just a standard kind of little example of you guys to start working with. So the idea here is is that we first load the genotypes and phenotypes. So let me do that. Of course, if you start an R file, always add a header, right? So again, mention that this file is containing the answers to the assignments of lecture number eight. It was written by me. And of course, then the first thing that I do is move somewhere on my hard drive where I have stored my files. So as you can see, these files are on my one drive. And then I just load in the phenotypes and I load in the genotypes file, right? And in this case, because when I look at the file, I can see that they have a header, right? Because the every column has a column name. That means that I have to specify that in the call when loading it in. Furthermore, if we look at the file, right, we see all of these little yellow arrows. And because of these little yellow arrows, that means that they are tab separated. So and tab is specified by slash t. So let's do this, right? And let's go to R. So let me open up the R window for you guys. I need to move it out of the way for myself. But just let's load them in, right? So if we want to know how many genotypes we have, right, we can just ask for the dimension. So the dimension of the genotypes is 162 rows, 117 columns. That means that there's 162 individuals and 117 markers that have been measured. I always look at a little piece of the file just to make sure that everything was loaded in correctly. So I generally just say, well, from this matrix, select the first 10 rows, select the first 10 columns, and print those two screen for me. And that all seems fine, right? There's row names, there's column names. So it kind of understood the file. Let's do the same thing for the phenotypes, right? So if we look at the first 10 rows of the phenotype, we see that it seems to be loaded incorrectly, right? If we would go back to notepad and we would go to the phenotypes, then we see indeed that there's x3 also. So it seems to be all perfectly fine. All right, so then the first thing was is to figure out the amount of missing data, right? So let's go back to notepad. So here we just say missing data, right? So we need a variable to store this. So we have for every marker or for every row, we are in this case for every marker. So for every column of the genotype matrix, we want to figure out where or how much missing data there is. The annoying helicopters are back. I hope it's not too loud. I don't have the window open, but they're flying like really close over the building. So I hope it's not too bad in the back. But tell me if you can hear them. Good. So it's just me. Like there's just helicopters in my mind, probably. You're nothing. Okay, okay, then that's good. That's good. All right, so what we do is we say missing data is nil, right? Because initially we just want to create a variable and this variable holds nothing. And then we just say for x in one to the number of columns of the genotypes, we could have also just used the column names, right? Because every column is a column name. But in this case, I'm just going to use the standard for x in one to the number of columns. So x, first time that we go through the loop, we'll have the value of one, second time it will have the value of two. So what do we do is, okay, so we do a one liner. So the first part I gave you, right? So I'm saying that the new missing data is a combination of the missing data that we already had. So the first time that we go through it, it would be would be empty. And then we need to calculate the percentage of missing data. So the percentage of missing data is actually quite relatively easy to calculate, you just say, well, take from genotypes column number x, that's this part, then ask is na, right? So this will give you back true and false for every marker or for every individual, right? Because we're looking across a single marker. So if one individual is missing, it will say false, it will say true, because we're asking, is it missing? And for individuals that have a one or two there, it will just say false, right? And since true is one and false is zero, we can just sum this up. And then of course, we need to divide by the length to get the percentage. We also need to multiply by 100. So this is just calculating the percentage. And it then directly adds it to the missing data vector that's growing with each loop. And then of course, we want to plot this data and to kind of show that how much missing data there is per marker. Of course, we need to give it a y label and an x label and a main. So on the x label, we say marker. And on the y label, we see a percentage. So let's just run the code, see what happens just so that you guys can see the nice figure. And so you can see that this data set was actually genotype pretty well, right? We see here the 120 markers. And we see that on average, most markers are 100% genotype. There is not no 0% missing data. We see that for some markers, there's like 3% missing data. But this is all perfectly acceptable, right? If generally, I put my threshold for missing data at like 5% or 10%. So if more than 5% to 10% of the individuals is missing, then I kind of throw out the marker because there's too much missing data. But of course, this really depends on what kind of a data set that you're working with. But for QTL mapping or for genotypes, because these genotyping arrays are really good, having more than 5% of the markers be not determined or missing is generally an indication that something is going wrong at that marker. Alright, so this is the missing data per marker. We also ask for the missing data per individual. And of course, this is the same thing, right? It's just that we now go not through the columns of the genotypes, but we go through the rows because the individuals are in the rows. And the only thing that changes is this part here. So instead of taking the column from the genotypes comma x, we now take the row. So we say genotypes x comma. And for the rest, the code is exactly the same. So we can then just copy paste the code in and then show you guys what happens. So then it looks like this, right? So now we see that most individuals have between like 0 and 3, 4% missing data, there are two individuals which have a large amount of missing data. So these individuals 7 or 6% of the data is missing. And then this one 7% of the data is missing. So we could throw those out. But we have, of course, when we throw them out, we have to remember to also throw them out of the phenotype file, right? Because otherwise the phenotype file has 162 individuals and the genotype file only has 160. But so we have to keep these two consistent with each other. All right, create two plots. Use the main xlop, okay, calculate the average percentage of missing data in the genotype matrix. So that's up to you guys to do. I didn't do that assignment. The average percentage, of course, is nothing more. Let's do it, right? So let's go back to R. So let's run the code for the missing data per marker. It shouldn't matter that much, right? But now when we take our missing data vector, we have for each marker, we have the percentage of missing data. So if we want to have the average of this, then we would just say mean, and then it would calculate the mean. So on average, there's like 0.4% missing data, which is really good for for genotyping. And kind of what you expect, because nowadays, snip chips are pretty good at determining that. All right, so then the next step is to do a basic effect mapping, right? And this is already really hard, because a basic effect mapping means that you have to take the genotypes, take the phenotypes, align them to each other, and then do a t test between the two groups, or calculate the mean between the two groups, right? So it's a lot more code. I gave you a little bit of code. So the code that I already gave in the assignment was this part. So I gave you the kind of the means one means two. So how, because we want to calculate the mean of the first group, so the Columbia group, and we can want to calculate the mean of individuals that got the Lansberg-Recta genotype, of course, we have to go through each of the markers. And then this part in the middle was the part that I wanted you guys to write. So how does this code work? Well, we go through each of the columns of the genotypes, right? And then when we're looking at a single marker, I'm just going to ask which individuals, so which of this marker, like genotypes, is is one, so has an origin of Columbia, and which genotypes comma x is is two, which means take the x column from the genotypes and then see which individuals are two. And then I just store this in two variables, right? So I take a variable which is called E and D in one, so individuals in one or individuals who are one, and E and D in two individuals who have an origin two. And then I'm just going to calculate the mean, right? So I'm going to say take the mean of the phenotypes of the individuals of one, and then just do x, right? Not one, and do x here as well, right? So from the phenotypes matrix, oh no, for the first phenotype. Yeah, so just for the first phenotype, so not x, so just for the first phenotype, right? So from the phenotypes matrix, I take the first column and then I ask individuals in one, so take only those individuals out of the matrix and then calculate the mean, make sure that you remove an ace, right? Because you cannot calculate a mean when there are missing values, because then the mean will also be a missing value. And we didn't look at the missing values in the phenotype, so because I don't know, I'm just going to make sure that I'm when I calculate the mean that I ignore the missing values. And then of course I just add this to my means one vector. I do the same thing for the second means, now I just use the individuals in two. I hope that's clear, like it's just a kind of basic selection from first matrix and then using the selection in the second matrix, right? So when we run this, then it will give us the means one and means two. So let me show you guys the R window again. So when I run this, it's very, very quick. But now when I look at means one and I look at, for example, the means of the first 100, not 100, first 20 markers, right? Then I see that at the first 20 markers, the average of the individuals who had genotype one, so genotype from Colombia is 3,543. If I look at means two, right? At the first 20, then I see that, oh, that's interesting. The individuals who had a Lansberg erecta allele at this marker, at the first marker actually have a mean of 5,000. So much, much higher. Good. So now if we want to visualize this, we can do this in two ways. But the easiest way is just to plot the difference, right? Because if the difference is big, then we expect there to be something in the genome which is controlling the difference in the phenotype. So let me make that plot for you guys. So when we do the plot, then it looks like this. So we see that at the beginning that there's like a minus 1500 for the first marker, which we just computed. But you can see that across the 120 markers that we have, there are two, perhaps three regions which are relatively interesting, right? At these regions, we see that like the one group is almost 5,000 units higher than the other group. Here there's almost 6,000 units increase. And here there's like 2,000 units increase, right? But we still don't know exactly what is going to be significant, right? Because we only know that there is a difference, but not how significant this difference is. So just a very basic plot. We could actually add the values themselves. So if I would say points, and then say means one, right? So if I wanted to add the means one value, and I would just say color is, for example, green, and then comma LTI for line type is two. Then here you see in the little dots you see that the value of had the first group. And of course we could do the same thing for the other group. If we look at the second means, and then we could see if there's a big difference, right? So we can see here that there is indeed a difference of like minus 1500. And here we can see indeed that the green group is much, much higher than the red group. So there's something at this point, which is segregating, which is linked or seems to be linked to our phenotype. Good. So let me switch back to notepad. All right. So then the next thing that we wanted to do is just do a basic TL mapping, right? Since we have only two groups, we can just get away with using very, very basic statistics, right? Because the very basic statistics that we can do is just do a t test. If we have two groups, we can just say, well, I do a t test and I compare group number one with group number two. So again, I do the same thing as I did before. So I go through each of the markers in the genotypes. So since the markers are in the columns, I'm going to one through the number of columns. And I define a new variable called p-vales, which will store my p-values, right? So it's just an empty vector at the beginning. And every round or every time that I go through the loop, I will add one p-value to it. And the p-value is nothing more than saying, do a t test between, well, from the phenotype matrix, take the individuals who are one in the genotype matrix and then take the first phenotype and then compare this to the phenotype matrix, where I select the individuals who are two and then take the first phenotype as well. And then do dollar p-value. So this gives you the p-value associated with the difference. And then of course, when we make a plot, then of course, we don't plot the p-values. And I will show you actually why. So let me just run this in R. So I'm running this in R. And I would just plot p-vales, right? Then I would get a plot which looks like this. And of course, I cannot see the difference. Like, is this significant? Right? I can say upline, give me a horizontal line, which is at 0.05 divided by the number of markers, right? Because that is my significant threshold. And then you see that there's a line here being drawn. But I can't see if these dots here are below the line or if they're above the line. Right? So that is the reason why we use this minus log 10 transformation, because by using the minus log 10 transformation, if you have one times 10 to the minus five, it becomes five. So it becomes really visible, right? And 10 to the minus three becomes three. So hey, you can see the difference between 10 to the minus five and 10 to the minus three. And here you can see the difference between these values, right? Because have you know that this is like one times 10 to the minus one, zero point or 0.5 times 10 to the minus one. But what these values are, you can't see because they're all squished together near the zero p-value, right? So when I do the plot and instead of taking the p-values, I now take the minus log 10 of the p-values, right? And then I see all of a sudden this. So I see that these values here, which used to be all the way at the bottom, are actually very significant, right? So it's 10 to the minus seven and 10 to the minus 15, 10 to the minus 16. So those are really, really significant differences. And of course, I can again add my line, right? So if I wanted to add my significance line, which is 0.05 divided by the number of tests that I did, because I have 120 markers, I can now just do a minus log 10 of that as well. And then I would say that color is green, right? Because everything above the line is significant and everything below the line is not. So what we see here is that these two regions that we now identify, they are significantly associated with the difference in our phenotype. So we just found our first two QTLs ever. And so now, of course, the next step would be is to go to ensemble, see where this peak crosses the line, right? Because this is the interval where we expect the gene to be in. And then we would go through ensemble, we would download all of the genes in that region, and then either use literature to kind of filter down which genes there are, or use some other strategy like looking at gene expression of these genes to kind of see if we can find a gene where the expression of the gene correlates with the metabolite that we're interested in. Of course, this is a very basic plot. This plot can be improved a lot, right? There's no header at all. Here it just says minus log 10 p-vales, which might just be better to say lot score. Had this axis here is a little bit weird because the numbers are turned. But these are all little things that you can easily fix in R. And that just takes a little bit more time. But of course, the results won't change because of that. That's why I like R. It's really good for like very quick, very basic data analysis and exploration. All right. I think those were all of the assignments. Yeah, so not a lot, but I hope everyone was able to at least get it working. I hope everyone found the assignments on time, not that people looked on Friday morning and thought, oh, there are no assignments. So that's all fine because they just forgot to upload them. So in case that you are actually looking on Friday morning at Moodle and seeing, oh, there are no assignments, send me an email so that I know that I should upload them. Like sometimes I forget, right? I do the lecture generally after the lecture. I go home and eat pizza. But I tend to forget when I come in on Friday morning to upload the assignments for you guys. So do send me an email if you don't find the assignments. And then if there are no assignments, you get an email back saying that there are no assignments. So just relax. And otherwise, I will directly upload them. Good. All right. So that was it for the answers to the previous assignments. So let's go back to the overview because I wanted to do a shout out. And that's a little bit interesting because you guys might remember me doing this drawing last week or the week before. I think last week, right? So last week, we had Pastor Sauras redeem some of his channel points and asking me to draw a puffer fish. So actually yesterday when I was sitting in an office, one of my colleagues came working in and she said like, oh, my daughter watched your video and she got inspired and also wanted to draw a puffer fish. So this is the image that I got from her.  I have it over here, right? So I can show it on cam as well. I'm really, really happy about it. So yeah, definitely if you feel inspired by me drawing and think I want to draw as well, then do. And just send it to the Humboldt University under my name. And it'll be fun. Like I love getting these drawings. So I was pleasantly surprised. I wasn't expecting anyone to be like motivated by my drawing. But apparently someone was and I really love it. Like it looks a lot better. Like the eyes are really good and it's a lot better than my horrible, horrible puffer fish. So yeah, if you think you can do a better job, then definitely do and definitely send it to me because I love getting those things. All right, back to the lecture. Polymerass chain reaction. So, Keri Banks-Mullis, one of my all-time, all-time, all-time favorite scientists. And the reason because of this is, is just because of his kind of life story. And he invented Polymerass chain reaction. So I think everyone knows what PCR is for, right? If you feel sick at the moment and you have to go and do a test, right? So you do a home test and the home test says, well, you might have COVID-19. Then of course, you are going to go to either the Gazuntites Amt or however it's called in your country, like the RIVM or whatever or the GGD. And then you do a PCR test, right? And PCR tests are more or less one of the most sensitive tests that we have to determine if a certain sequence of DNA or RNA-based pair is occurring in a sample that you took. It is one of the most valuable techniques. So it was invented by Keri Banks-Mullis in 1983. And it is, like I said, it is one of these techniques that, after its invention, quickly revolutionized the world of molecular biology and also the world of bioinformatics, right? Because using PCR, we are able to determine things like genetic markers, right? So for QTL mapping, what we just did, we need genetic markers, right? And since we need genetic markers, we need a way to determine these things. And the only real way of doing that up until like 2010, I think, when we got CASP, so competitive allele-specific PCR, which is a kind of update of the PCR technique, you could only use PCR, right? So it is used in cloning, in phylogenetics, in gene analysis, in genetic fingerprinting, like police officers use it when they find like a blood trace at a crime scene. And in 1993, Keri Banks-Mullis got the Nobel Prize in chemistry. But the invention story is really interesting because Keri Mullis was a big fan of LSD, having grown up in like the 1960s, the 70s and the big like sexual revolution and all of the drug that came with it. He was an avid user of LSD and working for the company, actually, there's this really, really good book, right? So he wrote Dancing Naked in the Mind Field and this book is his autobiography where he explains how he came up with the idea for PCR. So he has always claimed that PCR was given to him by aliens. So that on LSD, he was, he had like a bad trip, but in his mind, his belief is that he encountered extra terrestrials and these extra terrestrials according to him looked like fluorescent raccoons and they told him how to do PCR. So he doesn't claim any part of the invention for himself. He just says, no, one night I was just sitting at home and encountered some extra terrestrials looking like fluorescent raccoons and they told me to do this and that would be like a revolutionary new technique. Very, very interesting guy. He is also one of these people that has a lot of controversy on his name. Like the first paper that he ever wrote and if you guys are interested in this paper because I have a copy of it, I can put it on Moodle or on my website. I don't think I can put it on my website because nature is actually quite, quite stringent about his first paper because it's one of these papers that is very pivotal. But the first paper that he ever wrote was published in Nature in 1968 and it is called Cosmological Significance of Time Reversal. So it is a paper in which he outlines his ideas about how time and how time can flow in multiple directions. So not just forward like we all kind of experience every day but he believed in that time travel was possible and that you could kind of reverse time in several different ways. Besides that he wrote like three papers about PCR and then he actually wrote his book Dancing Naked in the Mind Field. So if you are looking for a good book to read over the Christmas holidays, definitely pick up this book. It is an amazing book with all kinds of LSD references and he was a big fan of papers still on Nature. Yeah, but you have to have like an account to get it and so if you're not at a university you have to get it. So if anyone's interested then I can just put it somewhere for you guys to download. It's a very short article. It's like a one and a half page read where he explains his ideas about time reversal and he's just a very, very interesting guy and his whole life story is super interesting. But he is generally credited as being the guy who invented PCR although I think in 1999 courts overthrew his patent. The nice thing is he got screwed massively by the company that he worked for. So the company that he worked for was called, let me look it up so that I don't say it wrong, so the company that he worked for was originally called Cetus. So when he was working at Cetus he invented PCR or not invented because aliens told him that he should do this and that was a good idea. But then he got a bonus. So he got a bonus for inventing this massively important molecular technology so he got a $10,000 bonus which of course in 1983 is not a bad thing but still a couple of years later the company actually sold the patent to Roche and they made 300 million just from selling the patent on PCR. So he got screwed over royally until like 1993 when he got a million dollars from the Nobel Prize committee of course. But still it's really interesting and the whole like lawsuits that followed because another company also wanted to buy the patent. They were unsuccessful so then they sued Roche for saying well this patent isn't valid because there's previous work but yeah he is generally kind of considered to be the inventor. There's some other people who did a lot of preliminary work but he came up with the thermic cycling for PCR so really really interesting person and definitely worth reading. I actually love Nobel Prize winners because most Nobel Prize winners after they get a Nobel Prize they go a little bit crazy but he was already like out there in the left field way way before he actually started publishing and doing his big inventions. All right so Polymer has a chain reaction let's get into it. What do we need? So we need template DNA. We need a sample so you need to take a paper cup from somewhere, steal a paper cup and then see if it has COVID-19 in there or not. So you need template DNA. So template DNA is the DNA from which we want to amplify a piece. We need water. Water is an essential part of PCR. We need to be able to do precise thermal cycling and by precise I mean thermal cycling. So it used to be that the way that people would do PCR is to have just three kind of buckets of water and these three buckets of water had different temperatures and what you would do is you would take your cup with your substances in there and then you would put it into the first bucket and you would just hold it there for like 30-40 seconds. Then you would take it out and you would put it in the second bucket and then you would wait again for like a minute and then you would take it out and you would put it in the third bucket and then you would wait a little bit and then you would take it out and then because we did not have heat stable polymerase in 1983 yet, you had to add polymerase to all of your samples again and then you started over. And that was the whole technique. So we need heat stable polymerase. Nowadays we always use Tuck polymerase from Thermos Aquaticus which is one of these extremophiles which lives near these like warm water sources in the ocean. We need nucleotides so just free nucleotides so A, C, T and G because the polymerase needs this to extend the DNA so you have to have nucleotides in excess and we need something which is called oligonucleotide which we call primers. So the whole lecture is about how to design primers and what is a proper primer.  StockFishO22, thank you for following. And of course for PCR to work you need an unlucky student because as soon as you've got anything more than a master degree you're not going to want to do PCR. You just have someone else do it for you right because you're not going to wait for it for three hours just have a student do it right students can pipe it. All right so how does it work? So I told you that you need three buckets of water right and the first bucket of water is supposed to be at 90 degrees Celsius and at 90 degrees Celsius so you take your mixture right so you mix all of these components together your template DNA, your H2O, your taco polymerase, your nucleotides and your primers so you just all pipe at this into a cup and then you throw the cup at 90 degrees Celsius for around one minute. So what happens when you bring DNA at 90 degrees Celsius? Well DNA since the molecule is more or less a double stranded molecule when you heat it up the hydrogen bonds that hold the two DNA strands together they kind of dissolve and the DNA opens up right so you get two single strands of DNA instead of having a double helix so and that takes around a minute right so if you if you have a cup with DNA you heat it up to 90 degrees Celsius then a minute later you have single stranded DNA. Then the next step is the thing that happens at around 54 degrees Celsius and this of course is dependent on the length of your primers but generally like 54 degrees Celsius is where it happens so that because you have all of these things in the mixture when you when you cool this mixture down to 54 degrees Celsius the DNA starts to kind of rebind together but of course the primers are much smaller than the whole template DNA strand right so the primers will kind of quickly bind to the DNA because they are they are just more efficient at binding so the primers will start binding at the DNA and then the next step is to do the extension so the extension is the part where the polymerase binds the double stranded DNA question with RNA do you skip step one no with RNA you do an additional step so you first do a cDNA synthesis step so you take your RNA then you use reverse transcriptase which turns RNA into DNA and then you just do it like you would have DNA and that's generally the way that we work with RNA the way that you work with RNA is just turn it into DNA and then you can use all of the molecular techniques available to DNA all right so in the third step right so we've opened up the DNA we then cooled it down a little bit so that the primers have the ability to bind and then we heat it up to 72 degrees so what happens is that the polymerase binds the DNA and starts extending the DNA just base pair by base pair so hey it just binds here and then it goes forward and every time that it moves a little bit forward it it finds the right nucleotide and incorporates that so it makes double stranded DNA again so instead of having one copy you end up with two copies right so we had well you had two copies of course but instead of having two copies at the end you actually have four copies of the same so PCR gives us an exponential amplification of the target region right so imagine that we have here this little piece of DNA of interest right that's the red part so here we have the whole chromosome and this is the part that we're interested in so the first thing that we that happens if you have one cycle instead of having one copy you get two copies so two to the power of one the second cycle will give you two to the power of two is four copies and so on and so on so generally when we do PCR then we amplify it like using 35 cycles right and 35 cycles means that instead of having one copy of the piece of DNA that we want we actually have like 34 billion copies and this is a lot so this is something that you can see on a gel right so when you when you have a single copy then this is very you can't see anything right but when you have 34 billion copies of a piece of DNA then you can more or less see it on a gel so if you run it through an agarose gel then then you can really see the DNA on there of course this is not exactly what happens because when we look at the first four cycles in detail what happens is because we so we have our template DNA right so we make it single stranded and then what happens is the primer binds the amplification starts but in the first cycle we don't get any of the product because the product is still too long right so we start from the five prime to the three prime DNA is always synthesized five prime to three prime the same thing happens with the other strand of DNA but of course you can imagine that the polymerase doesn't know where to stop so it'll just continue copy and copy and copy so in the first cycle we have no real product of interest in the second cycle the same thing happens right because we now have this kind of longer piece here so this is the green piece with the little piece that we want which was just amplified from left to right and only in the third cycle because now when the amplification starts now the other primer binds because you need a primer for the forward and for reverse so only in the third cycle do we see that we get the first two products which have the exact length that we want and then here in the fourth cycle we go up to eight but from then on it again does exponential so you need to do at least three to four cycles to amplify DNA using PCR so this is kind of how we generally schematically think about it but what happens in real world is that since the polymerase doesn't know where to stop the first fragment just continues on copying until the end or until the it cannot copy anymore and then we have to wait for the next cycle to have the reverse primer go in so it's a it's a little bit more complex in detail so what makes a good primer right because these oligonucleotides the 20 base pair long pieces of DNA which we use to target which sequence we want right because a polymerase can only bind to double stranded DNA we need to have this little piece of like 20 25 base pairs which targets the DNA that we are interested in so what is a good primer well a good primer means that it lacks secondary primer sites so that means that if I have this 25 base pairs of DNA it can only fit in one position in the genome and not multiple it also needs to have a melting temperature between 52 and 65 degrees so we cannot make this piece too long right because the longer the piece the higher the melting temperature because the higher the temperature that it needs to properly uh kind of bind to the art to properly bind to the DNA right so if we would take a primer which is 70 base pairs long then the melting temperature would be around like 80 90 degrees Celsius and because we have these three temperature points at which we do PCR right so we we start off with the first step which is the denaturation then we have the annealing so if the annealing temperature is too high right if the annealing temperature is around 72 degrees then of course the annealing doesn't happen properly and the extension already starts before the annealing part so have we we always need to make sure that when we design a primer the primer has kind of a melting temperature and we will talk about melting temperature what it is in detail but it needs to be between 52 and 65 degrees there there needs to be the absence of dimerization capability so that means that the sequence of a primer is not allowed to be able to fold back on itself and and make like a little hairpin um head like uh or or hairpins or dimerizations so two primers are not allowed to stick together or stick to themselves um and then we always want to have a little bit of low specific binding at the three prime end so when we design a primer we want the three prime of the primer so it goes from five times three prime the three prime needs to be a little bit loose or looser than the rest so we generally want the primer sequence to end at like an a or an at or at a um so not with g g g right because um an a and a t have two hydrogen bonds while a g and a c have three hydrogen bonds so a g just binds much stronger to a c um than an a binds to a t so when we design a primer we generally want the primer to at the three prime end have a little bit more a's and t's than it has g's and c's um and this just has to do with the way how polymerase works because when polymerase starts um extending the dna um it it needs to incorporate the nucleotides and if it's if if you have a dna strand which is tightly bound together then it cannot incorporate the first base pair so have we have to have a little bit of a lower specific binding at the three prime end so let's go through all of these right so the first thing is uniqueness there shall be only one target site in the template dna where the primer binds right because otherwise this whole technique is not working because we want to amplify a very specific part of the dna um so had the primer sequence shall be unique in the template so we cannot design a primer which occurs like 10 times in the genome no we are very interested in in a single gene and of course we're targeting that gene by making the primer long enough to be only binding at this position in the genome furthermore of course we don't have to consider just the genome that we're interested in right if we do um amplification of for example uh human well not human dna but if we do amplification of mouse dna right then me as a human researcher is working with this dna so when i'm just scratching my head a little bit of my own cells fall off so that means that i have to make sure that the primer binds to mouse dna specifically so that the the sequence that i'm targeting is the sequence in the mouse genome and that the primer is not accidentally also binding in my own dna or in dna of contaminants right because bacteria can come into your sample yes so you have to make sure um that there is no site where the primer can bind in things like mouse or human or rat so you need to make sure that you have to that your primer is specific for the species that you are working on and that any possible contamination sources like humans are excluded and head you can use blast so um we will talk about blast in a later lecture but that's just an alignment tool where you can take a little piece of dna and see if it occurs once twice or a hundred times in a certain genome so the length of the primer has an effect on the uniqueness and the melting temperature right the longer you make the primer the higher the chance that it's unique right if i'm looking for 12 base pair into the genome then that probably occurs at multiple sites but if i'm looking for 50 base pairs right then of course the chances of 50 base pairs mapping to five or six different positions is very small um so head the longer the primer the more chance that it's unique uh than it's unique but the longer the primer the higher the annealing temperature right so the annealing temperature needs to stay below this 72 degrees because 72 degrees is the the temperature at which the polymerase starts extending so before that before this temperature the the primer needs to bind to the dna right so and of course we cannot make primers arbitrarily long um so generally speaking the length of a primer has to be at least 15 base pairs to ensure uniqueness right because if it's shorter than 15 it will start binding all over the place in the genome um and usually we pick primers which are 17 to 28 bases long um i would say that most commonly primers that we design here in the lab are 20 21 22 base pairs so that's kind of the the range so between 20 and 22 right but head the longer it is the more chance that it's unique but the longer it is to hire the annealing temperature so the higher the temperature needed before it starts binding so the base composition of a primer is is affecting this as well like i told you guys you have if you have an at binding then you have two hydrogen bridges if you have a gc binding you have three hydrogen bridges so of course you can imagine that a primer which has like 80 gc is binding much stronger than a primer which is 80 80 right because it just has more hydrogen bonds um so head the the best way is that you you want to avoid uh at and gc rich regions so if you design a primer you want to have a primer which targets a region where there's around a 50 50 at 50 50 50 at versus gc content right so the average gc content in in in mammals is around 50 to 60 percent um so head that will generally give you the right annealing temperature for ordinary PCR rejections and will give the appropriation appropriate stability of course when you're working with certain species of bacteria they might have 70 gc genomes so their gc content of their genome might be much higher right so you can always adjust the melting and annealing temperature uh to to make sure that you have the um that that you can target the right area right so the gc content we generally want to keep it between 50 to 60 percent right so 50 gc 50 80 um but of course this can change and we can change this a little bit but then when we change this we also have to change the temperature of the reaction so of the of the annealing step so this is why we have something which is the melting temperature so the melting temperature is defined at the temperature at which half of the dna is single stranded and half of it is double stranded right so it's like a an ld 50 where the the dosage at which 50 of the bacteria die and 50 continue to survive right but the melting temperature is defined as the temperature at which the dna genomic dna template dna uh half of it is open so it's single stranded and half of it is still in in a helical shape right so the tm is characteristic for the dna composition the more gcs you have the higher the t melting temperature because there are more hydrogen bonds so if i want to calculate tm that's actually relatively easy um if your piece of dna is shorter than 13 base pair um the t melting temperature is just the number of a's plus the number of t's times two plus the number of g's plus the number of c's times four right so if you want we can do a little example um had just come up with a random dna sequence which is like 10 base pairs long and then we can determine what the tm is if the if so w a stands for the number of a's the x t stands for the number of t's the y g is the number of n so on when your piece of dna is longer than 13 base pair um then you use this formula so you have 64.9 plus 41 times and then the number of g's plus the number of c's minus 16.4 divided by the total number of base pairs that your dna strand is long so now of course we can actually kind of figure out where this 90 degree comes from right so the 90 degrees is the temperature at which template dna is half open and half close so why is it 90 degrees well very basically if you look at a whole genome right you have billions and billions of base pairs so this term becomes massively big right so this term is just two billion base pairs for a human genome in the human genome around half of the bases are g plus c right so it's like 50 percent right so what we get is we get like one billion minus 16 divided by two billion so that is around a half right so that means that this whole formula for genomic dna kind of collapses into 65 plus half of 41 and of course 65 plus half of 41 is is generally kind of 90 degrees right so it's 85. something degrees but that's where the the 90 degree temperature comes from um and of course hey if it's if it's 22 base pairs then of course this formula doesn't easily collapse but if you're thinking about genomic dna then this whole formula very basically collapses to uh 65 plus half of 41 and that is the the the temperature at which genomic dna is melting so half of it is single stranded half of it is double stranded the annealing temperature is the temperature at which the primers anneal to the template dna and if i know the melting temperature of a primer then by definition is uh the the melting temperature or the annealing temperature so if i know the melting temperature right because i can calculate that using the formula um then i can just say well the annealing temperature so the temperature at which these which the the genomic dna or that the primer binds to the genomic dna is just the uh the melting temperature minus four degrees celsius right so four degrees below stuff starts binding together um because at at the tm half of the dna is single stranded um so a little bit lower stuff starts becoming double stranded so by definition the annealing temperature is the melting temperature minus four degrees celsius all right so i like if we want to design a good primer we have to make sure that there's no secondary structures which can develop at which can prevent the primer from binding to the genomic dna right so if primers can anneal to themselves or anneal to each other then the efficiency will be dramatically reduced because dna and start of instead of binding to the template that we want to amplify it starts binding to itself and and of course this this doesn't work right so a hairpin is when a primer kind of is able to fold back on itself right so here we see a hairpin so here we see a little piece of dna and then here we see that this ggaa is complementary with cct right so the primer is able to bind back on itself and of course at that point it starts annealing with itself so it cannot anneal to the template uh we can also have like a self dimer right so if we have for example the forward primer and the forward primer in in in another configuration right because these pieces of dna are just floating around in your cup so we don't want them to be able to bind to itself we also don't want to have dimers right because primers we always need to have two we need to specify from from where we want to copy until where we want to copy right so also here we want to make sure that the same sequence of like five six base pairs doesn't occur in the forward primer and then the reverse sequence occurs in the reverse primer right so we have to when you design primers you have to make sure that these things don't occur unless of course they only occur at like 30 degrees celsius right the lowest temperature in pcr is around like 58 60 degrees celsius so if the self dimers occur at 42 degrees then there's not an issue um but generally you want to design primers which are not affected by the secondary structure so that they kind of bind to themselves or or kind of make hair pins or or bind to other primers in the reaction and of course these dimers become more and more important when you start doing multiplex pcr where you're not targeting a single region but for example 10 regions in the genome at the same time because if you're targeting 10 region you're using 20 different primers so of course you then have to check that none of these primers one of these 20 match to the other 19 and of course at that point you're not doing it yourself on paper but you're having a computer figure this out for you so primers work in pairs you have a forward and the reverse right so since they are used in the same pcr reaction you have to ensure that the pcr conditions are still are suitable for both right so that means that if i design a forward primer which is 22 base pairs long i cannot have a reverse primer which is 60 base pairs long because then of course the annealing temperature would be way too far apart right so one critical feature is their annealing temperature which shall be compatible with each other so there is a maximum difference and this is not a rule set in stone but this is more or less the kind of guideline if you want your pcr to work well um then you make sure that the difference in the annealing temperature between the two primers that you are using is not more than three degrees celsius but the closer that they are together the better the annealing temperature works so for the first hour i actually went through it perfectly because it's 158 so the summary for the primer design criteria is one make sure that your primer is unique so that it only targets a single part of the genome only a single binding site for each primer the length should be between 17 and 28 base pairs this range varies a little bit but generally this is kind of the range in which you want to design your primers um the base pair composition should be around 50 60 percent right so the number of g's and the number of a's should be more or less consistent uh or the number of g's plus c's should be similar to the number of a's plus t and you have to avoid long at and gc stretches right so you cannot have a primer um that has been a a a a um question from a first time chatter hey first time chatter uh no one has to go how long have you been coding um that would mean that i have to reveal my age um but um i've been coding since i've been four five years old so when we got our first commodore 64 when i was really really young um i started coding directly because at that point you you you just had like little programs so you had to modify the programs to do anything with it um so but real coding i think i started when i was like 10 11 years old i really became interested in writing stuff for for computers um but let's finish the list so number three is optimized base pairing to minimize false priming right so um like i told you guys because of the way that polymerase works it needs to bind very tightly at the beginning at the five prime end but it needs to be a little bit less bound at the three prime end because when the polymerase binds and starts extending it needs to be able to add the base pairs which means that um the like the part of the primer should be a little bit loose at the melting temperature of the primer should be between 55 and 80 degrees celsius this is of course a long region or a long range but it has to generally you would say that it has to be like 56 57 degrees um so primer sets have a t anneal which with two to three degrees of each other right so if i have one primer then the other primer which i use in the same reaction should have the same kind of temperature um characteristics right we can't have one primer binding at 40 degrees and the other binding only at like 90 degrees because then they won't work well together and of course we want to minimize internal secondary structures like hairpins and dimers and these kinds of things so that's more or less about the theory and so if you follow these steps right so if you take your piece of paper and you have your genomic sequence and you just say i want to target this part and then what you want to do is you want to make sure that all these seven criterias are fulfilled and of course for a human this is relatively hard um because when you're human it's difficult but a computer can easily optimize these things um so in the next part of the lecture we would look at some more advanced primer design so what happens when we want to target not a single region um but for example multiple regions um and then we will just have a little introduction on how to use primer three um to design a primer to test a certain hypothesis so we will just go through all of the steps needed for primer design good so that's it for the first hour so everyone watching on youtube thank you for watching and we'll see you in the next hour


You gotta practice. What's up, money geeks, Mr. V here. Welcome to another video, guys. So in today's video, we're gonna talk about paper trading. When it comes to you investing in the stock market, there's two things, right? Most people are so scared because they think the stock market is so complicated. Number two is that most people think they don't have enough money to invest in the stock market. All those two things might be true or might not be true depending on who you talk to. So if you want to drive a car, what do you do? You know, you practice, just make sure that you know what it means to get on the freeway and drive. Similarly, with just investing in the stock market, you want to understand exactly what the stock market is, understand the basic terminologies and understand what it means to buy and sell a stock.  I'll say if you can practice again with paper money, which is fake money, and just learn the terminologies, that would really come in handy for you. So before we actually jump into the details, guys, into this video, if you're new to the channel, we talk about how to earn money, how to save money, how to invest money and build wealth.  Okay, that was just a joke. Anyways, so there's multiple platforms out there that you can actually practice how to buy and sell stocks with fake money. And to this video, we're gonna be looking at Weibo. I've talked about Weibo here on the channel. I most recently posted a video as to how transferring your assets from Robinhood to Weibo. I've done a review on Weibo here on the channel. I'm gonna hook those up here so you can definitely go check it out. So we're gonna jump into it. I'll show you guys exactly what it means to buy and sell stocks using Weibo, which is fake money. So let's jump right in. So the first step for you is to go to Weibo.com. If you haven't already signed up for Weibo, I would encourage you to sign up for Weibo. And I'm gonna put the link in the description below. If you do sign up using my link in the description below, you get two free stocks. The first one would be between $12 and $250. And the second stock would be between $12 and $1,400. So definitely use my link. And again, it's an affiliate link. So when you use that link, I get a free stock from Weibo as well. So that's something that helps me out, continue to put out content. So again, you can sign out without using my link, but if you wanna help me, you can definitely use my link. That does help me a lot. So once you have your Weibo account, everything's set up for you in Weibo. You just come up here. If you go to, say, trade on the website, it's gonna open your trading platform on the web so you can also do that on your phone. You open your trading platform on the web. And specifically, we want to go to, so if you're on the left hand here, if you go to paper trading, and then it's gonna open this window for you. So now you are in the paper trading window. So they give you, setting out, they give you a thing, a million dollar buying power. And you can totally change that number and reduce the number. So, but in this case, let's say we have a million dollars. As you can see, my profit and loss ratio. Right now, I have made $21,000 trading and just buying and selling stocks. But again, that's fake money, right? So, and this, I did some tricks today. That's how I like today. If you see today, I made $11,600 for some trades that I pulled through. So, but again, this is all fake money. So it's not real. Don't take this serious. So basically, once you come in here, for you to start trading, you want to know exactly which companies you want to trade. Let's take for instance, I'll use a company that you guys are all familiar with, right? Let's say if I look for Facebook and Facebook comes up and I load Facebook in here, right? So this is Facebook. So it's got a paper trading again. So Facebook is loaded. And now I'm because like, hey, right now Facebook is trading at, let's say a hundred and, let's say a hundred and 75 dollars. And you can go here and say you want 50 shares of Facebook at that price. So some of the things I'm gonna explain to you, if I do this drop down, see, there's a limit price and there's a market price. So markets open between 8.30 and 3 p.m. central time. And so if you're Eastern, you just add one hour. If you're, I think, Western, you minus one hour. So that's for us in the Midwest, it goes from 8.30 to 3 p.m. So that's when the market, so if you choose a price here as a market, it means it would execute during that time. If the market is closed, that's not gonna execute. Then you choose a limit order. Limit order just simply means that you choose a price that you're comfortable paying for that particular stock. So let's say for instance, if I can zoom in here into this chart, you can see that Facebook is at 175, that's still low.  You can see that Facebook was at 177 at some point after hours or during the day, 178 during the day. So what I would do is you decide how much you want to. Let's say I want to pay just 175 for Facebook. Then I'll say 175, that's my limit price. And I want 50. And I want this trade to happen during the day because you have, so I want this trade to happen during the day. Do I want it to happen within extended hours? If I say yes, extended hours means that this trade would happen after the market closes because there's certain stocks that continues to trade even after the market is closed. They have two hours. So from 3 p.m. to 5 p.m., you have after-hours trading and from 8 to 8.30 p.m., you have pre-market trading. So some stocks do trade in that window. So if you care about buying and selling within that window, you can select yes, you want extended hours. And then you just select right here if I say, so I have to select, so I want to buy. So I have to choose buy. And I say trade. Watch, right now it says it's working on it, so it hasn't been filled yet. Once this trade is filled, it's going to move over from this window. It's going to come over here and that's going to be the position that I've actually bought. And then what's having here, it's going to show me my P&L, which is profit and gain. So if it's red means I'm losing money. If it's green means I'm making money, which is always awesome. As soon as you feel comfortable, you say, hey, now that I have that position, it's gone up, let's say, 15, 20, 30, 40, $50. You want to take the profit, then you can come in here. It's just that simple. You come into the selling window, you select sell and then you can choose the limit order. You say, oh, now this stock is at, say, 177. Or you say you want to sell that 177 and then you place a trade. Oh, sorry, I didn't choose the quantity. So let's say 50. And then you pick the trade. So you can say, hey, I want to sell this. And then if it's there, you're going to sell that particular one. So the only reason this one's didn't execute is because the buy didn't execute. So the sell is not going to execute yet. So that is very simple. You can practice with any stock that you possibly think of from Google to Amazon, buy, sell, just test it. During the day and see if that works out for you. See if you understand the concept, what it means to buy and sell a stock. And again, this is not designed for you to go out and be a day trader. This is just designed for you to understand the concepts of being able to buy and sell a stock and investing in the stock market. I'm assuming that you're somebody that wants to invest your money and let it grow. So this would be an opportunity for you to be able to learn. So that is the basics of it. There's so much in Weibo that we can go talk about. But for today, I'm just going to leave it right here just to show you guys the basics how to buy and sell stocks using Weibo's paper trading, which is the platform that they provide for you to be able to practice trading. So that's it for you guys. So if you like what you see, go ahead and give that thumbs up. And if it's useful to you, share it with one friend. And if you think that what we're doing here is great, go ahead and hit that subscribe button and the notification bell because it does help the channel grow. Question of the day, if you use paper trading before, if not, are you going to try it after watching this video? Guys, don't forget again, use the link in the description below to open a Weibo account. You get two free stocks, one that's worth between $12 and $250, and the second one that's between $12 and $1,400. Totally free at no cost to you. And as always guys, stay safe, stay motivated.