 Back everyone, if you're watching this on YouTube, thank you for being here. If you're on Twitch, then also thank you for still being here. So let's just continue. So lecture nine was about primer design. So the first part was about polymerase chain reaction. So what do we need for PCR? So we need water, nucleotides, primers, template DNA, and a master student to do it for us. But we also talked about what is a good primer and when is a primer a primer, right? Because primers need to be a certain length and they need to have a certain binding capacity by having like an AC, GT kind of, so an ATGC composition. And we also talked about advanced primers so that you can do multiplex PCR where you have or where you're not amplifying using a single pair of primer but you're using multiple pairs of primers. We talked about universal primers and semi-universal primers if you want to amplify a piece of a virus but not just from one strain, but multiple strains. So then you can use things like universal primers. I think the example that we did there was Hapephe, something like that. But then we also have Gessmers so if you don't have a DNA sequence available, then based on a known protein sequence, you can still design primers for your animal which doesn't have a genome sequence available. And you do this by back translation, right? So you look at the amino acid sequence and then you code the amino acids back to their DNA equivalent, which of course is not perfect. So Gessmer primers are generally longer than standard primers to make them still bind or have to still give them the binding properties that you need. So when we talk about PCR, PCR comes in three steps. So know that the first step is denaturation where we heat up our sample to go from double-stranded DNA into single-stranded DNA. And then the next step is lowering the temperature a little bit so that the primers can bind. So this is generally like 90 degrees Celsius and kneeling happens at around 60-something degrees. I think the temperatures were mentioned in the lecture. And then had the primers bind to the DNA and then in the next step, we put the temperature up to like 70 degrees Celsius and then the DNA polymerase starts amplifying the DNA and DNA polymerase only starts amplifying DNA when we have these primers there. So when there's double-stranded DNA. So we talked about PCR, right? So when we have our gene of interest that we are trying to PCR out, then of course in the first cycle, we get like two copies, four copies, eight copies and 16 copies. Let me just mute myself. It's going on with my voice, but so in PCR, we do an exponential amplification which means that the number of cycles that we use, we can kind of estimate how much product we're going to get which is two to the power of the number of cycles that we do. We also showed the first few cycles in detail, right? Because this is just a kind of stylized picture and this is what we would like to happen. But of course, this is not how it exactly happens. But if you're interested in that, then please watch back lecture number nine. So we also talked about the length of the primer and the uniqueness and the fact that when the primer gets longer, you need to have a higher melting and annealing temperature, right? But there's this trade-off because the longer your primer, the higher the chance that it's unique, but also the longer the primer, the higher the annealing temperature. And then you end up in this situation where there has to be enough temperature difference between the annealing step and the elongation step. And we talked about things like melting temperature. So the melting temperature is the temperature at which half of the DNA is single-stranded and half of the DNA is still double-stranded. So the melting temperature for genomic DNA generally tends to be at around 93 degrees Celsius. But of course, for primers, this is much lower because primers are a lot shorter. And we also talked about annealing temperatures. So the annealing temperatures, the temperature at which the primer starts binding to the genomic DNA. And we talked about multiplex, PCR, semi-universal primers, gasmers and many more. And I also showed you in which fields primer design skills are required, right? So if you are ever going to do real-time PCR or studying population polymorphisms using microsatellites or AFLP markers, then you use primers. And we also talked about internal probe design. But then the most important things to remember about this whole lecture is that if you design primers, you have to achieve the appropriate hybridization specificity. So it has to be unique, right? You only want to amplify one part of the genome and you have to be able to do this stably, which means that you have to have enough difference in the different temperatures for the whole process to be able to kind of cycle through these three different temperature levels, right? Because if the primer is too long and your annealing temperature is around 70 degrees Celsius, right? Then the annealing temperature is the same as the temperature used by the polymerase and then stuff starts going wrong because then the stability doesn't hold. And stability also has to do of course with the number of ATs versus GCs because you want to have your primer to bind very tightly to the DNA and of course this has to do with the template DNA and the ACGT content of the template DNA as well. To remember, right? If you design primers, never design primers on repeated sequences because if you design primers on repeated sequences then of course they're not going to be unique. So primers cannot contain repeats themselves either. So we want to get rid of them and for that we can use a tool like RepeatMasker, right? So you can use RepeatMasker standard or you can use it directly in ensembles. So when you export your sequence from Ensembl, you have the option to mask your sequence which means that it kind of blocks out areas which are repeated across the genome and it blocks out areas which are of very low variability. Right? If you have ATC, ATC, ATC, ATC then it will block out this region so that you don't design primers by accident to these kinds of sequences. So lecture 10, we talked about databases. So we talked about some terminology about databases. So what is, things like SQL and these kinds of things. I try to explain to you guys why we need databases because they have advantages over just storing data on your hard drive and they allow things like sharding which makes them much faster and you can do indexing. So no sharding means that you put it on different sites. So you have one database which is not just local on one position on the earth but which also has, for example, the same database but then in Japan so that people in Japan can also use it. And then you have indexing. So indexing means that you look through a column, see what's there and already start anticipating on future queries by building an index so that you can quickly find back stuff in a database. We talked about normalization of data within a database and the organization of a database so that you can search generally in different ways and that you have, and we had an overview of all kinds of important databases in bioinformatics and biology has so you need to know that what can we use ensemble for, right? What is in PubMed? But also that there are databases like the PDB which focus entirely on proteins that you have DBSNP which only focuses on storing single nucleotide polymorphisms in humans. And during this lecture, we also talked about Biomart and I did a Biomart example using our African goat data I think and have Biomart is a connector for R and for other programming languages so that you can automatically query many of these important databases in bioinformatics so that you don't have to go to the database, to the website and start clicking on it and downloading stuff. Now, Biomart allows you to write code which will retrieve data for you which of course makes your research much more reproducible. So a little bit more about the normalization, right? So normalization of data means that instead of storing the full student name in a single column, you can break it down to a more granular level saying that no, every name has a first name, middle name, last name, right? And then that's easier because now we can see that all of the students have the same middle name which of course is not obvious from the data that we used to have in a single column. I told you about over decomposition so when you start chopping up things that actually should not be chopped up, right? When you have a phone number then store the phone number in a single column, don't start doing smart things like storing region codes, area codes and then phone extensions because these might change over time plus that no one's ever going to do a query, give me everyone who has this phone extension, right? Because that's not a very good thing. We also talked about things which go wrong in databases, right? So for example, duplicate data and not so much duplicate data, but the worst thing is duplicate data which is named differently, right? So if you have fifth standard and fifth standard, once you write it with the number five TH and the next time you write it with fifth, then of course the database does not know that these two things are the same, right? So it is a duplicate data but it's even worse because it's duplicate data but it's also inconsistent. And this is the thing that databases help you solve by using things like foreign keys, right? So in this case you would say no, the standard is a foreign key which points to another table where all of the different standards are described. So we had a bunch of slides about this and you don't have to know all of the normal forms but know that there are things like over decomposition and that generally the database functions best when data is more or less stored in a normalized fashion. All right, so in lecture 11, we talked about sequence analysis. So we know that sequences change during the course of evolution. And so you have point mutations where a single base pair gets modified but we also have insertions and deletions and to make it even worse, we also have this kind of xenologs. And so genes which are transferred from one bacteria to the other but generally these are considered three main forms of sequence variation during evolution. And then of course we talked about homology trick, right? So that since all life on the planet more or less comes from one single event where life existed and then everything branched out, that we can use homology to kind of infer the function of a protein, right? If we know that a certain protein is transporting oxygen in humans and then if we find a protein in mice which has a very similar sequence, and then we can also assume that this protein in mouse probably also will transport oxygen, right? So that's in the homology trick works because it's a single tree which kind of grows up from the early beginnings. When we talked about sequence analysis, we talked a great deal about sequence alignment, right? Because that's kind of the fundamental algorithm in bioinformatics and that there are two major variants of alignment. One is global alignment and the other one is local alignment. So global alignment tries to match the entire string to the other string. So it is more likely to insert gaps at the beginning but when we, I think something went wrong with this picture. Yeah, this is wrong. Please ignore this slide and look at the slide in the lecture. I think I copied the same one for, but local alignment tries to find the optimal substring while global alignment tries to match the whole thing. So yeah, so this is just wrong. I copy-pasted the same thing more or less twice but then look at the original slide in lecture 11 and know that there's a difference between global alignment and local alignment and local alignment generally is used when you have very short sequences which you wanna compare towards a genome while global alignment is used when you have complete viral genomes that you want to align together. So we talked about sequence analysis a lot and when we talked about it we also talked about scoring functions, right? So that to compare if two sequences are similar we need to kind of have a mathematical definition of similarity, right? So the most basic definition that we had or that we could come up with is just the percentage of matches. So how many base pairs match? So you get a plus one for each base pair that matches and for every mismatch you get a minus one penalty in a way, right? So percentage of matches is just seven out of 12 base pairs match but you can also add this plus one, minus one system and this plus one, minus one system can then be extended with a linear gap penalty which means that when you open up a gap in one sequence then opening up a gap of two compared to opening a gap of four the gap of four is twice as expensive as the gap of two. But since in biology we know that insertions and deletions are very common we nowadays almost always use an affine gap penalty that means that you have a high penalty threshold for opening a gap but then when you make the gap bigger you don't put that much penalty on there, right? So opening a gap might be a score of minus one but then going from a gap which is from one Y to a gap that is two Y you give a 0.1 penalty, right? And going from two to three again you get a 0.1 penalty. So and this allows you to do much better alignments when you use this affine gap penalty. So know what the difference is between a linear gap penalty and an affine gap penalty. So the main difference is that in a linear gap penalty you score for every X base pairs that you make the gap bigger you get plus X penalty. While in an affine gap penalty you don't have that opening a gap is expensive but extending a gap so making the gap bigger is relatively cheap. And I want you guys to be able to calculate the percentage of matches on DNA and protein level. So when I give you one of these kind of amino acid codon wheels you should be able to read the amino acid codon wheel and if I give you two DNA sequences you should be able to say, well, okay, DNA wise it matches seven out of 12 but on protein level we see that there is a nine out of no a four out of four, right? Because of course we come in codons so three letters of DNA become one amino acid but be able to use these things. So I think we did a small example in the lecture as well. We also have to take care in DNA alignment that we score transitions and transversions differently. So because when you look at DNA then it's we had this figure where we show that when you go from an A to a T that this is relatively common because the chemical structure of the A base pair looks very similar to the chemical structure of the T base pair, right? So by using electromagnetic radiation or nuclear radiation it is very commonly it's a very common occurrence for an A to change into a T, right, but an A almost never changes to a C or not almost never, but when this happens it's called a transversion and a transversion is very uncommon. And the same thing happens in proteins because in proteins we have amino acids which have very similar side chains so changing a glycine by a glycine is a very small change because the side chain is the same, right? And then we have something which is this substitution probability matrix where we talked about the Blossom matrix and the POM matrix and these matrices they try to catch this fact that when we have a substitution of one amino acid by a different one so we have kind of a mutation there it tries to score some more heavily or penalizes some more heavily than others, right? If we have a positively charged amino acid being changed by a positively charged amino acid then this is a relatively common occurrence but a positive amino acid which gets changed by a negative amino acid that of course has a much bigger penalty associated with it. Know what BLAST is, basic local alignment and know about cluster W so alignment, multiple sequence alignment where we try and align multiple sequences together and cluster W, when we talk about cluster W we talk about how to detect conserved residues we can find conserved regions but we can also find patterns in our amino acid structure and when all of the amino acids are for example positively charged and then they don't all have to be the same but then still there might be a pattern saying that for the functioning of this protein it is very important that at this position you have a positively charged residue or positively charged amino acid. All right, so lecture 12 was all about gene expression analysis again we did more or less the exact same thing as what we showed in lecture one where we looked at microarrays, right? So creating Oligo arrays had no where bioinformatics is involved. You don't have to know all of the different things, right? But know that a TIFF file is just an image file with these dots on the microarray which can be red, green or yellow. Cell file is this proprietary format from Afimetrix which stores data about microarrays in kind of a compressed way. I talked about normalization and why do we normalize during microarray analysis and this is because the dyes have varying behavior, right? The green dye has a much higher dynamic range than the red dye but there's also variation during the hybridization like the surrounding temperature or the surrounding humidity has a big influence on how well your sample hybridizes to a microarray. And of course there's variants in the manufacturing if I buy an array now and I buy the same array in like five years then of course the quality of the array might be different, right? Because the technique gets better so the array that I do today is not directly comparable to the array that I do in like five years. So to kind of get rid of these effects, right? These are all effects that introduce variants into the sample and we want to get rid of that and that is why we do use normalization. Besides normalization of microarrays we also almost always look at the log two ratio of a microarray and this is because of this varying behavior of the dyes where we want to have kind of a linear scale saying that if I go from zero to one then this needs to be the same as going from zero to minus one, right? So it's a transformation where we go and then we divide the green dye intensity by the red dye intensity and then we do the log two and this is just to prevent the fact from having, if you have one divided by two that is different from two divided by one. And I think the slides actually in the lecture explain it pretty well why you want to use it. When we talked about gene expression I showed you guys that you can do it using t-test but also you can do it using ANOVA test, right? So a t-test is really nice when you have two groups but when you have for example two different groups two different tissues and you have two different factors for example low concentration of medicine and high concentration of medicine then you are forced to do an ANOVA test, right? Because an ANOVA test allows you to adjust for covariates. It allows you to put up a model saying that my intensity of the probe is related to the condition in which the probe was measured plus the thing that I did to the sample plus the type of mouse where the sample was taken from, right? And you can do that with a t-test because a t-test only compares two conditions so condition A versus condition B and does not allow you to control for other factors. Here again I mentioned multiple testing, right? So the type one error is calling a gene significantly changed even if it's just by chance. So type one errors can be avoided by Bonferroni correction and the type two errors is when you say that a gene is not significantly changed or not significantly different between two samples that you have while it actually is. And you can only optimize for one of the two so you can say I want to have a minimal amount of type one errors but then of course your type two errors go up and you can say I want to minimize the type two errors but then the type one errors go up. So that's the kind of trade-off that you have to do. And that this one can be avoided by Bonferroni correction. The type two error is binomini Hoogberg false discovery rate adjustment. We also talked about gene ontology, right? Gene ontology is a common terminology we use to describe the things like the cellular component where the gene is found, right? So a gene can be active in the nucleus, a gene can be active in the cytosol or it can be active outside of the cell so that's exported. But also we have a common nomenclature so a common terminology for things like biological processes and molecular function. And this allows us to do these over-representation tests, right? Imagine that I do a microarray analysis and I find 50 genes which are different between the two animals that I look at. Then we can do these tests looking to see if a certain cellular component is over-represented in these 50 genes, right? If all of these 50 genes are nuclear genes, then of course we hypothesize that there might be something going on in the nucleus but if all of these 50 genes or 40 out of 50 genes are located in the mitochondria, then we might assume that no, the mitochondria are the thing where it goes wrong or where the animal has an issue. And the same thing for CAG, right? Using CAG we can actually, it provides these map of different pathways in different species and we can actually overlay our gene expression data onto a CAG pathway to see if everything in a pathway is up-regulated or if a whole pathway is down-regulated based on the tissues that we're looking at. Again, here we talked about similarity, right? Because we have to have a mathematical definition of what is similar and of course it's different from when you look at, when you compare DNA sequences to each other or when you compare protein sequences to each other. When you compare expression profiles to each other, there are three different distance measurements that you can use, right? So the Manhattan distance is just the absolute difference between the sample one and sample two and then across all of the probes that you measured. The Euclidean difference is more or less the same but it's not the absolute difference, it's the difference to the power of two. You add up all of these differences and then you take the square root of the total difference and then the Minovsky distance is more or less the distance generalization for this where you can choose your own m factor, right? So an m factor of two means that you have Euclidean distance but you can also have an m factor of three and why do we sometimes use Minovsky distance? Because sometimes we want to put more weight on large differences, right? Because 0.1 to the power of three is of course much less than two to the power of three and so by choosing a higher m factor you're focusing more on extreme differences compared to small changes which are globally across but three different distance measurements to express how similar or how different two expression profiles are in animals or in mice or in plants. Again, we want to build a tree, right? Because we want to see which things belong together, which things are not belonging together and then we also talked about this clustering. So there's a difference between single linkage. So if you have a group or a group of two profiles and a other group of also two profiles then the single linkage is based upon the two most similar elements within the groups. If we look at complete linkage then we look at the two most dissimilar elements and at the distance between the two groups is then based on the two most dissimilar elements and then we have average linkage which is also called OPGMA and then we look at the distance between two clusters is taken as the average of all distances between pairs of objects X in A and objects Y in B and that is the mean distance between the elements in each of the two clusters. So average linkage is the best but it is relatively expensive to compute when you have literally hundreds and hundreds and hundreds of elements, right? If cluster one has a hundred elements cluster two has a hundred elements then you have to compare all of them, right? So if you compare one versus a hundred the second one versus a hundred the third one versus a hundred so you do like a massive amount of comparison. So OPGMA is the most computationally expensive method and that is why sometimes people look at single linkage and complete linkage because it is relatively cheap because you only have to do one comparison. We also talked about where you can get free microarray data and so go to gene expression omnibus if you want to get free microarray data to work on and write a scientific publication without spending any money and the same thing you can do at Array Express the massive advantage of Array Express is that they have curated re-annotated archive data which is a very high quality because someone looked at it and made sure that the sample that was submitted is really the sample that people said that it was and that's not the case for gene expression omnibus gene expression omnibus anyone can upload data, even me so that means that there's no curation going on. Lecture 13, standards for analysis very interesting lecture I think because have we talked about different biological file formats like the comma separated file but also FASTA files, so sequencing files or files holding sequence data then we have FASTQ which is the standard output for DNA sequencers nowadays which contains DNA sequence data but also DNA quality data we looked at the GFF format which is the format for storing genomic features and we have the VCF format for storing variations relative to a reference genome and we also looked at the BETMAP format which is a very common format when you do association analysis so it stores variations on one side and other also phenotypic measurements on the other side so it's kind of the common file format used in association analysis like genome-wide association and QTL mapping. We talked about difference in testing strategies so if you write code as a bioinformatician then use test to test the code that you've written. So a unit test means that you test the smallest unit so you've written a function so you throw all kinds of different input to the function and then you see if what the function gives you is actually correct based on what you wanted to do with the function, right? So regression testing is different because regression testing means that you take the code from someone else and then just throw in data, see what comes out and then you start modifying the code but you make sure that every time that you make a modification that you run the test and make sure that for the same input still the same output is produced and then we also had some words about test-driven development where you develop software using this iterative approach where you say I want to add a new feature so I write a test that tests the new feature the test initially fails then I start writing code I then run the test again and if the test succeed then I have successfully implemented that feature and I continue with adding a new feature, right? So it's writing tests and then writing code to pass the tests. We also discussed all kinds of different types of documentation so we talked about user documentation and documentation which is written beforehand we talked about code documentation and so know that there are different types of documentation for different audiences and so you're not only writing code for yourself but you're also writing documentation that belongs to the code like a tutorial for people that will use your code but you also write things like function descriptions so saying that this function has five parameters and these parameters had the first parameter needs to be an integer between zero and a hundred and so there's different types of documentation for different groups of stakeholders when you are writing software. Last lecture of last week I think that's also one of the most fun lectures because I always liked doing it we talked about citations why do we cite stuff in science and what's the use of it? We talked about things like web of science so if it's not in web of science it's not science we talked about Google Scholar and Research Gate and things like H indexes and I indexes we talked about scientific reference management and that you should do some form of scientific reference management using a reference manager like EndNote or Mendeley and then I showed you a difference between distributed and centralized version control right so that's it's not directly related to literature management but version control is related to kind of software management because you want to be able to go back in time to rerun your analysis and this has to all do with reproducible research so in theory lecture 14 should have been called reproducible research instead of literature but we talked about citations which are there to make sure that when you claim something that you point to the guys that actually did the research for it we talked about reference managers which allow you to kind of easily include references when needed and version control is there so that your code is also version so you can go back in time because of course code changes and sometimes you need to rerun an analysis as if it was 2017. All right so with that first example question so if you throw the answers in chat then we can go to the next one and we can all go home early so what is the difference between pre-mRNA and mature mRNA? I have a sound effect for that like let's do the audio and then just do crickets so I'm just gonna continue this sound effect until anyone answers the question all right question answer number one it's not spliced hi by the way yeah hi Sainaxin welcome to the lecture were you here the whole time or did you just arrive but indeed indeed pre-mRNA still has the introns inside of the messenger RNA just forgot to say that's okay that's okay it's good that you answered so yeah so mature mRNA does not have introns pre-mRNA still contains the introns so there's this process called splicing which removes them so that's entirely correct all right next question next question what is the function of tRNA I'm just gonna do crickets again I have more sound effects we can do birds let's do birds for now and then so anyone can answer like there should be six people viewing it minus myself and my moderator of course um we can have an answer to what's the function of tRNA I actually mentioned it during the lecture I think so it's the link between RNA sequence and the amino sequence of proteins yay very good so tRNA is indeed it reads the codon in the messenger RNA and it links it to an amino acid so that indeed is the function of tRNA so it's the link between the RNA sequence and the yeah very good all right next question what are the four steps in a mass spectrometry workflow slash experiment just that xanaxin doesn't answer all of them like misha come on you know this misha like get your head away from the olympics and answer at least one of the example exam questions stop watching the olympics like they will win the gold medals even with you not watching them no I don't one gold in the pocket okay okay so at least we won a gold medal that's good that's good all right so the four steps are of course compound separation right because we have a mixture so we need to separate the compounds then we need to do fragmentation and ionization then it is separation by mass over charge and then it's detection I think I'm doing it wrong I don't have to know it fortunately you guys do so that's that's kind of the way that it works right so I already gave the lecture so for the lecture I read up on it but yeah the four steps I think are compound separation fragmentation and ionization separation using mass over charge and then I think detection so that that's kind of the four steps by the way I am relatively strict when it comes to numbers so if I ask you guys for three things and you write down four it is completely wrong if you write down two then you are maximally allowed to score two out of three points but it's not a guessing game right so if I say what are four reasons to do x and you write down six then it's completely wrong because I'm not going to pick which four are correct and which two are wrong or the other way around right even if all six are correct then because I asked for four you did not understand the question extraction separation identification quantification yeah that's what google says but uh that's okay google can say something else I think it's more or less similar right let's just scroll back quickly so metabolites compound separation fragmentation and ionization separation and detection yeah see that's okay all right um last example question I think I had four name two protein purification techniques and describe how they work so um a difficult question I don't think that I actually mentioned it here because we did have protein purification in the protein lecture but I didn't I don't I don't think that I mentioned this but uh and of course you don't have to describe now how they work because then you're typing for like 15 minutes and uh but these are the kind of questions that you can expect right very basic questions about the the the lectures that we had and generally I I like these two fold questions right so that you name them and that you quickly describe how they work will the exam be oral or written what do you want it to be because um I like to do a written exam but in the Prüfunksen Studiums or Nong it says it has to be an oral exam but then the question is because I actually submitted a request to the exam committee to have it changed from an oral exam to a written exam and they actually accepted that but it's still listed in Achners as being an oral exam while I'm actually so it's it's it's probably going to be written because I I think that written just makes more sense I'm fine with either all right good um and I think I looked at Achners and I think you are the only one who registered for the first period um for the second period there's actually three people registered um so since you are the only one who registered for the exam next week and the other people all registered for the makeup kind of exam or the the second exam date in your case we can we can do whatever you want if you say I just want to have them orally because they were through quicker than that would be fine with me as well but I will think about it and I will let you know because if two or three other people of course still register you can't do a drawing question in an oral exam you actually can because we're doing it via zoom so Sonic sin can just sit there make a drawing and show it like that right and the question is still perfectly valid like um draw platypus right it doesn't matter if it's written down I can draw a puffer fish that's a that's a that's a that's a challenge we can we can look at that but anyway yeah I'm I'm still thinking about it a little bit I think legally I could do both since I did get the the okay from the exam committee to do it written but on the other side you're the only one who registered for the first date so we might just want to do it orally then because that's going to be a lot quicker and I can be a lot more flexible right if you write it down wrong then I have to kind of say this is wrong but if you if you just say it wrong I can kind of sit there and do like right so we'll have to see I will let you know I will let you know I will discuss also with the other people here because when we do it orally I also need to have a secondary examinator there well I don't need that for a written exam since I just have your written exam but I will I will let you know and I will let you know before this weekend so I will think about it I will discuss with my colleagues here and then I will let you know tomorrow probably because I think people can't register anymore for the first day so I think they can still register for the second date but not for the first date anymore anyway doesn't matter too much there will be an exam next week and you will do perfectly fine because already here you had three out of four questions so that that's going to be going to be in the direction of like a 1.7 I think but we'll see so all right so that was it for today and for the whole lecture series so I discussed with you guys all of the different lectures that we had also which lectures you should not focus on learning so it don't don't spend too much time on the r introduction lecture although it's an important lecture because programming is essential there won't be any programming questions on the exam so that's it so we're through I think in total we did 50 hours of streaming 50 hours of lecture so I want to thank everyone that attended the twitch streams I want to thank you guys for for being there thank you for attending the course and like I said I'll mail everyone who registered for the exam with the details as soon as I get the list still didn't get the list should have gotten the list already from the pre-functs but they're not doing and yeah good luck on the exam and I am I hope that everyone will pass so that we don't have to have a third exam date and yeah thank you guys so much for being here and I hope you guys learned a lot of course if you have any questions then feel free to ask and besides that the could we have that PowerPoint that acts as a study guide so you mean this one yeah I will I will upload it directly I didn't upload it yet yeah no I will do that directly good all right then yeah thanks so much for being here I really enjoyed it it's nice to stream it like or to be able to do it like this at all I miss the in-person lectures I like the in-person lectures a lot as well but I think this is this is as good as we can do with the current circumstances so thank you guys for for being here and spending 50 hours with me on bioinformatics and all of the different topics that we discussed and I will see Xanax in at least next week on the exam and the other workers slash students I will probably see them on the second exam date and that that's it for now so unless anyone wants to get rid of some of their channel points slash Danny bucks and have me make a drawing then I'm actually going to close the stream early for today and then enjoy learning for the exam and then enjoy the weekend already all right see you next week yes and then to all the other people that are still watching bye bye and we will see each other on stream let me see let me see because I do have another date for the summer semester so streams will continue or restart let me see so the data analysis using our course will start on 21 April so the 21st of April there will be at least probably a stream unless I have to do it in presence or if I can do it in presence but if it's going to be online then 21st of January 21st of April we will start the course so see you then and I hope you enjoyed it I enjoyed it a lot and thank you for being here