Good morning. My two sessions are really about detecting genomic variation, right? So how do you actually go from the data that you've mapped onto the genome to detect either the single nucleotide variants like we see here? So we were sort of looking at the data quite a bit by GV yesterday, but now we're actually going to use algorithms to call these variants. So the objectives of the module is really sort of go over the steps to actually call variants, understand the principles of variant calling, know what are the different steps that can affect and improve variant calling step, know how to filter, get rid of bad variants, annotate variants. I had already some questions yesterday about how do you annotate variants, so call variants. So far the file formats and the files that we've looked at a little bit were FASQ files and also the BAM files after you've mapped them. We're going to look a little bit at the VCF format that was mentioned yesterday, which is the variant calling format. So this is the files that you get that are much, much smaller actually, that actually include the description of all the variants in your data. And then we'll go back to using some of the tools you've learned yesterday and to visualize these SNPs and play in an IGV event. So before I start, so I wanted to do a parentheses and these are a few extra slides that I've added yesterday because we mentioned yesterday that so even though for this workshop we're using Amazon Cloud, there's other resources, especially if you're Canadian, you actually have access to what I think is a great resource, which is these large clusters that are part of Compute Canada. So at McGill we have a cluster with 25,000 cores. Sherbrooke is another one, there's HPC4Health, we have something like this in Toronto. But no matter where you are in Canada, you actually can request access to these clusters. You just sign up or your PI needs to sign up and then you can get an account on these resources. And so the types of commands and the types of tools that you use, it's really the same process but instead of using the Amazon Cloud, you can use these resources. So again, if you're Canadian, that's great. If you're American, well, if Trump gets elected, that's another reason to come to Canada. We've installed many of the BindFarmatics tool on these resources. So a lot of the tools that we're using now, you don't even need to install them a bit like on your Amazon instance, the GATK that we're going to be using, BWA for mapping, all of these tools are already installed. So I've added some links with information on where you can find more information if you're interested. And then the last bit, and this is related to the Galaxy that you're going to be using this afternoon, but we also have set up Galaxy on Compute Canada as part of this project of GenApp. So you won't be using this particular Galaxy instance, but again, if you enjoy that section this afternoon where you're using Galaxy and the public Galaxy is too busy, you could potentially use Galaxy from this site. But again, I mean, this sort of, it was just an aside. I think David will talk more about, when he talks about Galaxy, we'll talk more about the Galaxy instances that we've set up on Compute Canada. Okay, but back to the real meat of the module and what we're going to be talking about. Just a little bit of context. Why is it interesting to call variants? And why do we re-sequence? We've sequenced the human genome, why do we re-sequence? So we re-sequence because we're interested in detecting both single nucleotide variants and larger structural variation across individual in the context of disease. So we want to know if there's specific variants that are more common in people affected by a particular disease. In cancer genome sequencing, I had some questions yesterday about the difference between somatic and germline mutations. So you acquire new mutations, so you want every tumor has its own set of variants. So you actually, that's one of the reasons why you would want to be re-sequencing. And these are two examples that are a bit more human-centric. And I guess most of this module, we've been using the human genome as a reference. But of course, there's parallel application in many of the other areas in which you work in. And the principles are the same. So even though we're using here the human reference genome as an example, obviously the pipelines with some small differences, and Jared yesterday talked about some of the differences and some of the challenge with some of the other genomes, but still hopefully many of the principles that we're talking about here also apply to your model organism. So this is similar, but looks different than the slide that Matthew showed, but this is a similar workflow for a variant calling analysis. So you start with some re-trimming and removing adapter, trimming for quality and so on. So that's what you see here on top. Then there's the alignment steps. And then we move on to variant calling. And in parallel to that, it's useful to look at some various statistics. So in terms of the workflow, again, we start with quality control, which is very important, especially important for variant calling, because if your reads are very noisy and have lots of mistakes, that's obviously going to affect your variant calling. So it's good to take a look, because especially if you have a project with many, many datasets, it's good to make sure that the profile of quality of your reads is similar across different individuals, because otherwise you might end up with some outlier that have lots and lots of variants. And you wonder why? Well, it's likely because it was a problem with the sequencing and the quality of the sequencing is not as good in some of the samples. So quality control, very important, preprocessing. So this, again, is what you were doing with Matthew yesterday. So trimming the reads, removing adapters, and so on. The mapping step, which is quite important. And again, that's what you did quite a bit yesterday. And now we move on to the two other sections. So calling small variants, this is what we're going to cover. And then after the coffee break, calling larger variants. Okay, so again, one more slide on this quality control, but it's really, really key that you look at your data before you start and at every step of the way. That's why IGV is such a great tool, for instance, after you've mapped. And as you'll see, once you call variants, it's good to look at some of them to see if there's any patterns or if the data looks reasonable. So that's really what we're going to be doing. And then a couple of comments I guess that I've made already, which is, we're all the sample sequence at the same time. Are there some samples that maybe are a little bit weird? So it's good to look at. And that's why many of the pipelines sort of generate statistics throughout. And it's good to look at these statistics to make sure that today is quite uniform. And of course, a lot of times I get, I mean, we get questions on what's good or what's bad. I mean, in some ways it's hard. It's more like you want to make sure that your data sets is uniform. And then at some level, you get more, with experience, you get more familiar with what values to expect at some level. Okay, but this particular module, so what do we want to do? So we want to call single-nucleotide polymorphism and indels. So the goal is really a little bit like we were doing yesterday, but yesterday we were just scanning it visually. The question is, so this is an example where imagine that to the top, it's probably is a tumor. And then the bottom is a same sample from that individual, but from the blood. So what you see is that you have perfect matching to the reference, except for maybe a few errors that are sparse. So these are probably just sequencing errors. But once you sequence the tumor from that same individual, there's clearly a difference at that position.  How does the computer know that this is an actual position? So this is now a cartoonish version of the same thing. So the sequencing, the sequencers are great. By now, like Illumina sequencing, 99.9% accurate for the most part. But it still makes errors, I guess. I mean, maybe one, I'm not sure I'm ready to do mental math at this stage, but imagine if you have 100 bases, right? So say it's 1% errors, let's make it easy. 1% errors, you have 100 bases read. So you have one error per read on average. But then sequencing run, you have 100 million reads. So you do have 100 million errors in your data set, right? So how do you distinguish the errors, which are going to be sort of random from an actual SNP where, and that's why we sequence such that every base is covered multiple times. Because if you only cover a base one time, you won't be able to tell that this is an error, if you only, or a SNP. But because we cover the genome multiple times at every position, and that's really one of the requirements to be able to do there in calling, we're going to see this SNP multiple times. So this is what we expect. So we can distinguish the two. So Matsuya mentioned already the fact that in the context of the sequencing itself, even when it reads a base, it itself has some information. Because sometimes, because every base that's read, the sequencer will report its error or predicted error, right? Because I mean, it estimates that just by how clean the signal is for that particular base. So depending on whether the signal is very ambiguous, a very clear or ambiguous, it's going to associate also a quality score to every base. So there is information that comes from the sequencer also that says this base looks suspicious. We call it an A, but we're not really sure. So this is converted into a quality score of every base. So as typically, again, we hope or we expect to have a quality score around 30. That means the base, there's 99.9% chance that that base is correct. So when we do the SNP discovery, we also want to integrate that quality information. Because even if you only see one read, so here you get the reference on top, on the bottom. So you see you have the reference on top. So when you read, even one read, there's a difference between some reads that say, I see a T here and I have a high quality that this is a T versus I'm not so sure. I think it's a T, but I'm not sure. So this information that comes from the sequencer about the quality of every base, we also want to integrate. So this is a slide that I'm going to be quizzing you all at the end on. So this is the actual underlying. So this is for GATK. This is the underlying algorithm or calculation to basically predict whether it's a variant and how likely it is to be a variant. And the details of the formula are not important. What's important is what goes into it. So given the data at a given position, given all of the reads that you have at a given position, you want to calculate the probability of a genotype. So you want to calculate the probability that it's a variant, that it's different from the reference and have a score. So that probability that it's a variant will be converted into a score that will use downstream. There is part of the reason why this is a little bit more complicated than why the formula is a little bit more complicated is that every position, especially in the context of the human genome, you have two chromosomes as we saw yesterday. So in some case, just like in the example I had here, or no, this one's better. So in this example, if there's a variant, you don't necessarily expect all the basis to be different from the reference because you might have one chromosome that's different from the reference and one chromosome that's the same as the reference. So this kind of profile is expected because you've got the two chromosomes. So the formula also has to take into account that at every given position, you can have two alleles. So that also goes into the equation. But again, the detail of the equations are not important just to know that this takes into account how many reads are saying that there's a difference, takes into account the quality of the basis. So you see that here actually. So it also takes into account the quality of the basis as reported by the sequencer. So again, this is just sort of the mathematical representation of what we're doing with our eyes when we're looking at it and we count how many reads say that there's a difference and it, but it also incorporates the quality score and the fact that we expect potentially two different haplotype at the same, two different genotype at that position. So, showing up a little bit. So just to re-emphasize some of the things that Mathieu talked about, there's a few additional things that beyond just that formula that calls how likely is it that it's a variant given the data. There's a few things that are important to do. So these are four steps and we're gonna go through these steps. Well, you've done some of these steps already yesterday. So local realignment, if you remember. So one problem that happens is that when the reads are mapped individually, right? So in the step of mapping where you're mapping the step, they're each read individually. But when you're mapping each read individually, the best way to sort of align the very end of the read, you don't have much information to know how to align the very tip of the reads like this. So what happens is that you tend to make systematic errors where the mapping basically makes the same mistake multiple times. But by making the same mistakes multiple times, it looks like very good. If you compare it to the previous part that I showed, these look very good as variants. So if you simply take that data and feed it into the magic formula that I've showed you, you would probably be calling variants here and here. So this local realignment step just identifies all the regions that have variants and then just double check that there's no way of repositioning the reads in a better way to explain that region. But now you're using the information from all the reads at the same time, so you're doing a better job. So that's what you see here. So if you just realign the reads, you find that there's a better way of doing all of this mapping that actually can now explain only just one indel. And it's really around indels, sorry. It's really around indels that these types of mistakes end to happen. And that's why it's useful to do that realignment step. Okay, so local realignment. Duplicate marking, which you also saw yesterday. This is to recover from potential PCR artifacts. Again, in the context of the variant calling formula, it's gonna be looking exactly for these kinds of patterns of multiple repetition of a change, right? So, but the problem is something else can lead to this pattern, which is if the same identical DNA fragment ends up being PCR-amplified, you're gonna basically, and there's a mistake, there's an error that was inserted, you're gonna basically be reproducing that mistake, so it's gonna look like a variant. So same thing, so removing reads that are identical, the pattern that you would want to see or that you would expect if I go back. Oops. If I go back to this, this is the type of pattern you want. You want reads of all sorts of different reads that are pointing to that variant. You don't want only the identical read that might be a PCR artifact to point to that variant. So by compressing the, you still keep one, but you compress it to just one, such that it's only gonna be counting as one piece of evidence for the calling. The last one that you've covered as well yesterday in the steps is this base quality recalibration. So if you recall, this is just because the quality score of produced by the instrument for every base is used in the formula to calculate whether it's a variant or not. If the sequencing instruments make systematic mistake or systematic mis-evaluation of the error rate, that's gonna translate into bad variants being called.  So the last one that I don't think you covered yesterday that's also important is population structure and imputation. So what do I mean by that? So this is when you have more than one sample. So when you have more than one sample, and suppose that, so you have two appetites. So that particular piece of chromosome has only two version in your population. And that's typically true. There's not so many appetites in the population. So suppose your population only has either the ATG or CGA in that region of the genome. And then your sequence and all you see are reads of this type below. So can you guess the value? Let's see if you guys are awake. So can you guess the value of N? Yes, what is it? T, okay, correct. So here notice that it's quite, what you did in your head, it's quite different from the formula or what I was saying before. Everything I said before was you're just looking at the evidence at one particular position. And you make the call based on that particular position and the number of reads. What you guys ended up here doing in your head is the fact that because the human genome is organized such that you actually always give segments of chromosomes or haplotypes, there is sort of correlations between positions. And then when you have information about neighboring position and information about like I have here in the population, when you have a C here, you always have a G or when you have an A here, you always have a T. So you can combine information from your population and things you've seen in other sample to basically make prediction at the position. So this type of varying calling is a little bit more elaborate where you're actually using this information about the fact that there's correlation between variants when you're calling them. So again, this is a slightly more advanced way of improving variant calling. When you have multiple samples, is that you're using the structure of haplotypes to improve it. So to do that, you actually have to call variants using multiple sample at the same time. And this is especially useful if you don't have very high coverage. If you have high coverage in a position, right? If you have high, then actually you don't need to use haplotype calling as much, but there are projects or instances where all you have is, you know, five X coverage or three X, so every position in every sample ends up being covered just a few times.  So this is where using, you know, did I observe it in other sample might help you as well. But again, this is a bit more technical. But it's just, it's been shown that, you know, using multiple samples to improve variant calling can really help you. And that's especially true if you don't have very high coverage. So this is just a demonstration that the multiple calling, multiple sample calling ends up, can improve your variant calling. But this is, you know, if you have, yes? I'm just not sure. So you sequence it separately, but to call variants you make use of information in other samples because, right? So you don't call one sample at a time because in one sample, I mean, so assume you only have, you only have five X. So most positions in the genome only have five reads. And so if you see a variant in one sample at one position, but you only have one read that tells you it's different, you might not know, you don't, you won't have enough confidence to distinguish it's an error from, it's a real change. But if you have a hundred individual and in the hundred individual, you often see a change at that, the same change, you can use that information to give you confidence it's probably real. So if you wait the confidence difference three, if it's a sample sequence multiple time, I mean, re-extract and re-amplify the sequence compared to multiple times. Yeah. Wait in the same difference three. Yes, so I mean, if you're, I mean, this type of, you know, identifying saying I have multiple samples to make the call would be used in the algorithm. If you have multiple data sets for the same sample, you can also use that. And actually there, there is especially if, so suppose you sequence twice the same individual and the variant, you only see it in one of your batch of reads, that's probably a bad sign. So that's another thing that, so it's very useful if you can verify that indeed that you have reads that are coming from both sides. A little bit like, that's one of the things that's nice in IGV, for instance, you might want to double check that you've got reads that are going in both directions that are coming from both read sets that overlap in that call that variant. Oh, right. No, no, absolutely. So you can also do that to verify that the calls are reasonable, for instance. So that's very useful in annotation as well that you can use on the variants, right? So once you've called the variants in the, if you have a trio, you could annotate the variant and make sure that inheritance makes sense, right? And that, which is another way you can actually improve your quality of variants. And using like thousand genomes. Yeah, so we'll get to the annotation, I guess I'm getting into that a little bit. I mean, both, here we're really just calling and then there's gonna be lots of ways of annotating the variants to further filter or improve them, but we can look at the frequency in thousand genomes and things like that. This is still not using any other data than the samples you've sequenced and how best can you call the variants just using your sample. Yeah. So within GATK, you have two modes in which you can run GATK. You can use it, you know, sample by sample, but you can also feed it multiple sample and use this Apple type caller module. No, I mean. So, I mean, again, so the difference is whether it's just doing the relatively simple form, well, relatively simple formula that I showed, which is basically only using the data from one sample and the number of reads and the quality or whether it's incorporating into the score some information that's coming from the other samples and the Apple type. Yeah. Sometimes you don't get some. That's right, all right. Well, no, so the Apple type, if you're using the Apple type information, it's using a different formula that incorporates this structure and this is what this plot was showing is that they've shown that the performance is also better, especially when you have not a lot of reads, right? Because if you don't have a lot of reads and that position in that sample is only covered by a couple reads, sometimes it's gonna get it right, sometimes it's gonna make a mistake. If you then do the same at the same time, you're looking in multiple sample. In each sample, the calls will be a little bit better. So if you look at the GATK framework, which is really what we're following for our variant calling in this case, and this, so you had module two yesterday, which is really, you know, we did this, the mapping, the realignment, the duplicate marking, and the recalibration in the context of the variant discovery now, especially if we have multiple sample, and this is what we're gonna do in the practical. I'm not the practical of this one, more the structural variant, but we'll do this distinction of calling variants using just one sample or using multiple samples, but this is gonna be in the second practical, but still you get the idea. After the mapping, we're gonna actually be doing the variant calling. So to give you a sense of the size of the data sets and the time, the various steps take. So yesterday was mentioned that, you know, the files that come out of the sequencer, if you're sequencing a human genome are in the order of 200 to sometimes 500 gigabytes, if you have high coverage. So the first step, well, so this is after the mapping, right? We get the band files that are roughly this size. Calling variants across the whole genome actually takes quite a bit of time, whether you're using GATK, which is what we're gonna use, or there's other tools, SAM tools, FreeBase, there's other ways and slightly different formulas about you actually calculate where the positions are of the variant. So we're focusing on the GATK framework, but again, there's very similar strategies that are used by other tools, but if you're processing all of that data across the whole genome to call variants, it takes 10 hours or so. So for that reason, we won't be doing it on the whole genome. We're gonna be doing it just like yesterday only on one particular portion of the genome. But what you get after this are really a list of variants and this is in the VCF format and we'll go a little bit over this format. So these are all the sites that appear to be different from the reference. The size of those files are much smaller because now you're not keeping all the data, you're just keeping the data of the positions that are different from the reference with all of the extra information of the evidence that you have. So now that we've called the variants and so on, I'll move to the second section of what we're gonna be doing in the module, which is, and we touched on that already, how do you filter and how do you annotate the variant? So I've been talking about this VCF format. So if we look at it a little bit at the structure of that format and there's links on the website with the full description of what that format is all about. So the VCF file starts with lots of header files that just have information of what's down below and how things were calculated and so on. So what did you use as a reference? Information about the various information because this is sort of encoded in the way that then is a bit cryptic, but you have some information about what's in the file, you know, the main, so it's really just a file where in previous files you had read that corresponded to lines. Here you have every line is really one of these variants. It identifies the position of the variant. So in which chromosome, the position, whether you have a DB SNP ID, we'll get back to that. But basically it says, you know, the reference genome at that position has this sequence and we've observed a variant and this is the variant we've observed a quality score, a bit like what we had with the mapping quality, but this is now a quality of the variant itself at some level, whether it has passed. So the variant caller will report whether according to its algorithm, it actually qualifies as a good variant. So it's passed the filter and then information, more specific in the number of samples, the depth of reads that covered that position and so on. But we'll look more at some information, but that's the rough format itself, really one line per variant, some information about what's the reference at that position and what's the variant that we've observed. Yeah. Is the quality score? So it's Fred's scale, but it's the way it's calculated is not, there's not an interpretation that's as simple as, you know, probability of error, but that's still the scale, it's the same scale. But I say that it depends on the variant caller itself. So sometimes they actually use a different scale. So the mapping quality scores usually use the Fred's scale and then depending on the variant caller, they're not necessarily using the Fred's scale. It's really, so you really have to look specifically at the algorithm and what's the quality score that there is. I use it more as a ranking usually because different algorithms use different scale actually for the, yeah, it's more, yes. And then for the specifics, it's good to look into the manual of the software itself. All right. Okay, so from the variant calling, we'll get one of these raw VCF with all the calls. What we probably wanna do, some variant filtering. So typically when you get the raw variant calls, you have a lot of false positive. So how can you filter? So one way is to filter directly based on specific parameters. You can have a filter based on this quality score or depth of coverage. You might say, well, I only trust variants that are above a particular score and that have 10 reads covering them. So you can come up with yourself with some kind of rules of what looks like a good variant. And one way is to look at some of the variants in the browser and then the side. And again, there's some, but still it's a bit arbitrary. It's sort of required. And it's not clear exactly how to define this. By now, there's better ways of filtering the data in particular within GATK. There's what's called variant recalibrator. So this sort of builds up a set of rules itself based on the raw output of what's the score and what's the depth of coverage. It sort of will reorder and filter variants that look suspicious. And the way it does that is using known variants. So so far it wasn't, we were really doing this sample by sample using only our data, but there's a way to actually use known variants to improve the variant scores and variant ordering. So that's called variant quality recalibration. And the idea is that you actually use known variants that are coming either from the HAPMAP project to see whether you're actually missing out. So you can use the very good quality variants from the HAPMAP project to know whether you're missing out a lot of variants. So you can also use, DVSNP actually has a lot of variants that are errors in some case that have been detected in other project and reported as been observed before. So some of the work that was done by the team that set up GATK was to look to see which variant, you know, what was the profile of some of the, you know, overall of the variants call of the, you know, you have a set of very good variants and a set of more suspicious variants. And using that and comparing the scores, you can recalibrate the raw scores that are outputted to sort of filter out. You learn sort of from previous mistakes and from good data, you learn from that and you recalibrate your score such that you can filter some suspicious looking variant. So that's one of the steps that's implemented in that workflow as well. So you input what are known as good variants and bad variants to sort of fine tune the variant calling algorithm. So this is the filtering step that's recommended. So again, if I go back, you know, you can have your own set of filters if you'd like. So you can set, I want to look at things that are above a certain score, that are above a certain coverage and so on. You know, there's reasonable ways of making these choices. Something that's recommended in the context of using the GATK framework is to give, to make use of known wheel snips and known potentially problematic variants to do that filtering automatically out of your data. So that's one of the steps that we're gonna cover as well. So on a slightly different topic now, not a filtering but of annotating. This is a project that I was part of that I wanted to show as sort of a motivating example. So in this particular project, and to give you a sense of the challenge ahead. So this is a project where we sequence a hundred Hoji Nome kidney tumors. And when we did that, we're now looking at germline variants but at somatic variants. So these are new mutations in the tumor. But if we sequence a hundred Hoji Nome, we detected more than half a million point mutation. So every little square here is a thousand mutations. So we detected over a half a million change. So on average, each tumor had close to the 5,000, 5,000, 6,000 mutations. So there's mutations everywhere. So one challenge is how, where do you start looking? So every tumor had on average 6,000 mutation. One obvious annotation is whether these are coding mutations. So you see that there was small subset of these mutations that are coding. So that's great. And that's one basic annotation you can do. But it's still then a little bit unsatisfying because you've sequenced the whole genome, you detected all of these mutations and there's only very few that you're able to annotate. It's a very small subset that's rating the gene. Those are still the interesting one and you want to annotate them as such. But it's just, you get a lot of mutation and you don't really know where to look. So how do you know where to look? So this annotation step is quite important. There's tons and tons of data and we start talking about some of them, 1000 genome and BBSNP and so on. So especially if you're sequencing something like the human genome, there's a lot of annotation in the human genome. So it's important not just to take the raw VCF or the filtered VCF, but to also start adding information about whether it's hitting a gene, whether it's coding or non-coding. If it's non-coding, is it hitting some of these encode elements and so on? So there's different ways that this annotation can be done. The one that we're gonna be using in the course is this SNPF software that was actually developed by Pablo Singalani. That was at the genome center for a while. So it doesn't, I mean, it has the advantage of already regrouping many database and doing many annotation all at once. So to go back to this overall pipeline and the size of the data sets and the time it takes. So typically, so by now we were at this stage where we have much smaller file which are these raw VCF, the variant call. So it's good to then use potentially GATK again to filter these variants and then annotate. And there's different tools, but annotating these variants such that you know which ones are coding, you know which ones are more likely to be impactful and so on. So I think that's all I had in terms of an introduction before going to the actual lab. So, yeah. Kind of a tough question about the tumor sequencing. So tumors are so difficult because typically you're dealing with a mixed sample. It's a bunch of good, healthy cells and tumor cells. So you're not looking at an even distribution. So you might have a mutant there only has a certain small percentage, maybe five or 10%. So how much extra sequencing coverage do you need to get to be able to convert and call? So not only do you need more sequencing, you also need, so the formula I showed had assumption on the genotype and the fact that it probably, it expected basically roughly 0%, 50% or 100% to call a variant. So you also need to adjust and tell the algorithm I actually don't necessarily expect these proportion and I want you to call variants that are at 10% or 20% because if it's a mixed sample and you might have a somatic change that it's in 20% of the cells. So you don't necessarily expect the same proportion. So you need to make, hopefully have sufficient coverage and then you're still limited because if it's a very small, the advantage though is that typically unless you're doing something very fancy, you're looking at something that's probably present in most of the tumor cells or something like that and not the very, there are other application but typically you're looking for variants that are not the rarest variant in the tumor. And so in that sense, you don't necessarily need super high, but you definitely need to adjust the algorithm such that it knows that it doesn't necessarily expect the 0, 50 or 100% of the read to be calling a variant. So that's something that you as the buyer and the petitioner have to do or do you have to? Well, so there's specific variant calling software for somatic mutations. So something like UTEC and so there's variant call so there's already where you, and you also you need to call the variants in some case, it's even better if you give it the two sample, the normal and the tumor and then you really sort of adjust and change the parameters and it's a bit called specific, yeah. So to show the formula at the beginning, and you highlighted this part and says it's a bit deployed so does GTK expect you to use to work with the deployed organism? So there's settings there too. So you can specify whether it's not the case. That's by default is the expectation but you can change that as well. But yeah, that's definitely something that if you're not dealing with a deployed genome, you need to adjust. Yeah, from the center still, but it seems to be... It's true that it's definitely tailored for that, but I mean, based on the setting, the raw variant calls, for instance, have all of this information, right, initially. So you can still revisit them but it's true that the quality or the filtering, for instance, would not apply if you're actually looking at the population. That's right. I mean, so there's definitely... I'm less familiar with them, but there's definitely callers and specific settings that allow you to do it in the context of population, but you need to... Maybe let you know. Population, or it tends to use that kind of the pool. The pool of population, you have in your data and you use it again in your database to call in the form of some tools, and then you use it in your database. You tell it, I estimate to have this amount of capital type in my data, and you go do the coding in that information. That would, I think, be popular. What's the name of the software? Population. Population. Yes. Put a note on the sticky note. Yeah. I was going to say, for that one slide, when you're like, okay. Right. So that's really... So it's GATK Beagle. So I mean, this is an older slide. So GATK Haplotype Caller. This is similar to this GATK Beagle. So that's the Haplotype Caller module within GATK, I believe. But again, you only get this type of boost and improvement for very low sequencing. So this is a thousand genome type of sequencing where there was 5x sequencing, and then, of course, it makes a huge difference, right? But otherwise, it makes a difference, but it's not as incredible. It does because even if you have 30x sequencing though, you do have some regions of the genome where typically you have much lower coverage. And again, it's going to help you in those particular regions. But in regions where you have sufficient coverage, it doesn't help you that much. There are some bias in them that we might use the real variant because of boosts in the... So the realignment, right, is based on not the indel calling itself, but just variants. So regions that have lots of variants get realigned because there might be indels. So the indel realigner component is based just on... Because if there's an indel, it's going to lead to lots of other variants around it, and then that's what gets realigned. Indel calling is still... I guess I didn't have much on that in the slides. It's still not nearly as accurate and as easy as especially slightly longer indels or in regions that are a little bit more difficult. The problem is that indel calling, if there's real indels, the mapping step breaks down. So this goes back to what Jared was talking about a little bit. If it's just a snip, right, you're going to get accurate mapping of reads in that region. If it's an indel, the mapping step that's the first step that allows us to look into that region is not as good, right? So indel calling is definitely lower quality in general. And we're not covering that in much detail, but typically, yeah, you have to look at those. Especially if you're not using any specialized salt specifically for indels, you have to look at those with grain of salt usually. And then, again, there's lots of other algorithms that specifically try to call indels, but by default, the quality of indel calls is not as good as single nucleotide variants. Next question is like, amongst all, we need to manually add those indels. Will it add what? After annotating with the database refining, do we need to add the info in the end section? Like, because for AC, like, all the info that is in, all the infrequency info that GD is, Gnode is. Right, right, right. Yes, you'll see that in the end of practical, in the end, so many of that will be added automatically. It's part of the SNPF annotation. Okay, so let's, unless there are other questions.


Well, thanks very much, Anna, and thanks to all of you for coming here early Sunday morning. It's great to see such a large group of you here. I'm really pleased to be here. In fact, I was here about six years ago, exactly, 2008. And I have a talk on it. I had a chance to give a paper on the health clubs, which you've heard quite a lot about this morning. So I'm not going to really go back on that. But I really would like to talk about those. Just kind of what's happened really in the last six years since then, because it was at that stage, it was still very much at an NGO kind of level we were really thrilled by what was happening. We were seeing in Zimbabwean and with other partners, with other NGOs that we were partnering with in different parts of Africa. But it was kind of an NGO kind of stuff. And as you heard from Honours when he gave the presentation from Dr. Fidel Ngarbo, who's the director of maternal and child health in Rwanda, suddenly it's got to another stage. And this is what we're really so excited about, because it's come out of the realm of NGO project stuff and it's got into the national thing. And we've been working as Africa ahead, working closely with the Ministry of Health in Rwanda for some years. In fact, since about 2008, 2009. And it's been really exciting. And it's basically this sort of, I'm trying to tease out what's actually happened in these last six years. And I think there are three sort of themes, I think, that I believe has made it go from NGO to where it is now. So there's obviously the whole issue of scalability. It's gone from kind of NGO to scale and a country like Rwanda where it's been taken, as you heard earlier, right across the country. There's also the whole issue of integration, how this integration has worked. And you've heard quite a lot about that from Judith now and moving into the fan club. So I think there's that whole aspect that's a strong selling point. I think governments, presidents, like the idea of an integrated program getting a really good bang for your buck. Certainly the Ministry of Finance really likes that. And in fact, the Ministry of Finance is able then to put a lot of pressure on the Ministry of Health to stop and only talking about curative stuff and to move on to preventative health. And the third thing is about quality control. How do you have a nice little model? But how do you keep the franchise going, as it were, sort of McDonald's? You know you're gonna get a good quality hamburger wherever you go. But how do you make sure the health club can keep up the standards? And I want to talk about that a little bit. But also just to sort of say how in terms of going to scale and what's happening in Rwanda right now with Ngarbo who couldn't come here, Dr. Ngarbo. Just yesterday, and that's why I'm slightly talking of peace and not going to be sharing my presentation right now because I'm not very good at multitasking and having the locked into the slides will just spoil things for me a bit.  You've seen quite a lot of slides anyway. So that sort of hopefully explains a lot of it already. You've got it in the mind, you've got the sort of sense of the pictures of what's been happening. But just yesterday we got an email from the head of environmental health who we've been kind of connected with for the last few years. And the news from him was that environmental health, the environmental health desk, it was a desk in the Ministry of Health is going up to directorship level. And so for us that's a really quantum leap. It's kind of like, wow, environmental health is now being taken really seriously in Rwanda. Where it should be? It's environmental health that saves the millions. It's not the curative hospitals that does sort of curing people that shouldn't have got sick in the first place and spending all the millions of dollars there. It's really stop people getting sick in the first place. And we know from those graphs that we're seeing and WHO and UNICEF sort of show us 87% of the disease burden across Africa can be prevented. So that's what we should be doing. Saving the millions and also saving the money. So anyway, those three themes, reaching scale and how to achieve integration and quality control is what I'm sort of arguing about can be achieved with this health club model. It's a recipe. It's just this very simple recipe that can be repeated. But as long as you kind of repeat it properly and there's some key ingredients that I think Julia's been talking about. But so maybe just a quick example of what we're excited about in Rwanda because it's there, little Rwanda, 20 years exactly since the genocide, everybody was totally alarmed by it. And this country has just suddenly surged. It's the bright star in Africa now in terms of development. More women, MPs than I think even in this part of the world. It's just, it's really strong, not just MPs, but Ministry of Finance, Ministry of Health. They're all women and they really are changing things there dramatically. And so there's really good leadership there and it's attracting the locals. So just a few weeks ago, I had to go back to Rwanda because we had a delegation of senior doctors, directors of health from the three Eastern provinces, North Kivu, South Kivu, and Maniema, the whole Eastern sort of swath of DRC. And they'd heard about this project going on. In fact, Rusezi, which you saw slides of earlier, Rusezi District in the extreme south waste of the country, that borders Burundi and DRC just across the river from Bukavu. And so all this delegation about 18 people came across from DRC to find out about this CHC model. Is it, does it really work? Because they've got a great model that they're very proud of in Congo, in DRC, called the Village Assani Project, the Village School and Health Program, Healthy Villages. And they're quite strong on that and it's well embedded into their program. So quite understandably kind of they've got a good program. What's this about CHCs? Is there any added value we can bring to our own program in DRC from what they might see in our program in Rwanda? And so this large delegation came across and we were hosting them together, as Africa here together with the Ministry of Health, to see how, you know, to show them around. And I'm sure a lot of you who've come on these sort of tours, evaluations or annual reviews for big programs and stuff, who have had this experience where you're kind of being led by the nose and being shown to the villagers and showing everything that sort of, that they want you to see. And the kind of the frustration one has of that when you're just being, the whole things that have been rigged and you've been taken to the village and expecting you that they've had lots of time to prepare. And I personally had this rather horrible experience a couple of some months ago where, you know, we were told a whole area, this is not Rwanda, this is another country altogether. And I won't mention names, but it was quite a wake-up call for me. Because we just have been complaining, why aren't we seeing, your report is saying all of these villages are ADF, can't we just duck off the road anywhere and have a look at some of these villages? And they, you know, because we haven't seen much evidence yet. And so sort of they said, okay, we'll take you to one tomorrow morning. We'll collect you from the airport, from the hotel and get you to one of these villages. And honestly, they collected us quite a couple of hours late from the hotel. Then we drove for a good couple of hours. So it was a good four or five hours before we actually got to the village, sort of late morning by the time we got there and got out and nice little village and everything looking great. Went to the one home, lovely toilet, all nicely swept, nice little bowl of ash, nice lid on the top. Went to the next one, same sort of story. Eleven in them in fact, just went on to all of them.  So, and that's what you don't want obviously. So anyway, took this, no flies, no smell. Well, this is quite impressive. Had a look inside. It was about that deep. And it had never been used. This is this ODA village.  Went on to the next one. They were all exactly the same. They'd all, they were dummy latrines. They'd been built in a few hours that it took us to get there. And so this was a major program in a significant country using a lot of donor funds. And this is what we saw. Complete cover up. And so anyway, so I had, I had the feeling this crowd from DRC, doctors, senior running whole programs weren't that going to be that interested in a pre pre rigged thing. So we gave them a list of the, of the villages, the 50 villages. Here we are, this is the date they were started. This is how many, you know, how many members we've got in this club. Here's the list, you go and choose.  And they divided into three teams and we all went off. And I, you know, it's really sort of, you know, we're pretty concerned about this.  And it's a program that had only been going two or three months by that stage. Anyway, off, off we went and drove the village that I, the vehicle I was with, with one of these directors of health from Maniam province, went along. And we drove for a good two and a half hours to the village that he'd selected, off dirt tracks, winding along. And then we sort of stopped, there was a few people around, we thought was, you know, this, I wonder if this is the village, let's stop and ask somebody around. So we got out of the car and saw a little old lady sort of peeping out from her hut, a little old man hut. And they asked her, they will speak the same language there, just across the border. And they asked her, you know, is this the village that we're looking for? And she sort of nodded rather nervously. And then the guy said, well, are you, do you know anything about health clubs? So she sort of, again, nodded, not wondering what the nurse was going on. And then, well, do you have a membership card? And then she ducked back into her little hut and came back with a green card wrapped up in plastic and showed it to us. And we looked at it and half the topics had really been signed off and she was sort of getting quite pleased about things. And then she took us into her home and we wandered around. And I mean, it was just stunning. Everything we've just been hearing about, you know, it was beautifully swept. There was a garbage pit there. There was a hand washing tap there with soap and water. She hadn't had time to rig it and put it up. The toilet was there. The cover was on. It was a clever thing that you use your foot. So it's a kind of a hands-free way of changing the cover. You always know there's a point of contact. It's just asking for trouble to have that. So nice sort of foot thing. And it was just there. It was perfect. It was an efficient stove. We were totally amazed. And she was old. I mean, she was about the age of my mum. She was about 90, 91.  And she said, well, it wasn't just me. I'm part of the club. They all helped me. They came. They fixed me. They made this stove for me. So anyway, I was actually delighted, obviously. And the team I was with were Julie and Prist and they were quite convinced. And we went on like this. And it was really great. They had this randomized idea of looking and finding out things. So we got back and we had the three days of workshop and they were all well and truly primed. They thought, yes, we can introduce this. So anyway, this is kind of going to scale. And I just, so how did we start in the first place? And I think in Rwanda, I was with an advisor with WSP, with the Ministry of Health with sanitation at the time. And I had that introduced to the Minister of Health by the head of the environmental health desk, now the directorate. Anyway, the desk officer introduced me to his minister, Minister Richard Sezibera. And who actually is now the, he's the Secretary General for the whole of the East African community based in Arusha. So he's gone to dizzy heights. But anyway, this is the kind of minister that you want in every country. He was just sort of, he asked me to sort of tell him a little bit about the health clubs and how they worked. And I did my best. And I sort of said, okay, well, you know, could we try them? I think it might work in Rwanda. Could we try them in a couple of places on a pilot basis? And you go, Anthony, we don't have time to pilot in this country. You've been piloting for years. No, go, just give me a roadmap. So anyway, this is the roadmap and we did it. And he signed the forward and you can see it. But it was really just such a great start. And we did this and we did it in Kenya, Rwanda. And just sort of what we thought how it would work, the whole principle, how they were. Anyway, so he went off and he talked to his cabinet colleagues. And if we had a printout in Kenya, Rwanda.  So suddenly we'd gone from some NGO project work to a whole national program in a country like Rwanda. And now we're seeing it sort of spinning off across into DRC. Burundi is interested. Tanzania we're really talking about it. We'll hear a bit about Uganda. So it's kind of like from one little place it's beginning to spread. So that's the exciting side for us, sort of going to scale. This whole idea that we can reach scale. And I think the other thing was just going back to what was it about that appealed to Dr. Richard Cesarbera, the Ministry of Health. And I think it's a lot what Anna was talking about earlier. And I think it was kind of like seeing that round pie graph that we were looking at. If you just think of that as kind of like a target for dots. And it's sort of like instead of one dot going for that little dire little segment the whole time. It's like getting a whole fist of dots and hitting the whole dot. It's just kind of this idea of an integrated approach.  From a Minister of Health's point of view to hit that whole target is what they're looking for. It's really exciting to them. And when he starts talking about that to his Minister of Finance and to the President and everybody, they say, well, for goodness sake, if this thing works, let's just do it. So I think it was that sort of idea that it's practical, it's low cost. It's using their own existing staff, the extension staff, and the people in the villages. So it's not waiting for a donor to come and dish out any money. This red light doesn't talk about money. So everybody's saying, but where's the budget? How many millions do you need for this? There was no budget, deliberately no budget. It was just what can we do ourselves to get things moving? So I think that was the kind of the whole integrated side of it. And you saw a lot of that already. And the final thing is the aspect of quality control. And I think Julia also mentioned about the Gates Foundation coming in. Because again, this was a kind of a seeing-as-believing moment with the Gates guys who talked to them at a conference like this. And they said, come see what's happening in Uganda, in Rwanda. And they sort of said, yes, OK, let's have a look. And they've got carried out funding, a lot of money to do a randomized control trial, massive thing. And that's running right now. And so in a way, and they funded us to be able to do the quality control really. But it's a classic CHC. So we could bring in all the things, the four T's, the trainers, the training, the training materials, quality training materials, piece of paper about training materials and transport, motorcycles or bicycles, so that the guys can cover the villagers out there. And for environmental health staff to have motorcycles and be given this kind of stuff, it's great. So it's just making sure that the recipe was done in the proper way that we had had experience of. They started 20 years ago in Zim. So this is sort of a lot of refinement and getting, bestilling out what are the key ingredients. And that's what's happening in that RACISI program that you heard about earlier with those results. And I think that was really, really a thing. So I think, time for the next three of these things. Very fast, if you like. OK, so it's the five million. That's the target we're talking about. Just, I think you've seen them. All of the PowerPoints are going to be available on the net. We'll have them all uploaded for you. The RAC we went among, that's a little old lady that we stopped and asked her if she, if this was the right village. And then she is finding her card with her card and so on. Anyway, I will. If you need this presentation, I think it's on the Africa Head website. I just must do my training. I just, one thing I must show you. That's integrated development, I think. This is the five dollars. And we're part of an energy thing. Just both on your energy component as well. Anyway, thanks very much. Just one very, very short question. I know I cut the gentleman off last time. If you have a question now, you're more than welcome. Or if you want to ask that question from before, you can do that. Otherwise, we will tip to you. You'd have to come to the podium for that. Thank you very much for the opportunity. I'm Gordon Chinnemarim from Nigeria. OK. On the six months training course, the course you do for the health club members, I would like to know what's your vision. Do you hope to train the entire members of the community? Or do you hope to get some people trained that were in town? Train others? I also want to quickly say about our program in Nigeria. We have a voluntary wash club. What we do is we get students willing to volunteer. They are trained on issues surrounding water, sanitation, and hygiene. They now go into primary schools to establish wash clubs. They also go to communities and enlighten them on wash issues. Last week, we picked about three of them and we trained them on boho drilling in Kaduna National Water Resource Institute, Kaduna. So I want to say we would like to bring in this, your concept of explaining to the community, training them, giving them certificate. I want to say that that strategy is a very good one because in Africa, people value certificates. But it gives them all the certificate and say, oh, you've been trained in this area. That's what we're excited. Like what you showed there. You saw people excited about that. So we are interested in bringing this into communities in Nigeria. We hope to partner with you on that. Thank you very much. Maybe, yeah, again, that's not really clear. We sort of take it for granted. What we do is, OK, look at Rwanda, I developed a training manual for Rwanda. Not this one, but we've got a Rwanda one. I then trained core trainers, 25 core trainers for Rwanda. I did the training myself at that stage because we didn't have such a team then. So I trained the trainers. Those trainers are working with Ministry of Health. Those trainers do other training in districts. They go out and they train all the environmental health staff in each district. So you've got 30 districts. You work your way around the country doing the training in each district with your core trainers. Then when you've got your district trained, you've got core trainers in each district, those district trainers who are still Ministry of Health, right? You're not paying them anything extra. That's their job. Then they train from the community. So now say one environmental health technician has got five villages under him, right? He's gonna train one person from each of those villages. Just community members. They've gotta be fairly literate. They've gotta have a good recommendation from be kind of volunteered by the community for the service. It's a voluntary service. They get trained and all they get for it actually is usually just a t-shirt. If the project allows maybe a bicycle as a sort of form of recompense for the training. And then they train their own community. So it's not you coming in from outside training it with your project officers. It's the community training themselves. And that's why it's sustainable. The trainer remains in the community. They remain with their toolkit. They know what they're doing. They can carry on and do reruns of the same training at Infant Item. That's how you get to a country just one by one like that.


Welcome back to the Versus series. I asked for suggestions the last time I did one of these kinds of videos, and one that stuck out to me was a philosopher versus SCP-343. Now poorly written, though I think SCP-343 is, a philosopher arguing with God is not really the kind of story I could see myself passing up, so I decided to write that. This is one of those situations where I just kind of started with a premise and wrote a story and saw where it took me, and it kind of ended up surprised at the end. This happens a lot, where I really only have a vague idea of what the story is going to be when I start, and I just write what feels natural in the scene I'm building as I'm writing it. This is definitely one of those cases. And I will warn you, this one's a bit heavy, so let's get started. Dr. Voss groaned as he turned the corner. His hip was aching again. He stopped at the door and nodded to the guard. Then Voss waved a keycard over the door lock. The lock flashed green, and Voss listened to the loud click of the mechanism. The interview room was much better lit than the outside hall with eggshell-colored walls and white marble-tiled floors. Moving from the carpet of the hall into the tiled floor, he could feel the hum of the reality anchors underneath become more noticeable. The man sitting in the center of the room was SCP-343. 343 had dark brown hair that was graying light skin, blue eyes. His beard was very short and neatly capped. Dr. Voss laid his clipboard and pinned down on the table between them and sat down. My name is Dr. 343 leaned in. Voss, I know. This will go much faster if you let me get the preliminary information out of the way. Most of this is for the benefit of the people who listen to the logs later. 343 nodded and leaned back in his seat. My name is Dr. Victor Voss. This is the first interview of SCP-343. It is being conducted on July 25th, 2008. Subject of the interview is SCP-343. SCP-343 cocked his head to the side and smirked. You know you can just call me God. I'm sorry, but I can't do that. If you have a real name, we could just use that. SCP-343 shook his head. Yahweh, Elohim, Brahma, Allah. Take your pick. So it's your contention that you are, let's say, God with a capital G. Well that's just the truth. I am that I am. Alright, well if that's the case, how long did it take to create the universe? SCP-343 chuckled. You may be a philosopher, but you've got a rudimentary understanding of physics. You can't measure time before time exists. Space-time just is. Dr. Voss shifted in his seat. Okay then. What color was Moses' hair? Brown, then gray. And how many innocent people have you killed? I don't kill people, Vic. Sodom and Gomorrah would indicate otherwise. Those are stories, written by men, trying to understand the universe. That was just a meteor shower, not me. But you allowed it to happen. I don't allow things to happen. I granted you free will. Then why do SCP-343 raised a hand and Voss stopped in place? Why don't you ask me what you came in here to ask me? Dr. Voss's eyes went wide as soon as he could move again. How did you do that? SCP-343 shrugged and rolled his eyes. Dr. Voss nodded. Alright. What happened to my sister? SCP-343 sighed and closed his eyes. You were ten years old, but she was eight. A minivan stopped by your house. A man asked if you wanted to go to the park. Your mother was just inside the house, but out of sight. You said no, your sister jumped into the open van, and he left. And? And you never saw her again. The funeral was closed casket. And why did you let it happen? SCP-343 was no longer sitting, instead was standing behind Dr. Voss. 343 reached a handout and placed it on the doctor's shoulder. Son. I didn't. You did. You could have stopped it from happening. You... And what about you? Why didn't you stop it? I was only ten. Events shaped people in unexpected ways. Not having a father made you not want to have kids of your own. Your mother's suicide put you into the system and made you want to fight for those who can't fight for themselves. And your sister made you unafraid to speak up when you know something bad is about to happen. So last year, when the Scarlet King almost escaped, speaking your mind saved the world, you wouldn't have that, otherwise. Dr. Voss was shaking as he turned to face SCP-343. That's a lie. You could have done something. It is a lie. But it's a comforting one. Dr. Voss sneered. He pulled a small reality anchor from his coat pocket and placed it on the table. A blue flash bathed the room as a strong vibration shook the table. Voss pulled a shard of dull grey metal from his other pocket and turned to lunge at SCP-343. 343 knew this was coming and was out of the way immediately. He sighed and then he snapped his fingers. And Dr. Voss could no longer see anything except for himself. Even though there was no wind, his senses told him he was falling. The endless black stretched in all directions and then he saw it. He saw himself walking backwards from SCP-343's room. He saw himself getting back into his car, driving backwards towards the house, showering, going back to bed, sleeping, getting up at night. All of it played in the distance, but on a screen so large he couldn't conceive of it. But still, he could see it all happening at once. After what seemed like an eternity, the falling stopped. And he landed on his childhood front lawn. And there she was, his sister, smiling and laughing. His mama just went into the house to grab something for them to drink because it was very hot outside. A dark, grey mini-van rolled up. The man inside opened the passenger side door. Hey, you guys want to go to the park?  For a split second, he was paralyzed with fear. Then he surged forward and stood between his sister and the van. She acted like it was a game, trying to get around him. And then, out of nowhere, Victor felt a pull in the back of his collar as he just dragged up into the van. He kicked, he screamed, and his sister stood frozen in place. The world shifted again. The black void spun around the scene. Everything reversed again. The little girl yelled for her mom. She talked to the police. She attended a funeral. And eventually, Dr. Valerie Voss set at a table with SCP-343 and asked him the question she'd wanted to know ever since she was a child. If you're all powerful, why do you allow bad things to happen? SCP-343 sat in silence. Okay, so I don't think anyone won that, at least of all me. If you guys have any other kinds of story prompts in the vein of a future versus episode, let me know in the comments below. If you see something in the comments that you like, hit the like button on it, and I'll know that that's the thing you want to see next. If you suggest anything, any kind of competitions, they don't have to be deathmatches, but hey, if you wanted to suggest that, you could. And of course, if you want to support my content, you can join my Patreon at patreon.com. I look forward to seeing you join these fine folk. Thank you, and I'll see you on Tuesday.


In this video, we're going to talk about a penny stock with really good fundamentals, which I think could receive absolutely Explosive growth and if you could leave a like on this video, that'd be fantastic It helps out in the YouTube algorithm and also got to say thank you for all the support recently the channel's been growing massively and You know be nice if we could reach 2,000 subscribers by new year The channel's been growing loads. I've been getting what it is about 50 hundred subscribers a day Which is massive growth because before I was only getting two to three subscribers a day So thank you for everybody who continues to like the videos and what I'm going to do is I'm going to get right into the information And respect your time at the end of the day because you don't want to see me waffling off So let's get right into the useful information that you can assess and maybe consider this stock and I'll waste no further time. So this penny stock has massive growth potential. That's the first thing I want to say and Also, I've got to say I'm not a financial advisor. I am a business teacher in my daily life But I'm by no means an expert on the stock market. What is this stock? Okay, it's called drive Shaq. This is the penny stock that we're looking at today and Drive Shaq is a leading owner an operator of golf related leisure and entertainment businesses The portfolio consists of Drive Shaq, which is a nationwide network of innovative golf Entertainment venues and by the way, it's not just golf also includes other entertainment facilities within the facility And then you've got American golf one of the largest golf course companies in the world and also real estate assets as well So they've got some assets What is the mission of Drive Shaq as a company? So Drive Shaq is a publicly traded leisure and entertainment company focused on bringing people together through technology driven competitive socializing experiences That's what they are as a company. That's what they're trying to do the mission of the company And I do always like to look at the mission statement because it gives a direction as to what they really value and what they want to Build towards the mission statement states that to become the largest venue based Competitive socializing and entertainment platform in the country So this is in the states and it looking at becoming an incredibly competitive And successful company and it is as well and we'll go through some of the information about that Okay, and you'll notice we're getting right into the information. I'm not I want to just give you as much helpful information as in Little you know compact video here So I'm only going through some of the main accepts of research that we found on this company here So first of all, we've got here. So this is interesting. These are some of the two facilities that they have Okay, and what do they have so they've got their drive Shaq venues with large formats And this is a picture of one of them And this was launched in 2018 the large formats consist of outdoor entertainment Offering soup style bears multiple bars and a restaurant Socializing is the driving force from competitive tech powered golf games to elevated F&B offerings So they've got a range of entertainment in there. Then they've got their put three venues So they've got the large scale venues that they're going to build it, you know, which are much larger Then they've got the sort of more mini venues, which is put three Venues here. They have operate for venues by the way today of the large formats And the plan to open seven mini venues in 2021 our small format as indoor entertainment Anchored by multiple bars and lounges features high tech mini golf elevated food offerings And specialty cocktails created by mixologists. So it's basically a mini version of that one and what you've got to think there the Expenditure the running costs are going to be less the investment to actually build these is going to be less They're going to be able to build them much faster So if these are incredibly successful and they are up running seven We're going to see massive revenue growth because they've got expansion plans in place They're looking to build a lot more of these venues and if they build them in prime locations with the potential revenue And as and as after the pandemic as as things get back to a bit more normality Then they're going to see an explosion in customers Come into the venues and actually using the service. Okay So what is it and I found this very interesting there was a bit of market research here And and this really sort of interested me out of all the information I found this really did interest me and One of the interesting things here is is these two figures 78 percent of millennials would rather spend money on an experience Versus buying something now That is important really because what they're offering is an experiential sort of platform here What people go to drive shack for is for the experience And and you know Millennials are saying that they would prefer spending money on that and probably indeed older people as well potentially You know there So that's important there because there's a lot of revenue potential growth And if you think about it after the after the pandemic and things get back to normal Well, people are going to want to have those experiences because they would have had That time where they'd maybe feel a bit less inclined to go to these venues But now, you know, they're going to get more customers in As the pandemic goes down Essentially So at that experience point and then another point here the 70 percent of consumers Prefer to dine at entertainment formats versus typical casual restaurants for group occasions so again The reason why this you've got massive revenue growth is the is the actual Keyword here of groups, you know drive shack It attracts a group of people together And that's going to drive revenue growth and and from that there's a lot of word of mouth that's built up So even from the marketing. Yes, they can spend money on the marketing But because these are activities that are better done in groups, it's just going to naturally Kind of expand through word of mouth as well So this penny this is a penny stock and it's already seen really good promise here. It may not be a penny stock For For the long term shall we say So this is these two points here. I thought it's quite interesting And what do they do? So they combine three experiences in one roof. So they've got your entertainment Playing socializing a sophisticated fun setting. You've got your sports and you've got your food and beverage Okay, so they've got a range of experiences that are involved in the venue And and they're putting that together to ultimately drive a competitive advantage over its competition Which is interesting So it's a rising demand for social active and outdoor entertainment venues. Okay So so there's a lot of potential here. So look at those. Look at the potential revenue Yeah, so the entertainment is a 700 billion dollar industry sports 100 billion dollar and restaurants 800 billion dollar So it's saying it's a 1.6 dollar 1.6 trillion dollar industry within these three kind of Socializing industries hospital. You can say hospitality and entertainment industries Okay, and what drive shacks done here and again, we have to be Obviously skeptic Not a little bit skeptical because this is their investor presentation. They're trying to persuade people to invest So we've got we got a I have got a concern late run that I'll mention So stay to the end of the video for that. But I do think overall this is a good stop So concepts fail to adjust the consumer preferences. So you've got drive shack here Okay, and they're saying that they fulfill all these needs and so it's all weather because yeah, it's indoors You know, it's available indoors. They've got physical activities. It's social. It's competitive. It's out It can be outdoors or you know in the open air as well And it's defined space Then so they've got enough space for guests And what it's saying is that drive shack meets all these different Needs, whereas your cocktail bars obviously in sports bars only cover You may be social aspects And available in all weather because they're indoors constant venues, you know theme parks outdoor open air and social movie theaters So again, what we're saying and movie theaters obviously not being necessarily very social There but but there are obviously is a need for for these here But what there is a massive need for these here and they obviously pick companies in these industries are still going to be okay But what drive shack is doing is offering this unique selling proposition where it combines a range of areas together To really provide something powerful Um, and I thought it'd be interesting to look at the financial performance here, but just to note two main points Uh, we're seeing essentially some growth. So in 2019 it outperformed Expectations by 14 percent. So they had the expectations in terms of what the revenue was going to be and then they beat that by 14 percent And for 2020 Revenue for q3 average 70 percent of q1 levels and walk in revenue and q3 average 88 percent of q1 levels. So so that just goes to show that, you know The the growing there as well and you can you can see some of the figures here So what we're saying here is that there's potential growth and particularly You know as as because this was when I think America was in I think I think the states was in a lockdown at one point Around that time, which is why there was a drop off obviously and then increased But you can notice when the actual pandemic starts to um You know get get um Less severe and you know Things get back to a bit more normality Then you can see that there's going to be more walk-ins There's going to be more bookings for these group events and it's going to increase the share price There's going to be a lot of revenue growth that there is to come there And I thought Do you know what? Let's have a look and not many investor channels do this But I think it's important to do and this may you may think oh well what? You know, but I come at it from a different angle. I don't just look at the finances I try to look at all aspects of the business And I think it's really important to see what other people are saying about the businesses that they're using um So I had a look and I just thought let's have a look at some reviews So is a really interesting review here is is I can't wait to go back And this is in December 2019 Can't wait to go back great fun and wonderful food. The concept is great Location easy to get to so it's a you know Is it a prime location? And they like the concept because it's got all this range of entertainment combined with eating Combined with like his competitive gaming, you know So he's combining loads of different areas together to provide this full entertainment package And the notable thing here is 90 out 96 percent out 96 Reviews, you know, it's got nearly four We've got four and a half stars nearly five stars So people are happy with the service that they're getting there and this is was just the one from Orlando but you know Ultimately, it seems like a business model that customers are enjoying and You know, I think that's a good thing and and and it's a good sign that the company's got a good reputation Okay So what are the benefits of this company here? So first of all, it's got differentiation What I mean by differentiation is that it's it's it's different on the marketplace. It's not You know, there's got some different elements It's trying to combine a range of entertainment factors together and it makes it stand out on the market Is to think that okay I want to go out this weekend. What venues have we got? Oh, we could go to a restaurant We could go to a cinema Or we could go to this place where you can play some games go to a restaurant and You know Get involved in a range of activities You know, it sounds more promising. It sounds more persuadable to go to this venue if there's one near you So it's certainly differentiated and from that, like I said, because it's got a competitive advantage over its competition Because of the value it's trying to offer to customers not terms not in terms of price But the experience that it's offering to customers It's got growing revenues and it's it's it's uh, well it beat the expectations in 2019 in terms of revenue If it doesn't beat the expectations in 2020 revenue, it's understandable And do you know what? It's a buying opportunity because that will probably have an impact on the stock price And there but but You know, that's probably already factored in at this point. Uh, so it's already really low And then it's also got a good reputation as well as we say from the reviews Again, and it can grow through positive word of mouth The and and the company can grow what it's one of these companies that can grow up Partly organically. Yes, we'll have the marketing spend but Partly it can grow just on its own with what it is and and and the group aspect to it My earliest concern Is the scalability of the business Um, I'm really worried about the scalability of it Because it does need a lot of um investment involved in actually building more sites It's not like it's a digital product where you can kind of sell it Um as many times as you want and it can scale Uh, very very fast. They've got to actually build these new well They've got to acquire these new venues and you've got to put all their their, um Their entertainment stuff in the venues and that's that's a lot of work. So the scalability, you know I'm what I'm concerned about that. Are they going to hit their expansion plan targets of these number of venues? So that's that's it. I'm not saying that the stock's bad. It's absolutely We're looking into I think this stock could do really well just saying that the scalability is one of those concerned with With what it is That's my only concern However, I do think that this stock is got a lot of potential And it's been running down slightly in the past five days Which I think is present starting to present a buying opportunity It was at three dollars 20 on the 21st and on the 24th It's gone down to two dollars 70 after hours And I think, um, you know, this stock is it could easily get get itself up to four dollars Onwards very easily four dollars even towards five to six dollars and maybe in 2022 potentially over 10 dollars So so this is a explosive growth company. I think that this could have Exponential growth as it builds more venues as it gets more customers as it becomes more well known as a stock as well as a penny stock um, so If this goes down to to about two dollars 20 two dollars 30 I'd start to load up and even at this price I'd put in a small position 270 um, I'll put in a small position Um, but certainly if it gets round to the two dollars 20 two dollars 30 two dollars 40 I would load up and I would hold this stock and see For a few months to see how it how it does after the you know the the pandemic. Okay So that's that particular stock and I think like I said massive potential So thanks for watching this video. Don't forget to subscribe to the channel if you're new like the video if you found This somewhat informative and I'll speak to everybody in the next broadcast