All right, so this is what I call the holy crap, look at the data scape, right? You've all seen them, it's the first sentence of every abstract of every microbiome paper ever. Continuing rapid fall in the cost of computer components, blah, blah, blah, blah, DNA sequencing is now a fast procedure available, yada, yada, yada. So pretty standard thing. What I really like about this quotation in particular is that it is from 1979. Roger Shadden, developer of possibly the first bioinformatics package, which you can still download from the web. So that's pretty cool. All right, so welcome to my talk. I'm going to be talking mostly about microbiomes and mostly, mostly about marker genes. And one, so there are two lines that I wish to correct. In the program it says I'm a candidate research chair, but because of incompetence I'm no longer a candidate research chair. And the other is that Morgan is actually going to be giving the read quality and the 16S tutorial this morning. And the reason for that is that his software, Microbiome Helper, is actually very effective for both 16S and for metagenome analysis. And it allows us to give kind of a more consistent picture and it wraps things like time anyway. So it's not like you're going to be missing out on the base standards. Okay, so here's what we're going to talk about. Defining stuff, big questions. I would assess the microbiome using 16S and then the cranky part of the topic. All the limitations of 16S and how you can fall into certain traps that have been fallen to many times before. Okay, so let's define the microbiome. One thing that's annoying is that there is no sort of canonical citation for the microbiome. There's the classic Joshua Lederberg. This is kind of summarized. The collective genome of our indigenous microbes, microflora, the idea being that a comprehensive genetic deal of homo sapiens needs to include the genes in our microbiome. Okay. One thing that's interesting is that there are two different definitions depending on how you parse microbiome. So there's microbiome, which is like the genome and the various other ohms of the microbiota. There's also microbiome, which is actually the various tiny things that live inside us. I tend to follow this definition. Basically, the idea of your microbiome is all the bacteria and various other things that live inside you. Either is acceptable, just be clear about what you're talking about. Okay, so the microbiome, people often, when they say microbiome, they mean the human microbiome. But it's very important to remember that there are other, like, pretty much every habitat on earth also has an associated microbiome. This includes soil, this includes seawater, this includes non-human animals. So this is basically people have done studies of the gut and rumen microbiota of herbivores, carnivores, and omnivores to do comparisons, right? Different diets, different functions expected and found. And so there are a ton of microbiome studies done in pretty much every habitat on earth. Forrest Rower, who some of you might know, has claimed to have sequenced every single virome in every single habitat on the planet, which is a pretty bold claim. Okay, so the microwaves. So often, again, people, when they're talking about the microbiome, they're usually thinking about bacteria. But there's actually a lot more to it than that. And actually, we had a question already about bacteriophage, right? Viruses that infect bacteria. So it's important to remember that even if your particular study is focused on bacteria, and there's nothing wrong with that, keep in mind that there are many, many, many other constituents of the microbiome, right? So that other cool domain of life, the archaea, does exist in the microbiome. There's some important methanogens that live in our guts that are often missed by the standard 16S primers. Okay, so that's important to keep in mind. And then various other things, small things to consider. Viruses of us, viruses of bacteria. Microbial eukaryotes, very important. Some very important pathogens like giardia. And then some people also include, you know, overly complex organisms like worms and other types of worms, right? So it's important to keep in mind that there's this big ecosystem in which bacteria are extremely important, but they're not the only piece of the puzzle. Now, trying to characterize this all at once is super difficult. But again, just keep it in mind that everything's there. Jock already had a slide about this. And of course, we disagree on the number of genes of the human genome. Actually, I don't care, right? Somewhere between 20,000 and 150,000. So I said 25,000. But the key message here is that we have 20-something thousand genes. And if you take a look inside a standard human, typical human gut, you might find two to three million different genes. And quite a few species as well, whatever species means. The point is there's a lot of biodiversity. And so one of the important things, and that's presumably why you're all here, is to learn about culture-independent methods. Because we all have a giant pile of guts living in our microbes. I set that backwards. We have a whole bunch of microbes living in our guts. This is being recorded by the way. But there are obviously some limitations there. And we need to be able to consider approaches that look at things in the big picture. And so there's this great plate count anomaly. How many of you have heard of this before? Okay, excellent. Quite a few of you. So the idea here is that if you take all of the microorganisms from a particular habitat and try to culture them, you might be lucky if you can culture one percent of them. This statement is patently false for many habitats. And it's becoming more and more false as people come up with different culture techniques, different ways of actually getting these things to grow. Nonetheless, even if everything was cultureable, it would still not be practical to culture every single last bug that you find in the human microbiome. Some people, like Mike Saretzlav, are extremely good at culturing a ton of stuff. You can never be sure that you're getting everything that matters. And the other thing is that there are many ways to do this, right? So in addition to trying to grow things, there are many different ways, different types of data that you can generate, many different types of analysis. And that's what this workshop is about. So one of the simplest approaches to characterizing the microbiome is market gene analysis. The idea here is that you have all of bacterial biodiversity and you want to grab on to one thing that everyone has so that you can target that in a molecular analysis and do comparative studies to try and get at who's living there. And so the most commonly targeted gene is... 16S. 16S. ...prison zone learning. Yes, exactly. For all of its limitations. And so it's universally present. It's often, but not always, present in single coffee. And it has nice patterns of conservation and variation that we'll get out to later on. And the key is that we sequence these genes and then we use fancy bioinformatics techniques to try and translate the DNA sequences into proxies for the actual microbes that live in that habitat. And so this is from 1985. This is one of the first environmental DNA surveys. And these authors looked at fairly low diversity, super hot habitat. I believe it was in Yellowstone, if I recall correctly. Octopus Spray. And they actually looked at not the gene, but the R for the ribosomal RNA sequence itself, which is what you see the use in there. And by doing this, they were able to find sequences that match pretty well to previously known things. And also magic new things, which should come as no surprise, right? And so this was the first illustration that this could be quite powerful and effective. So it's all been done before. And then beyond that, and again, to get hooked up on semantics for a moment, the term metagenomics is often applied both to marker gene studies and to the environmental shotgun analysis that you'll be learning about this afternoon. Again, as long as you're clear about what you're talking about, okay, but you should definitely not refer to marker gene surveys as metagenomes around me. Okay? It's okay. So the key here is that the metagenome term was coined in 1998. And the idea is that you want to do some sort of functional analysis of the genomes that can be found in a particular habitat. And so what's interesting is that most people treat metagenomics as a synonym for environmental shotgun sequencing, but that's not where it came from. It was actually based on cloning and expression, but it's the same idea, right? Let's pull out a bunch of DNA, check it into some vectors, express stuff, and see what happens. So the use of the term is evolved over time, and now it's basically taken by most people to mean sequencing of random fragments of DNA from the environment. You're still getting functional ideas by looking at everything you can find, but you're not actually doing the expression and characterization. That step can come next. And yeah, here we go. By its strictest definition, it does not encompass marker gene surveys because 16S is not vaginal, right? So doing this environmentally is not metagenomics. And you will see some of these techniques as well. You can go from boring old DNA to looking at what's actually expressed in a habitat, and you can say, okay, what's the metatranscriptome, right? Which genes are actually switched on in a particular habitat by a particular set of organisms under a particular set of conditions? Metaprodomes, metapetabolomes, culture-omes, okay, this is like some of this large-scale sequencing, or not sequencing, culturing, and then you have more refined techniques like stable isotope probing to try and follow the track of different metabolites as they go on a whirlwind tour of various bacteria. Now, what would influence your choice of method is several things. Obviously, one of the most important ones is cost, and this is one, certainly not the only, one of the reasons why 16S analysis remains probably the most popular approach, I think. Reliability, okay, so that's an interesting one because different approaches will give you data with different levels of confidence, right? Think about sequencing error, think about false positives. Those profiles change if you go from one data type to another. Reference databases is another example, right? And then interpretability, which this is the part where we talk for the next three days and hopefully you don't get too depressed to teach you how to interpret some of these things. So big questions. What are some of the things we want to know about the microbiome that we might try to get at using culture-independent techniques? Here's one, the classic who is there. So you take a sample from somewhere, poop, the table, you know, the air, and then you do some sort of culture-free profiling. And as I said before, you can use the sequences you get as proxies for biodeversity. So here's an example. This is the famous Costello study from 2009 where they used 16S to profile about 32 different body sites, right? Including the gut microbiome, right, for which poop is the usual proxy. Lots of different skin sites, lots of various sites on and in your face, and so on. And you can see with little pie charts there that the colors represent different, in this case taxonomic classes or phyla. And you look at the skin sites and they tend to be dominated by actinobacteria. This was not hugely surprising. But then the gut samples right here, whose samples are dominated by two families, classes in particular. Bacteroidia is the purple one and Clostridia is the yellow one. So this is fairly typical. Now the ratio of those two phyla can vary, sorry, classes, can vary a great deal. So that's an interesting thing to look out for. So that's taxonomy. About functions. Well, if you do environmental shotgun, and this is fairly recent results from our lab where we've taken a cohort of elderly subjects in a nursing home and got this sort of microbiome profiling, this is metagenomes, right? This is shotgun sequencing. We've taken those sequences and we've mapped them against a highly curated database of antimicrobial resistance genes. By doing this, we can take the database hits, map them to the ontologies, which is basically just the categories of different types of drug resistance, classes of drug resistance. We can summarize this over our various subjects. And so the key takeaway here is we can look at specific types of function and we can look at the distribution of abundances across everybody in our cohort. And so here's an example, cephalosporin. You can see that this is a log scale. Some people have relatively high concentrations of cephalosporin resistance genes. And some people have not. So this is something that's fairly variable within the population. Elfamycin resistance, which is basically never used, you can see everyone has it and it's a fairly tight distribution, so everyone seems to have it in the same proportion. This is an interesting story that we can talk about later on. It's not actually elfamycin resistance. This will be a good cautionary table. And then you can look at something like beta-lactam resistance, right? Panicillin, emphicillin and friends. And it's also fairly abundant, but there's a somewhat wider distribution. So some people have more of it than others do. Other question? What does the microbiome correlate with? We do surveys all day every day, but it's not very interesting or useful unless we can start building, identifying associations with different types of potential drivers in the environment. And so here's an example. This is actually from 2006, but it's still one of my favorite examples. It's not even DNA sequencing. But these authors, Vera Jackson, took about 95 samples throughout all of the Americans, and they characterized soil in various different ways. pH, phosphate, nitrate, salinity, quite a few different drivers. And the one they found was the most important, or seemed to be the most important, was pH. And so pH increases along this scale, and then this is the diversity of the sample. For those of you who care, it's Shannon diversity. And you can see that low pH, this is about four, not a lot of organisms like to live in these habitats, because it's a pretty difficult habitat to live in. And then you can see that from there, it increases to a peak at sort of almost a neutral pH, and then as we get to alkaline, we start to tail off. Presumably, if we kept going to like a pH of 10, we would probably have a similar crash diversity. Okay, so there's one. You can test against various potential variables and identify the ones that matter most. I had to throw this in. Some of you may remember the click bait kissing microbiome study a few years ago, where they approached people in an Amsterdam zoo and said, hey, would the two of you like to participate in a study that involves kissing? And so what they said, if you look at it, you have to look at the supplementary material. Basically, a couple of things, okay, number of kisses per week between this couple, versus the similarity or dissimilarity of their salivary microbiome. And so, the more frequently they kiss, the less dissimilarity of their salivary microbiome makes sense. And this is simply time since last kiss. All words. But the best part of this, this is not relevant at all, but the best part of it is that they asked each, the members of each couple to independently describe how often they kiss each other. You have to look at the supplementary material. Okay, another question is how do microbiome respond? Again, not super recent, but one of my favorite studies, and it's an example of a bizarre multi-dimensional projection that actually tells a really cool story. So the backstory to this is you infect mice with C-dip, as one does, and we'll get into this later. This is kind of a two-dimensional summary of biodiversity. So you got like a zillion different bacteria in there, but you can't really project, you can't present that, so you try and come up with a much more efficient six-sync summary. Long story short, sick mice are over here. If they have C-dip, their microbiome tends to look like this. And they have four treatment groups, and as I should mention, healthy kind of looks like this. So you can see that they are pretty well separated. C-dip infection, not really surprising, right? They have C-dip, they don't really have C-dip. And then what they did was really interesting. They took examples of these mice, and they subjected them to various treatments. So they were interested in particular in probiotics. And so what you can see is from a starting point here, from a starting point here, the progression over days as the mice recover from their C-dip infection. So what's happening here? Well, what is it? Orange. Orange is simply a fecal microbiota transfer from a healthy mouse. So pre-transfer, three days, four days, six days, 14 days. How about gray? Gray is a sort of a passage, one of the passage inputs, which means that they try to culture the bacteria overnight, negative for mice, negative for mice. And so you see a similar progression. Although actually, oh, I'm sorry, I'm lying. I brought this backwards. This is a bacteroides lactobacillus formulation, familiar with. Pre, here's six days, here's 14 days. That's it. That's interesting. This is the culturing overnight, the blue. Pre, six days, 14 days. And then this is really interesting. The last one makes B. This was taking six bacteria from that passage culture and giving just those six to the recipient mice. Six, a controlled, reasonably well characterized microbiome sample. Pre, six days, 14 days. Last thing I'll point out, you can also treat C. Diff with antibiotics, right? Such as rhinomycin. Those mice ended up here. So this is, it's like my favorite figure because it tells such a great story with such a usually abused and horrible visualization. Okay, so like I said, I'm mostly going to be talking about 16S. We're just going to take over the task of metagenomics in the afternoon, but we're going to start simple. So, basic pipeline. Extract DNA. Amplify using PCR, using some set of numbers. Those are not the inevitable errors in your sequence. Build clusters if you're feeling brave and then look at the diversity, similarity, associations with environmental data, as one does with 16S surveys. Okay, so I don't want to dwell on this too much, but this is a simple collection of DNA extraction. There are kits for this. Different types of environment have different inhibitors for DNA recovery. So this is just something you need to read up on and look at the potential inhibitors and then find the appropriate kit or the appropriate experimental protocol for this. This is great. So in science, we have these things called controls, which are useful. And this is my fan. The paper published a few years ago about the microbiome of a particular organ that was thought to not have bacteria in it. The paper was like, oh my God, look at this. We sequenced the microbiome of this. And we found a huge representation of taxonomic group X. And then you're like, okay, that's interesting. And some people pointed out that taxonomic group X was a common contaminant of the reagents that they used. So, careful, careful. And it's redacted to protect the... Here's an interesting one that sometimes doesn't get considered, size fractionation. One thing that's really interesting is that different constituents of the microbiome, that's not surprising that they have different sizes, right? All bacteria are not the same size. But that if you fractionate based on different sizes, you can get completely different, completely contrasting ecological results. So this is a great example. Looking at an oxygen minimum zone in the ocean. And basically saying, if we looked at the 0.2 to 1.6 micron fraction versus the 1.6 fraction, different genes were enriched, which strongly suggests very different ecological roles. Another example, how many of you have seen this figure before? This kind of went, yeah, this went viral last year. This is Laura Hugg, who's one of the organizers of the Canadian Society Microbiologist Meeting that starts tomorrow. And so this is the candidate phylaradiation. So here we have the archaea. Here we have the eukaryotes, which are pushed off to the side, colored uniforms, which I love. And here we have the bacteria. And so they took it to those, and they added the super tiny cells that they discovered, which they made the candidate phylaradiation, and put those into the tree. These are smaller than 0.2 microns. So people previously hadn't really found evidence of these because they weren't looking that small. These things are small. They have small genomes. What are they doing to be determined? What's your PCR strategy? So there's kind of the typical somewhat boring stuff, like choosing the right melting temperature for your primers. What sequence are you using influences the read length that you get, primer specificity, and then I'm going to talk about this in a moment, and then comparability with previous studies. And did I touch on this? And it's kind of, and I'll show you a couple of examples later on, that might be the most depressing slide to hear. So just warning. But did I point it directly to the Earth microbiome project and as an example of an attempt to standardize the different types of analysis. So that, you know, wonder of wonder, miracle of miracles. If you do a study in 2016, and I do a study in 2018, we have some hope of being able to compare them to a meta-analysis and learn something that isn't lies. So that's good. So this, and I'm sure some of you have seen this before, this is the 16S ribosomal RNA gene, which is typically about 1,500 dittides in length. And these are the different base positions. And what you're looking at here is the different degrees of phylogenetic conservation throughout. So there are some regions, and those are the peaks that are highly conserved across everybody, right? Especially, you know, bacteria at least. And then these are much less conserved, and those are the variable regions. And so the common practice is to have primers that target the conserved regions so that the primers will typically bind to most things and to target, have the primers converge on specific variable regions that should hopefully give you some sense of the diversity within your sample, although it does have its limitations. So this is an interesting paper where they looked at the different types of bacteria that could be amplified using different sets of primers, targeting different variable regions. And basically, long story short, if you use something in the d1 to d3 region, that's kind of 5' end of the 16s gene, you'll get an over-representation of pre-vitellic use of bacterium-steptic opisthrand with patellar bacteries for carbon monosaturant. This is 20. Use v4, v6. You get these ones, and you often fail to detect fusobacterium, which can be pretty important, actually. So that's alarming. And then v7, v9 favors a particular set of organisms as well, right? And failed to detect synomonas, v7, and microplasts. When you're studying, you're interested in missing mycoplasma might be a serious problem. One of the bits of good news is that with the advent of long-read sequencing, and eventually error rates less than 10%, I can hope, the full-length 16s ribosomal RNA genes have actually been shown to be quite effective at detecting a lot of things, and, as you'll see, differentiating a lot of things as well. Here's another paper. Evaluation of general 16s ribosome RNA gene PCR primers. So these people went and they validated a whole pile of primer pairs, 512 primer pairs, based on 175 primers, and about 10 that worked well enough to be worth recommending. So this is a pretty exhaustive study, and if you look at the literature, you'll see these standard primers being used over and over again. So at the very least, pick one, which is somehow justifiable in your study, and stay without. You'll read it and interrupt if you have any questions. So in terms of the source fractionation, what was your suggestion there? Wow. I mean, it depends totally on what you want to look at, right, the habitat. Some habitats like soil, I think, can have many more larger bacteria. Whereas other places, you know, if you look in the ocean, then you'll have these pico-phytoplank, prochlorococcus, my other favorite one, sinecococcus. And so you might want to target a couple of different strata of size diversities. So if you're specifically looking to find a new place, your target place? Well, I think, you know, if you choose your filters, then you can catch a lot of stuff, so you just have to make sure that you're going to catch the stuff you're most interested in. Yeah. So you're saying if you sequence two different regions to be? Yeah. So basically, you have two different loci within the 60-and-60 that you amplify. You're going to get a lot better taxonomic resolution that's going to cost more for us because you need to sequence every gene in the place. Sorry, was that your question? Excellent. So was it better apart from the question? Well, it depends on the accuracy. You're going to get kind of the union of, well, I'm not sure about accuracy because you'll still have the biases. You will miss fewer things. And one hope is that at least within a set of primer pairs, the biases are comparable. So the question is about using mock communities where you have water or whatever medium and you throw bacteria in at some defined proportions and then you try and say, okay, what happens if I do various types of sequencing? Different library, perhaps different sequencing approaches. Can I get the right bacteria in the right proportions? And often the results have been fairly terrifying. So if you think of, I'd expect 10% of each of these bacteria except for the 10 equal proportions. Often, I can read the paper. It might have been out of John Eisen's lab. Morgan, correct me if I'm wrong. I think there's a paper out of John Eisen's lab that built mock communities and then tried different library preps and sequencing techniques. It was DNA extraction. Other people have looked at different B regions and also doing shotgun sequencing, the metagenomics, and then trying to reconstruct 16S sequences from that. And the answers are predictably different. So, yeah, it's something to keep your eyes on. And sometimes, like I said, the best you can do is try to be intern with the system. Okay, one more and then I'm going to keep going. Just for DNA sequencing, how is it easy to rely on data? So, sorry, you mean in terms of sequencing error? No. I'm interested in one area, but we also want to know the same thing. Like the flanking community? Exactly. I'm just wondering if the good strategy Well, I'm certainly stick with standard primers, unless you have a very, very targeted question. Other people might have opinions on this and this will be a great topic of discussion later on, but at the very least, if you're using standard primers, they have known properties. Whereas with custom primers, unless you're really worried about missing particular organisms, you can kind of stick to a standard B4, B6, B8, B4, B5, something like that. Okay, so steps and analysis. Quality control. If anything is left after quality control, you can assess the sample diversity. And there's basically two ways to do this. I care about, sorry, I don't care about taxonomy, and I care about taxonomy. Looking at similarity among samples, basically comparing things and saying, how similar are they? Are they similar at all? If they are similar or not, can we associate that with, for example, metadata, metadata basically is taken to mean everything that isn't the sequences. DNA is the only thing that matters.  Who cares? So often people call it contextual data, environmental data. Again, just be clear on what you're talking about. People are never going to agree on this. Semantics. Next, we can also look at machine learning classification. So I'm not going to go into any detail, but it might be a teaser for what's coming on Thursday. A couple of neat examples. And then functional prediction. This segues into something that... Hey, Morgan, are you talking about progress? Okay, cool. So it segues into something that Morgan will be talking about according to him. So there's a couple of standard pipelines. You've seen Chime, Mother is another one. These have been developed by different labs to more or less accomplish and achieve the same goals, but they're done in very different ways. So interfaces are very different, and Chime has actually just been updated to Chime 2, which is a massive, massive change. API-based. I haven't had the time to look at it myself very much, but my people in my lab tell me that it's much, much better laid out, much easier to use, and much more extensive. So that is promising. And then Morgan is going to be walking you through his software, Microbiome Helper, which wraps a lot of the other approaches, both for marketing surveys and for metagenomes. So let's talk about quality. So when you get sequences, you will get quality scores for each base in those sequences. Typically expressed as what's called a Fred score. The Fred score is proportional to the probability that a given base call is wrong. Higher Fred scores are good and lower Fred scores are bad. And so you can have some sort of threshold, redefine what constitutes high quality, what constitutes little quality, and then the stuff you can do with that is obvious. You can say, if a read has overall poor quality, it goes. If part of a read is pretty good, the rest of the read is bad, slice, if you want to keep that. Often you'll get ambiguous bases where the sequencer is like, I don't know, I have no idea. Maybe you can accept a couple of those, but if a read ends up having a lot of them, you might want to toss it out. Various different quality filtering tools and one important part of this when you do amplification, PCR amplification of a marker gene, you can often get PCR chimeras where the front half is from this gene and the back half is from that gene and I used to say partial lateral gene transfer, but that's not true. Often it's an experimental, it's a methodological thing and so there are several different tools including UTIME, which identify these basically saying, okay, reference database. This half is similar to this thing, that half is similar to that other thing, sorry about your helpful. So that's another important aspect of quality control. So FastQC is a very widely used program to do this. The URL is up there and very difficult to read colors. The basic idea is that I think I deleted the, okay, let's see if I can describe it. So it's very colorful and imagine if you will a read that goes across the top, right? Position, position, position, position, position. Position, position, position. Here, on the y-axis you have the thread quality scores. So what FastQC does is over an entire run it summarizes the quality scores per position. So here's a million reads. Position one, the quality score is 36 plus or minus five. That's pretty good. But as you go towards the end of the read you'll see the quality tend to fall off and so you go into yellow, right? The quality score is kind of borderline and then red's like no. And so the good example is green all the way along, right? Maybe tailing off a little bit at the end. The bad example is a little bit of green followed by yellow followed by a really long stop light, right? So that's kind of a, I don't know how I feel about this run kind of situation. Having been accomplished, now we can actually start treating the sequences as valuable things that we wish to look at. One of the questions we can ask is how diverse are these samples? So alpha diversity simply refers to looking at one sample at a time and seeing how diverse it is, right? Which can be expressed in many different ways. So a few different ways that you can consider. One is individual sequences. So you amplify your V4 region and you treat each unique sequence you get as an independent thing. You say the frequency of AGCC AGCC AGCC AGCC is 20%. Right? The frequency of the above. But there are a couple of problems with this. One of the key ones is that you will inflict your diversity because of sequencing error. Right? You're going to say this sequence is present in 0.0002% abundance when it actually probably goes to the state. Here's the real sequence, one nucleotide away is the oopsie, right? So this is one of the reasons why people do clustering of sequences, and I'm going to show you the cute little animation in a moment, but the basic idea is that you look at the entire constellation of sequences and you cluster the ones that are similar to each other. And I'm going to get more details. Another obvious thing you can want to do, taxonomic names are often like a big warm fuzzy hug, and so sometimes you want to say, oh, these are bactoroids, right? These are pre-vitalamus and so on and so forth. But that approach is extremely perilous, because there's bactoroids and there's bactoroids, lumping them together can hide a lot of important stuff, so you have to be very, very careful about that. I've got some examples for you later on. So okay, I'd like you to read this, and then if you reach the end, I'm going to assume you consent to this. Presentation of OTUs, operational taxonomic units, should not be taken as an endorsement of the OTUs strategy, nor should it be assumed that OTUs have any biological meaning at all. Do not hold me responsible for the use of OTUs. Now let me tell you how I'm going to do it. Okay, so here's the approach. First step for operational taxonomic units, choose a percent identity threshold. So you say, alright, everything that is, every pair of sequences that is at least 97% identical is going to go into the same ball, right? And so first of all, each of the little blue dots there is a sequence. I should mention that 97% threshold is very common to use because it certainly solves most of the sequencing error problems, and 97% is a magic surrogate for species, although if you look at the Airpush stock around paper from the 80s and you look at the sort of percent identity and how that maps on the species, you can also simplify the dataset, right? Because you can often go from like 1.5 million sequences down to like 50,000, okay? So it can just make the problem more tractable, yeah. Sorry, was your comment there that should be higher? I think the outcome of the next few slides is that you should not use OTUs. There are much, much, much better ways to cluster that are available now. I mean, you don't want to treat everything as real because of the sequencing errors, but there are much, much more thoughtful approaches that you can use, and there's quite a few of them now, and many of them are very good. So this pair of sequences is 6% identical, so you don't want them in the same OTUs, okay? And so what you can do is say, alright, well, let's find, let's start with some sequences. There are just different ways to choose this, but whatever. You say, this is going to be the center of one of my OTUs, okay? And now we apply this 3%, and it's basically a radius of size 3%, everything gets something, okay?  Now, there are several problems with this. One of the main problems with OTUs is that OTUs are not real, right? There is no clean separation of sequences into 97% identity, right? You know, here's an overlap, right? These things overlap. This sequence could be assigned to this OTU or that one. So there are important decisions to be made along the way, which are often mathematical rather than biological. So that's a serious problem. How do we do it? Well, there's a few different ways. One is you can use a big database like, say, green genes, if you can find it, and cluster against that, right? So your seed sequences are actually from a reference database that's already taxonomically attributed.  The alternative approach is to use no reference database at all and say, within my own set of sequences, I start with these and a cluster. One of the recommended approaches is to actually combine the two. Start with the closed reference database approach, pull in what you can, and then whatever's left, whatever hasn't been clustered yet, is treated as new, right? And then you can cluster that in a different way. So this is a hybrid approach. This is open records. When you do this, you get an OTU table, which is basically across the top. Each of your samples, this is, again, our nursing home study. Is that really a pattern for this? Excuse me, an example. So each column is a sample. Each row is an OTU count. So our OTU identifiers here are hopefully also integers. So down this column, this is actually ID. This is not counts. But you can see that OTU number 13, there are 88 sequences in sample 1. There are 55 sequences in sample 2. 166, yada yada yada. Now, there are several problems with this. One is that each sample will tend to give you a different number of sequences. So if you compare based on the sequence counts, you're not comparing apples and oranges. You are comparing apples and oranges, right? Apples and oranges, by the way, fall into the same OTU. So the thing about it here is that if this is sampled much more frequently, if you get more sequences from this sample than from that sample, you have to do something about that. So there are a few things you can do. One is that you can convert to proportions. And actually, given the time, I'm not really going to go into the details. Again, we can talk about this later if you like. Proportions are bad. Rare refaction, you can identify the sample with the smallest number of sequences and sub-sample everything else down to that count. So you throw away some of your data. And then there are model-based methods that tend to be a lot more statistically justified. They use things like the variance of the counts to try and get better estimates over all that diversity. Each of these has the limitations. Each of them is used, and so just the best you can do, even if you use one of these, is to be aware of the limitations. So we can visualize diversity. And basically here we have subject identifiers along the X and then relative abundance of different groups along the Y.  So you can see here, for example, blue, I believe is bacteroids, and subject 26 has no bacteroids. And so you can start to look for things, orange is achromandia, which more later, and so on and so forth. Now, OTUs, I don't like. Taxonomy, also questionable. So here's another one of these bar graphs. And one of these colors might refer to the genus, achromandia, or the species, achromandia, if you set a fill up, whatever. And then you can say other groups as well, a pair of bacteroids, whatever. And then you get all the mystery, right? Because a lot of your sequences are going to map to no known taxonomic group. And that's okay, right? You just discovered things. It's exciting. So how do you assign taxonomy? I'm sorry, I'm very simple. So how do we assign taxonomy? A few different ways. One of the most obvious ways to do it is to use sequence similarity search, right? It's 16S genes, everything's homologous, right? So you just use something like BLAST and whatever best BLAST match you have between your sequence and something in the reference database is what you call your sequence, right? So the best match is 2,4,5,6 into ballast. So my sequence is 4,5,6 into ballast using some sort of threshold, right? You wouldn't say my 16S sequence is 50% identical with 4,5,6 into ballast. So it's simple, but it's also too simple. Because what do you do if you get matches that are roughly equal identity or e-value to very different things in the database? There's different things you can do. Phylogenetic placement is pretty cool. This is where instead of doing the sort of similarity-based approach you can build a phylogenetic tree of your reference database, right? You've got the factories up here, you've got the pre-vetelo, whatever, and you take your sequence and you map it somewhere into that phylogenetic tree. That mapping can serve as the basis for the taxonomic site. One thing that's nice about this is that it immediately gives you some idea of how precise you can annotate this thing. If it maps into genus streptococcus, you can say it was probably streptococcus, but if it joins the tree at some higher level, like class level, you can say, well, I don't know what the genus is, but it's probably class claustridia. Makes sense? Another way to do it is machine learning classification, which is fun. The ribosomal database project classifier does this and sort of like an anti-visionary at some time. Basically what the RDP classifier does is it takes your sequence and it breaks it down into a series of what are called k-mers. A k-mer, k is an integer, k is a number. A k-mer decomposition is simply counting the different sets of nucleotides of length k. So if k equals 2, then you'd be counting the incidences of a-a, a-c, a-g, a-t, c-a, right? So you get this vector, this list of counts for each of those two. You can go further. k equals 3 and so on. The decomposition of this. So that's good because it's fast. It lends itself really well to classifying a million sequences quickly. But think about it. By breaking things down into little counts of length a, you lose all the positional information. You don't know whether this thing was next to this thing or over there, whatever. But it actually works, which was kind of surprising to me. So I'm excited at some ridiculous number of times, like 3,000 or 4,000. So it's pretty effective as a quick and dirty approach. Here's an example of what it looks like. So we've got two sequences over here, GD6J, whatever. And it gives you a classification of each rank. So bacteria is the domain level. In this case, the sequence has been assigned to plankton-myses, which is phylum level. So you get that level of classification. What's nice about it is that it also uses a bootstrap-based approach to give you some sort of confidence score on the assignment. And so look at the second one there, right? Bacteria, domain, the confidence is basically 100%. Fertigutes is the phylum, 32%. Okay, so on there, plus 3 to 8, class 0.26. Often what you will do with this is that a minimum threshold. You might say anything less than 0.7 is counting. I'm not going to forget it, right? 19% support. No. Do not call that an aero-electronicus or anything else. Alpha diversity, how do you calculate it? Well, simple way to do it is to count your OTUs or counter-species or gender, whatever arguments you're using. And so that gives you what's called species richness. You can also use these information-based approaches such as the Shannon diversity and the Simpson index, which tell you both about richness and the evenness of your sample. So richness would say 10 and 10. So Shannon would take into account that this has 10 species, but it's 90%, 1%, 1%, 1%, 1%, right? And this one is 10%, 10%, 10%. So richness would not distinguish them. Shannon diversity or the Simpson index would. Phylogenetic diversity is fun. That's where you take your sample with all the sequences, build a phylogenetic tree from those sequences, and then sum the branch lengths. Think about what this means. If you have a sample that for whatever reason contains only streptococcus, you're going to build a tree from those sequences, and the sum of branch lengths is going to be very small. So that's not a varied ever set. If you have a sample that spans bacteria and archaea, the sum of tree branches is probably going to be pretty large. So that can be pretty important. Here's an example. I won't dwell on this too much. Basically, and this is cool because it looks at different types of data as well, so metagenomics, reference genomes. And these are just different body sites, and you can see long story short, some are more diverse than others, right? OK, well, we knew that. But the choice of diversity measure and the choice of OTU threshold, taxonomic mapping, whatever, it's going to be obviously very important for this. OK, part three. So beta diversity. Now you take two samples at a time, and you compare them, right? So here's one of your sample groups. How similar are they? How dissimilar are they? And you can probably imagine in your head ways that you might want to compare them. Turns out there are dozens of ways you can do this. Once you've built this, you put each pair-wise comparison in a dissimilarity matrix, right? Example one, two, three, four, five. Difference, difference, difference, difference. And then you can summarize that matrix using various approaches, such as, I'm sure some of you are familiar with this, principal coordinate analysis, right? Ways of trying to cram that matrix into some sort of representation that may or may not be useful. So given a pair of samples described by, let's say, OTU abundance, calculate their dissimilarity. Beta diversity measures can be non-filer genetic or philer genetic. Do I care that the two samples are not philer genetic and very diverse? If I do, then I'll use a philer genetic measure. If I don't, then I won't use a philer genetic measure. Weighted or unweighted, do I care about the relative abundance of my different things, right? And there's approaches to that as well. And there are a lot of measures that's ridiculous. Ray Curtis is weighted non-filer genetic. Jacquard is unweighted non-filer genetic. weighted unifrack is weighted philer genetic. This is a paper we had a few years ago where my PhD student Donovan put in a total of 39 different beta diversity measures. This is not a philer genetic tree. This is a correlation tree. Each of these is a different measure. Their proximity is proportional to the similarity of their predictions, right?  And so Donovan identified several groups that tend to give you different classification types. And so one thing that might be useful if you're looking for patterns is to try one from each of the colored boxes, okay? Excuse me. What do you mean by normalized unifrack? Normalized. Oh, God, it's been a while. So it's weighted. So it considers the relative abundance. What's the normalized? Help me out here. I forget. I think it has to do with... I think it has to do with the distance to the root. So weighted considers branch lengths, but it doesn't really account for the fact that some leaves are really far from the root. So it tries to compensate for that, I think. So it's been a while since I've thought about that. And I don't think it's very widely used. Sorry? When we use weighted, everything is already different. Yeah. There's a way to normalize this one that I don't remember. Okay, what to do with the dissimilarity matrix? So this is that projection where you take your complex dissimilarity matrix, which is based on Latinxonomic stuff, and try to project it in a few minutes. So this is comparing the gut microbiota of three different populations. U.S., Amerindians, and Malalians, and you can see that on some axes of differentiation or diversity, you can separate this population from that population. But if you look along that axis, it's actually really not very differentiable at all. And then there are different methods you can use to say, okay, this is principal component or principal coordinate one. What does it mean? And you can look at different ways that the taxonomic groups feed into that dimension. And that can give you some clues as to what's really differentiated from this group. From a matrix, you can also do hierarchical bustering and say, yeah, these are pretty similar. These are not. There's different techniques to that, UPG and the different samples. All right. Different beta diversity measures can yield dramatically different results. And I'm not going to dwell on that. I know there was a figure, but it disappeared again. I think it's like... Associations of metadata. Okay, so you've got the diversity measures. Now what? Well, one thing you're probably interested in is how does the diversity of the microbiome vary with solidity or temperature or how recently a cow walked over that spot, right? And so you'll take your diversity information and you'll take your metadata or whatever habitat information you have. And you'll use some kind of technique. You might use one. So ANOVA, many people are probably familiar with. Permanent ANOVA is simply a non-parametric, you know, permutational-based one. You can look at similarity approaches. The Mantel test, which I'll show you in a moment if the figure's survived. So one of the simplest things you can do is take a particular taxonomic group, operational taxonomic unit, whatever, and regress it. So this is the abundance of a particular OTU. This is some metadata. In this case, it's the frailty of the subjects in our nursing home study. And you can fit a line. You can say, well, okay, there's a positive relationship. You can put a p-value. In this case, the p-value is like 0.001. And then you can look at the plot and say, but is that useful information? I always need to consider residuals and effect size as well. You can do ANOVA to compare different categories. So this is actually the abundance of a different function in people who are 60, 70, 80, or 90 years old. So you can do your ANOVA test and get a p-value. Let's see from Nicolas Sagata and Curtis Huttenhauer's group is really cool. This will tell you which taxonomic groups, back onto a tree, are overrepresented or underrepresented, relatively speaking, between two different symbols. So red are overrepresented in one type of group, green are overrepresented in the other type of group. And then there's other stuff. Mantel test is a different type of regression. Again, I'll just leave it there. Machine learning classification. You know what? I'm going to hand this off to Francois Levy with that. Basically, they're really powerful and really useful. But there's like a billion different pitfalls you need to consider. So this is one of my favorite studies. Well, one of my favorite things that I've done in my life. And it's using something called support vector machines or random forests to distinguish superdindable flat communities from subdindable flat communities. And using not just Otu's, but a more phylogenetic based approach. I'm just going to leave it there. But there's a classification accuracy. Functional prediction. This one's on Morgan. Take your 16s. Take a reference database of genomes and try and predict the functions in the metagenome from your 16s. Okay, problems. The complaining part of the presentation. Data quality is obviously a problem. Sequencing errors can be introduced. And, of course, it's proportional, both in percentage and in type of error, depending on your sequencing platform. Chimeras I've already talked about. Brar. Reproducibility. I've already talked about this. Two different variable regions can give quite different results. Ditto goes for sequencing platforms. There are papers showing all of this stuff. And then there's a bunch of different tools and your choice of workflow is going to influence what comes out in the end. So this is from a mouse frailty study we did a few years ago. And this is interesting. We had young middle-aged and old groups of mice. We didn't have very many of them. So we're like, okay, can we use sequences from reference databases to build up our own dataset? And so we did this kind of similarity ordination approach, principle coordinate analysis. And this reference set was actually microbes from six different strains of mice that were published in a previous study. And look at what we get. Our old are differentiated from our middle-aged mice, are differentiated from our young. We were hoping that these would be kind of reference middle-aged mice, but they all fall in there. So the two studies are not comparable, which is why this is not in print. This is cool. Okay, the figure disappeared again. The basic idea is that 16S is only one option. You can use others that are more highly variable. So the intergenic transcribe spacer sequence is an example. People have used that specific one to find very interesting patterns in the ocean within a single species that would not have been detectable at all using 16S. Okay, very careful. Okay, taxonomy and OTU suck. What else we got? OTU, so this is coming out of my call as a PhD student in my group. He has developed an algorithm and software called an algorithm which clusters sequences not based on sequence similarity, but based on their time series. So two things tend to go up and down all the time. They'll be assigned in the same time series cluster. So here's a time series. This is one particular lake in Wisconsin over the last 11 years. The data set was collected by the activity of the man. And this is if you do OTU clustering of the sequences. You get this pattern. It's seasonal, right? Right? But if you do time series clustering, if you look carefully, you can actually see that this OTU is supposed to one part of it, one time series cluster comes out predictably a week or two before the other one. So what have we lost by clustering into OTUs? A lot. So this is taxonomy. I think I'm pretty close to the end. Yeah, two more slides. And so what you're looking at here is a time series clustering of our dressing home subjects. And each color represents a different time series clustering of acromancia. So we're in this acromancia and we set a fill up, red. And notice how different subjects have different types of acromancia. And even within subjects, different types of acromancia have different temporal behaviors. Are these things ecologically the same? Should they be treated as the same thing in the analysis? No. They're all the same look to you though, right? This is potentially a problem. Last example, biological relevance. Here's a microbiome sample. 99.9999% is the log of active, right? And then 0.0001% is accidental active. So does this thing matter? We found it in the sample and if you sequence deeply enough you can probably find almost everything. But where do you draw the line in terms of biological significance? And so this is a very complicated question. So summary. There's many ways to do it. Marketing analysis is widely used and relatively inexpensive.  What can we do? Well, that's the rest of the presentation.  Children's inflatable poops. Chicken poop. Thanks to John. We've got squash bugs. We've got kimchi. Who likes kimchi? Statues. People are interested in the degradation of statues. Roller derby. Skin to skin contact and roller derby. Donkeys. But not the whole donkey. The certain parts of the donkey. So. And somebody asked about power calculations and so on. So I just found this online. I haven't looked at the web server yet because the internet was down. But I think this gets at what somebody was asking about. So. Compositionality. Yes. So you could list some of the methods that we don't believe but are they applicable to compositional data without transformation? So the question is about compositional data and this is a problem that comes up for instance when you convert your counts into proportions. Because like converting into proportions you lose freedom on your data. You can strain on some to one. And people have shown that certain types of analysis. One thing I didn't talk about at all is co-occurrence analysis. So if you run co-occurrence analysis on compositional data you are going to get a lot of spurious results. So. Again some of these problems can be remedied with other types of approach. And again there's like 15 different ways to assess co-occurrence.


This is a fast mirror exercise. What direction do you think I'm covering? I've hidden. Hey, what? Where's ha? Horsley. Neve Seth. That's a four. Hey! Hollywood! Anyways, today we're doing a video. This is called baseball versus cricket. Which sport is better? You don't want to do this. No, I'm telling you. I know that's not what this is. This is, that's, the title's just for clicks. I'm pretty sure. So like, in terms of like, I'm going to get, I'm going to make a lot of you really angry. Whatever, but this actually goes over the common misconceptions like what a cricket player has about baseball and vice versa. Great. So I think that's more of what the video is, because obviously you can't say one sport's better than another. It's all based off your preference. You can think, you can think currently, which is a great sport, is the best sport. No, there's not one sport that's better than another. It's just like saying this art is better than that art. Yeah, or somebody who's, yeah, I like, well, you like Pepsi. I like Coke, which one's better. Well, that's Pepsi. Yeah. But anyway, so I'm pretty sure that's what this is. Not really a, we're going to get a definitive answer.   So here we go. Versus crickets. For some reason, I get a lot of people making adverse comments on my videos on baseball and cricket. Most of them aren't just trolling, which is pretty sad. Agreed. But quite a lot of them seem to be misinformed, and I'm here to put that to bed once and for all. This video will destroy the myths that each set of fans has in regards to the unsports, and show you why it's a largely pointless argument to compare the two. Corp. Before I get started, you know what to do. Subscribe and hit the bell. And let's start by destroying myths held by cricket fans about baseball. Cricket fans will always argue that it's harder to hit a cricket ball, but this is not true. Correct. A show by ESPN Sports Science completely disgrouped this myth. For the contact surface area of a baseball bat, it's significantly smaller than a cricket bat, which means putting the ball in play requires more accuracy with a baseball bat. This was demonstrated where a pro-cricketer tried to hit with baseball bats with relatively poor results, and a pro baseball player tries to hit with a cricket bat with significantly better results. The form is going to be wrong, but you can hit it. The reaction time has to be quicker in baseball too. Correct. The distance between the wickets in cricket is roughly 20 meters. The distance between the pitch in rubber and football is 18 meters. Therefore, you have less time to think and react in baseball. This is not a myth. This is a scientifically proven fact. It is physically harder to hit a baseball with a baseball bat. A smaller bat, a smaller ball. Critics fans will argue that cricket ball is more suitable faster than baseball pitchers. Again, this is not true. Different. The average speed of a cricket ball at the top level is 88 miles an hour. There'll be 97 miles an hour. The average speed of a baseball pitch is 92 miles an hour. Upwards of a hundred a lot of times. The fastest of a cricket ball, Shoei Vakta, 100.2 miles an hour. 104 baseball. The fastest ever baseball pitch. Oh, 105. For all this Chapman, 105.1 miles an hour. Yankee. Baseball wins. Right there. Critics fans argue that cricket is played by more people, but why? This is an annoying statistic. As cricket is played by fewer countries, where the populations are massive. If you just could, you had to chime out. This is over-exaggerated popularity. And therefore superiority. That one doesn't... Unless you include all Indians. Baseball is played in more countries, where the populations are significantly less. Baseball fans use this statistic to over-exaggerate popularity, and therefore superiority. And even then, using populations of one number of countries is not an accurate measure of how many people actually play the sport in these countries. Right. To me, this does not dictate how popular a sport actually is. Absolutely. Cricket fans deem baseball inferior, because baseball players use a glove to catch the ball. Making fun of a sport because they use the equipment for safety reasons is incredibly shallow. Baseball players use a glove to catch the ball. This speed of a ball up is bad as being much faster than the ball up above. Not because the glove is capable of catching the ball. It's still crazy that cricketers can catch it. And any cricket fans who disagree with that, what's this then? What's that? What's that thing that they're working here on? Oh yeah, it's gloves. This argument is largely critical. I awful play safety, and I contend those who make fun of things that make a sport safe to play, especially if it's safety for kids.  On the other side of the coin, baseball fans will argue that the cricket ball is easy to hit, and therefore it's easier to score runs. But that's not necessarily true. Yes, we've scientifically proven that it is harder to physically hit a baseball, but it's certainly not easier to score runs because of it. Funny, the Indians holding it like a... Jen, cricket bats review that you can beat out. And baseball players will hold a cricket out like a baseball bat. In crickets, there's ten, and not only do you have to defend your weakest side. And then once you're out, you're gone. You have to defend yourself. Because in baseball, if you're hit by a pitch, you get to cry about it a little, and then put it in the basket of space. In crickets, if you're hit by a ball, that's when they hurt from it, it's perfectly illegal, and if it's trash your leg in front of the wicket, you're out.  Can you score runs efficiently if they're going for your face? Debatable. Baseball fans will argue that it's easy to put the ball in play. It... No way! No way! It's not enough to just make contact with the ball. It's putting the ball in play without being out. In baseball, you hit the ball between the foul lines, which forms this 90-degree area. You hit the ball outside the foul lines, you're not out unless you're caught in fouls. Exactly. No, I would say it's harder. But a cricket batsman has to be a cricket without questions around. Yes, they have to be adept at hitting the ball behind them, as well as putting the ball down at their sides. That aspect of it is insane. They can still be caught or run out, as opposed to being left off with just a foul ball. That's the first thing that blew our minds when we were learning about it. Yes, there are bigger gaps in the field because the fielding team has to cover more ground. But there's a lot more to think about than just hitting the ball with all your might. Exactly. In way more nuances they have to deal with. Exactly. Because once you're out, you don't get another chance at the batsman. Exactly. You are done. Much more pressure. The bats will argue the cricket ballers have no skill whatsoever. What? I don't know who said that. But if you argue that they have more... They don't know what they're talking about. In baseball, a pitcher stands in one place, throws the ball in the air directly to a batsman, who is standing in one place. But a baller in cricket can deliver the ball anywhere along the crease on either side of the wickets. They can throw the ball on the floor. Yeah, like in baseball, where he doesn't hit the ground. Or they can pass the ball from the floor. They can fastball it, spin it, puck it, roll it, either at the wickets and the catch around the wickets or at the player themselves. Whilst we've proven that they don't throw us fast, they use infinitely more tricks to try and get... Yeah, they don't agree. That's one of the only tricks in baseball. I've got the myths. I would say baseball players, pitchers probably have seven or eight things. And they've got 25. Yeah. Not exactly boring themselves with glory of history. Agreed, it's notoriously boring sometimes. Due to their formats, they can take anywhere up to five days to play it. Whilst that's down to... Look at baseball. It's just like watching a series of newy-few teams. Those are for days. Where some of the games are shorter than major league baseball games. It all depends on which format you're watching. Right. And, as for the boring part, that's a little subjective in you, rather than an up-match. That's an opinion of a lot of American football. It's why the ratings are down compared to other sports. This video has largely been a waste of time, because they're two different sports. Yep. It's kind of like comparing an apple to a lemon. Lemon fanboys will argue that lemons are better because they're more yellow. Yep. And apple fanboys will argue that apples are better because they begin with the latter eight. You could adopt any metric to support or disprove any argument between these two, but it's not a fair comparison. The only reason they get compared is because of the bat and the ball. In my lips. But they are two different sports. They're very different sports. And hundreds of both sets of fans say things like, cricket is an inferior copy of baseball. No. Or baseball is an American derivative of cricket. Neither one goes through. It's not true. Because they always were and always have been two completely separate sports. Yep. They were invented around about the same time. Correct. With no common heritage or origin. I thought that it hurt. I thought baseball came from cricket. I thought baseball originated in England. And I was wrong. There is no sport that is objectively better, as the objectives are completely different for each one. Yes, both sports are played with a bat and a ball, but that's when the similarity can end. The real question is, which sport do you prefer? Yeah. That is up to you. If you've enjoyed this video, please be sure to like, share, and subscribe. I'll leave your thoughts in the comments section below. Yeah, a great video. Yeah? Fantastic video. And it's exactly what I thought it was going to be. It wasn't, obviously, you can't, outside of, you know, curling, curling. That's the greatest sport of all time. I think we can all agree on that. My favorite Olympic sport. I could watch more curling. I'd love to watch curling. Any other Olympic sport. No, I won't go that far because there are other sports. Any other Olympic sport, I will watch curling above. I love it. Not me. I won't call it pucky broom. But I do love curling. Anyway, it's fun to watch. But yeah, that's exactly what I thought it was going to be, because we hear it all the time, obviously. Obviously, Americans know nothing about cricket. Absolutely zero. Zero. Indians don't really know much about baseball either. Right. Which is why you hear stuff, baseball is cricket without brains, which means you know nothing about baseball. Yeah. Because there's a lot of technicalities that go into it. There's so much going on in baseball, but if you don't know the game, you won't notice it's going on. Just like if you don't know anything about cricket. Exactly. You won't know what's going on, and you'll just be like, what the hell, you could just hit, there's no rules here. Yeah, like literally, if a person who knows baseball can be watching a game, and you can have the same situation in the second inning, fifth inning, and seventh inning, where you have two runners on, on second and third, with one out, and a left-handed pitcher throwing to a right-handed batter. And you know what the right-handed batter should be doing in that situation with two outs. They should be hitting the ball to the right side of the field to get a base hit to score those two runners in. That's because you know the game. There's a gazillion things about cricket I would never even think of because I don't know the nuances. But the thing I was going to say, he said right at the outset that I thought would piss a lot of people off. And it's true, there is a fact that studies were done not just on that one, but sports illustrated for years. Like every decade for the past 40 years has done what's the hardest thing to do in any sport. All sports around the world, Olympic sports, everything. And they always come back to taking a round ball and a round bat and hitting it squarely. It doesn't make the sport any better. It just says that that aspect of it is extraordinarily difficult. Really small ball. Yeah. And the bat is not that thick. No, it's a round surface. Especially compared to a cricket bat.   And you fail seven out of 10 times at the plate. And you're considered, whoa, he's on fire. Yeah. Because it's so hard.  Obviously, yeah. But he said it right at the outset. It's basically if you grew up with it, you're going to think that one's better. Yeah. And you can have an objective opinion that you like that sport more, which is fine. My favorite sport to watch is NFL American football. Then it would be soccer. Then it, well, baseball and soccer are basically pretty even for me. But in terms of, I'll watch, until it gets to the playoffs, I'll watch more soccer than I will regular season. But there's just too many games. Yeah. And that's, there's a lot of problems Major League Baseball has. And that's why they're trying to figure out and get their ratings at like, they did a great thing with, even though they didn't change the game at all, but the, the, the coster thing. Yeah. The field of dreams. Field of dreams. That was great. Game, because they're trying, because we have a problem here of nobody, nobody's watching baseball. Comparatively. Comparison to the other NBA, NFL. Those are the two big ones. But that's actually not a problem with the game. That's a problem with people. That's true. It really is. But they're, I'm, my bet is they're going to do something similar in the next few years to what cricket did with the T20 games. Well, they have been doing things where they've made some changes in the rules. You now have, they don't talk about it, but pitchers have a timeframe in which they have to deliver the ball now. Oh, really? They have a specific, they did this about two, three years ago where the umpire is keeping track of how long you take in between pitches. And your team, you can get called for a balk and other, other things can happen, a delay of game problems can happen for you if you're taking too long in between pitches to try and speed the game up without changing the, the nature of the game itself. And like my, my favorite sport to watch is, I love baseball, but obviously I'm very particular to Yankee's baseball. I don't, I won't watch any other game unless it's the playoffs in the World Series. Then I don't, I don't care who's playing. I'll watch playoffs in World Series. Yeah. Um, and then after that, it's combat sports, MMA, UFC, kickboxing, boxing is my least favorite of combat sports and then football. Yeah. And I love basketball too, but I don't, I don't watch it hardly at all anymore. Yeah, I don't think that's cool. No, I don't watch it hardly at all anymore. But yeah, no. My favorite sport to play is soccer. My favorite sport to play is baseball without football. Baseball is my favorite, will always be my favorite sport. Yeah. Yeah. But yeah, great video. I thought the video, uh, it was very informative about each side and their argument. I don't know. I've never heard, what was the thing you said about baseball players think like there's no skill in, Like it requires no skill to play cricket. I've never heard that.    And you have no idea what we're talking about. Exactly. Anyways, but yeah, great video. Let us know where the cricket videos we can react to down below.


What's going on guys today? I bring you this new Destiny 2 PC beta trailer featuring a few new things But mostly of what we already know with the beta but in stunning beautiful 4k now the n video 4k video trailer We'll be linked within the video description. So check that out if you want to definitely recommend you guys because it just looks Like a whole new game now We'll bring you guys a more in-depth detailed video of what we see from this trailer later today Which includes a new sword we see being swung at the end of the trailer and a few other bits and bobs Now does this mean they get a sword to using this beta? Those jammy old PC players But yes, I am gonna try this game out on PC if you're gonna try it out Let me know down below in that comments section in my opinion This is the most important part in creating the full game because changes have been made from the console beta to this Beta the things that the community complained about such as super regions abilities Etc etc have been changed and they're dropping them and introducing them into this PC beta So them changes we will see in place So I'm interested to see how it plays out and then to see what the community thinks of it And then the changes bungee mate through the final game But the fact we get to play which is for many of us the first time in 4k. I believe will be amazing I'm super looking forward to seeing what it looks like and plays like on PC I seriously am but yes guys I'm gonna leave you to watch the trailer and bring you a further video later tonight Discussing certain points of this trailer But guys, hope you enjoyed the video. Thanks always for stopping by. Do drop a like. I do appreciate the support and I'll see you on that next one Exclusively a blizzard battle net and get early beta access


The football is just what's on the surface value, but stuff goes much deeper than that. Something that we talk about a lot on here because it's important that we get these reminders that these football players, the coaches, everybody involved, they are human beings at the end of the day. At the beginning and end of the day. So while we, of course, root for our favorite teams and our favorite players and what not, and that's great and there's nothing wrong with that, we got to remember at the end of the day everybody participating in these games. It is just that it's a game, it is a business as well, but they are all human beings. So stuff like this, and it's crazy because we were just talking about, that's sad man. We were just talking about that the off season is here, the slowest part of the off season is here and this is where we always end up hearing just bad stories all the time and I hate that part. I hate that so much.  And you feel for Ray Lewis, you feel for his family, you feel for just everybody, his friends, former teammates of Ray Lewis, the third who had 28 years old man, 28 years old. So a baby, 28 years old man, they didn't say what happened yet, I don't even really, not that it doesn't matter, but a brother lost his brother, a father lost his son, a mother lost his son, a niece lost her uncle, nephew lost her uncle, a teammate lost their teammate, it's just, it sucks man, it really really does man, and you just, you feel for people man, you really do because there's no, that's something that you never get used to dealing with because we weren't meant to be used to dealing with death, but that's why it sucks so bad because it wasn't meant to be dealt with and that's why it's like, it's such a pain that just, it just never goes away and you really feel for them man, you feel for the entire family, obviously with Ray Lewis being who he is and obviously his impact with the Baltimore Ravens and just him being like that guy, even like this dude been retired for since what, 2012, he retired that season and a lot of people when they think about the Baltimore Ravens, that's the first person who still comes to mind till this day. So obviously his ties with the Baltimore Ravens, everything he done for and with the Baltimore Ravens, that legacy that he has is important, so people feel extra for him man, that's your son, like speaking as a father to a son too and I mean, as a father or a parent to any child, it's pain, it's definitely going to be pain, but yeah it's just tough man because with your child, you see so much of yourself in your child and with your child, one of the biggest goals that you have as a parent, well I will speak for myself, one of the biggest goals that I myself have as a parent is just trying to put our son, me and my wife's son in a position to do better than we are doing, to do better than we did when we were youngest, just to have him set up better and you do so much for your kids, you realize that when you really sit back and think about your life, you realize how so much of your life is centered around providing for your kid, providing for your family obviously as a whole, but providing for your kid, making sure your kid is straight, making sure your kid is taken care of, making sure your kid is entertained, making sure your kid got everything that they need and more and to be doing that and to have done that for so long for for 28 years and then his kid passed, your kid passed and then on top of that as a parent, that's something that you never expect to have to go through too, your kid passing before you, that's just got to be like a, that's got to be a different kind of pain man, so I really do feel for this whole family man, I really do, because that's tough man and it's like these past couple of off-seasons man, we remember last year Jaylen Ferguson, Tony Saragusa, literally I think they, it wasn't it on the same day, I think, if it wasn't on the same day then it was like maybe a day apart, but I think it was on the same day, I'm pretty sure it was, but then just to go back remember Trayvon Walker, remember him, that was during the off-season too and it's just, I mean it's tough and it obviously happens to more teams than the Ravens because players around the Leeds have so many players in the league and their fine families obviously reach thousands upon thousands of people and whatnot and it's unfortunate, but man it's tough man, it's really tough man, so just be good to people man, be good to people, I don't even know what to say man, just just be good to people man, be good to people, tell people you love them, try to squash any issues that you got with anybody, try to keep the peace, try to be peaceable, try to show love, try to be approachable, try to just be humble, try to just again show love, try to be good to people man, that's the only thing I could tell you, try to be good to people because people just be going through stuff man, people be going through stuff, you never know what somebody going through, you never know what issues somebody is dealing with, you just you never know man, you never know and you never know how you being good to somebody could have a positive impact on them, so I love y'all, I really appreciate y'all so much, thank y'all for being who y'all are, thank you for being the way that y'all are, I love y'all and we going man.


It's cool man! I think it's great the way it changes from it has the dynamics. You can go roll your bone down. It still has the tone of the clean. Congratulations Blackstar!


Ladies and gentlemen, I bless the name of the Lord for this prayer breakfast, this meeting that is dedicated to the prophetic birthing of a new nation and I must thank the organizers of the coalition, the organizers of the coalition of intercessors and the Christian Association of Nigeria and in particular our leader and father of the Nigeria Praise Movement, General Yaku Pugogawa for the kind invitation and for his continuing commitment and dedication to this nation from his youth when he was head of state, when he led the fight to keep Nigeria warm to his later years as an elder statesman where his well-seasoned words uttered in God who was done have cancelled our nation's leaders for decades. The theme of this prayer breakfast is the birth of a new nation and it's unimportant because as we all know it comes at a time of great travail and tribulation for the nation. The nation in the throes of a pandemic for well over a year leading to a severe economic downturn, loss of jobs, and livelihoods and as we climbed out of the recession we have been faced with an unprecedented scale of insecurity in different zones of the nation. But for us who are gathered here today, children of God, we see the invisible. We know that every time that the nation where the children of God live is challenged it is God's call to his people to take action. It's an opportunity for God to demonstrate that he is God of other nations, that he is indeed the creator of the heavens and the earth and that he knows the beginning from the end. But God also as we know wants us to know that he has ordained us to speak concerning the things that we want to see.  From Zerubah Belt and Nehemiah we see ordinary men and women building nations at the direction of God and by prophetic action. Let me make you ground my remarks this morning in a particular story in the scriptures and we'll find it in 2 Kings chapter 2 verse 19 all the way to 22, I'll just read 2 Kings 19 in 2 Kings chapter 2 verse 19 to 22, then the men of the city said to Elisha, please notice the situation of the city is unpleasant. Because my Lord sees that the water is bad and the ground barren and in 20 he says, and he said, bring me the new bowl and put salt in it. So they brought it to him and he went out to the source of the water and cast in the salt there and said, thus says the Lord, I've healed this water for me there shall be no more death or barrenness and in verse 22, so the water remains healed to this day according to the word of Elisha which he spoke. So scripture has consistently shown us that the children of God are central to the birth of a new nation, the rebirth of a nation. Almost everywhere you look in scripture we find this repeater and in this story it is so.  In this story the men of the city told the prophet of God, Elisha, see this city is a nice, unpleasant and prosperous place but we cannot enjoy this community. We cannot enjoy this nation because the water is bad, because life is unsafe and the ground is barren. It is a serious economic problem. They did not go to an expert on environmental disasters, yet they go to a politician, the one to God and God directed his prophet who asked for a fresh new bowl, put salt in it and went to the source of the water, the root of the problem and poured in the salt and God spoke. I have healed this water, from it there shall be no more death or barrenness and so it shall be for our nation in Jesus' mighty name. But let us just unpack some aspects of this story. Salt is crucial to the birth of a new nation. Matthew 5 verse 13 says that you and I are the salt of the earth. So the salt is actually the children of God. As God is concerned, the responsibility for birthing the nation belongs to those who are called by his name. Which is why he said and this I will repeat almost today, but I practically have a speaker. Second Chronicles 7 verse 14, if my people who are called by my name will humble themselves and pray and seek my face and turn from their wicked ways then I will hear from heaven and heal their land. The word of God does not agree with logic or with our thinking. God is saying here that it is not the nation that repents. It is his own people who are called upon to repent and turn from their evil ways. It is not the nation that is called upon to turn from his evil ways. That is not the logic of God. The logic is that his own people must turn from their evil ways. As we are the salt, so are we the light. And the light ends the darkness. It marks the end of the night of weeping and marks the glorious sunlight of the morning of joy. So the pouring of salt into the source of the water, the source of the crop, is the prophetic act that we perform today. We the salt are also poured today by prayer and prophecy into the source of our nation's prophets. And as we pray and prophesy, a new nation will be birthed. One where, like the city to which Elijah went, the land was healed. The people prosper and peace and joy prevail. So I speak these words also to our nation. God says to the Lord, I have healed this water. From it there shall be no more death or violence. And after the healing of the land, we are told, in verse 22, we are told that the water remains healed to this day, according to the word of Elijah, which is spoken. And we say the same words for our nation as well. That this nation is the birth of a new nation. But the nation has already been birthed. There is no question about that. The way that God reveals things is always, depending on who it is, in pictures, in forms and for some visions. From where it's from my bedroom, I can see the rock, as well as the huge rock. I can see it very clearly, just lying down like that, I can see it. Once I open the window, I can see it just right, far away or right in front of me, I can see it. But there are times when it is completely covered by clouds, completely, such that if I open my window, I cannot see it at all. I cannot see it at all. As a matter of fact, if I had come into that room on the day when I didn't know that the rock was there, I would have assumed that there was no rock there, because the rock is. That metaphor is the same for our nation. The nation, the new nation is birthed already. It is covered by a child, by the cloud road there. And God Almighty will be, will take all of the glory for the birthing of this nation. Thank you very much. God bless you.


Welcome back, in the last segment we talked about vectors. In this segment we are going to talk about sorting. So first how to sort a vector. So C++ provides a built-in facility to sort vectors and also arrays and for this you need to include the header file algorithm. So once you do that, suppose you have a vector v of 10 elements. Suppose somehow you put values into this vector. Then you have to sort, if you want to sort, you just do this. You write sort v dot begin, v dot end, that is it.  Think of these right now as mantras but I am going to tell you what they mean shortly. So this will sort v in non-decreasing order and begin and end are what are called iterators. So iterators are kind of pointers but they are abstract pointers and we are going to discuss them later. Sorting an array is quite similar but not entirely. So again we need the header file algorithm. So suppose we have an array of 100 elements and somehow we initialized it, put values into it and if I want to sort it, I just have to write sort a and a plus 100 or here whatever the length it should appear over here, that is it, then it is sorted. The sorting order can be decided, you do not have to always sort in non-decreasing order. And in fact you can sort anything on which the less than operator is defined. So for example you can sort strings. You can also create objects or create structs in which you write the less than operator. Once you write the less than operator, you are ready to sort it. So this means for example I might have a struct which stores marks of various students and I can now choose to sort those structs, say either by the physics marks or by the chemistry marks or whatever I want, maybe by the total marks. I just have to define the less than operator consistent with the order that I want. And you do not even have to define the less than operator. In the sorting command itself, you can specify the sorting order by giving a lambda expression. I am not going to talk about this in this lecture but it is discussed quite extensively in the book and it is not hard and I will definitely encourage you to read it because there will be some exercises related to it. So what have we discussed in this segment? So standard library contains algorithms to sort vectors and arrays. For this you need to include the header file algorithm and it can sort any type of objects provided less than operator is defined on those objects or you supply a comparison operation as a part of the call. This concludes this segment and in the next segment I am going to discuss the classes map and unordered map but let us take a quick break before that.


This is Tom O'Brien of TFNN. We got five days a week, we go seven hours a day, we go 24 hours a day on the internet at TFNN.com. Always remember, folks, whatever you think about, you bring about whatever you focus on, grows, hope everyone's having a great day, safe day. Let's make it a great night, too, folks. Disconcerning the truth. The problem with making assumptions is that we believe they are the truth. We make an assumption, we misunderstand, we take it personally, then we react by sending emotional poison without word. This creates a whole big drama for nothing. Have to read that card a few more times, even though I've been reading out of these maybe about 15 years. Mockin' wise, let's take a look at it out here. We have the Dow Industries up 195, Nasdaq up 22, S&P's up 11 and a half. Gold, gold contract, trading down a buck, 40 at 1965 an ounce. We had silver down 30, up 39 cents at $23.53 an ounce. Light sweet crude, down a buck, 80. $76, 47 cents a barrel, notes and bonds. Ten year note, down 21 ticks, trading 10807, 30 year up for full point, plus six ticks, at 114, 08 and King dollar. King dollar right now trading up 355 ticks, at 104, 408. The euros at 108, the end straightener of 151, the British pound is at 124 to one US dollar. Excuse me. Our phone number's<PHONE_NUMBER>. Give us a call, folks. I know it's going on in your world and the world of the S&Ps, let's take a look at them. What do you have? Well, bottom line, you have a market that wants higher price. You got an ABC structure up, confirmed ABC that is. And on the S&Ps, that's the Spira, that's 462. Now you can see what happened. You took a day on Monday, well, let's go like this. So on Friday, what you did is you took the B point out and you took it out with volume. And that gave us an ABC structure up to the 462 mark. Yesterday, the CPI comes in, comes in light, you get another sign of strength, you got a gap higher, bottom line, 449, we're 12 points away from an ABC structure up now. NDX 100, same type of setup inside of the NDX, also confirmed ABC structure on the way up. Now the NDX has always been stronger than the S&P. Right now we hit 370, 370, 387, 775 would hit thus far. The last high that was out here in the last six months ago is that 387, 80 or something, okay? No, 98, 397, 98. Now on the Qs, you're talking about a 402. So if we take a look at this, you're talking about a 402. Now this is what's cool, now watch this folks, okay? Cause this is what's gonna be really important, okay? So I have this on a weekly now, right? Today's Wednesday, okay?  And if we take it out with volume, let me see this.  So let me see this right now. So we're gonna need 382 million, you're at 150. Doesn't look like I could get it this week. So we'll see where this shakes out. We know we get an ABC structure up now to 402, but you can see this, there's a larger, there's a larger structure here, right there. That structure is pretty intense. Because if we even look at that structure, watch this. So this leg here, yeah, the last leg we had up in the Nasdaq only did a .382 retracement. So if it does take it out with volume, that's gonna be one large ABC structure up. That will blow away the highs. Pretty intense, man, there's no doubt. Now check this out. This is gonna be really cool. Now watch this. I never knew such a thing, but I wanna show you something here for a second. So here's the 10-year note, okay? So my take is that we're going higher price, lower yield. But guess what? What makes a market is that there's plenty of folks that think that interest rates have not peaked yet, okay? You can see yesterday a huge set of strength. Yesterday we did 2.5 million contracts today or 2.1. Now let me show you why we're at 2.1. You're watching Target TV. I'm gonna put this up here if you're not, if you're in the car, remember that this is archived. Well what this is, folks, is that all these trades that you see, at appetite to fade to treasury, these are all shot positions that we'll put on bottom line this morning, okay? Because, and they will block trades, and they're done, I'll go through this, but you want to see these sizes here, folks, this is thousands of contracts. At the, basically, they're giving you the numbers and what they were, a 10-year, five-year, two-year, they're doing the whole ball of wax. But check out what this is. Bond traders reload bets on higher yields after massive rally. Rate traders pulling back into the, piling back into the bets on higher US treasury yields after a huge rally on treasuries gave them a more attractive price. Wednesday's session kicked off in Asia with the flurry of large privately negotiated transactions known as block sales in the futures market, linked to the treasury bonds. Block sales and Asia session were followed by, for the short wages on the five and 10-year contracts in New York, trading as yields rose after a stack of mixed economic data at 8.30 a.m. The trades help explain Wednesday's rebound and yields following Tuesday's inflation and river rally. It's pretty cool, man, just thinking that that's how this is. Now, if I go back to this chart, okay, what this chart is saying is that you're up on volume, you're still coming back with lighter volume, higher prices are still coming at you. So another big surprise with another rally, you could have all those have to be closed again. Stay right there folks, we'll come right back.


A computer program integrates the data from individual sequencing reactions. It can spot where DNA fragments overlap and order them as they originally were on the chromosome. Many overlapping sequence reads are needed to generate the uninterrupted sequence of the original stretch of DNA. During the Human Genome Project, every base pair of DNA was sequenced an average of nine times. Some stretches of DNA were easy to read and needed to be sequenced less often, while other stretches were more difficult to read and had to be sequenced more often. During the Human Genome Project, scientists ran more than 50 million sequencing reactions. Some 2,000 scientists from more than two dozen labs around the world worked on the project.


So we're here with the Airbus. Hi, so who are you? Hi, I'm Liso Leonetti, the Living Library Product Manager. So I'm introducing this thing to you as a new one at Last Platform. What is the Living Library? So Living Library is a cloud-based subscription to access satellite imagery. So you can see that now we propose different subscriptions, a gigabyte to access satellite imagery. So the satellite imagery is done by Airbus? Yes, so we operate a lot of satellites, and in this subscription you can access player images, so 0.5 centimeter, and sport images, 1.5 meter resolution. Is the whole planet? Yes, everything. We acquire the planet every day globally. And you have the best resolution or somebody's better in the world? We have competitors, of course. But we are leading in the industry. Are you more precise than what's on Google Maps? On Google Maps you can find up to 30 centimeters resolution. And you are? 50 centimeter resolution. And we are planning on going to 30 centimeters with satellites, but we have also Zephyr, which will provide much more precise resolution. Alright, so who's the customer for this? We have many types of customers. We have maritime agriculture, so some thematic services, but we have also location-based services, analytics companies, and so on. And you have a video to show a little bit what it's about? Yes, sure. So are the customers worldwide or most in Europe? Worldwide. We have different types of use cases, so in one class you can find many services, so let's see. The first service is data, so where you can access satellite imagery. So it's just launched last week? Yes. So it's new? Yes, completely new, a unique collaborative platform. So you can also find change detection services, so detection algorithm on satellite imagery. Yeah. So do you also want to introduce your colleagues? So hi, so who are you? I'm Thierry Medjetrit, so I'm working on development of the digital services, and particularly for one class. So it's new, so what does that mean? You didn't have this stuff before? Yeah, we were used to sell imagery on, let's say, the regular way, meaning to distribute images in the ancient time on FTP or... Oh, really? Yeah, on very, let's say, not very connected world. And here we are giving access to the imagery. To anybody? Anybody. So you just have to register, be known by the company, and then you are able to download and stream images directly on your computer or even on your smartphone if you want. So the resolution is 0.5? Yeah. And what does that mean, 0.5? That means that's the smallest thing you can see? Yeah, exactly. What we mean, we call that GSD, ground sample distance. It means that this is the pixel size, if you want, of the tiniest object that you can see on the ground. So this is the today resolution we are providing. And if we go over here, if I try to click on this, just anybody can just buy this. What can you do with 50,000 tiles? What is it? How much can you do that? It's basically, as soon as it's like when you're streaming, say music on a service like Spotify, for instance, here you can stream some tiles. A tile is just a piece of image that's stitched together to build overall a great image. So you will consume that depending on the zoom level you will take or depending on the size of the zone you are observing. So to say that 50,000 tiles, it's difficult to say how much it represents. It depends on your way to use the tool. And the price? So the price? It's going to be good price. Yeah, basically, yes. All right, cool.