Let's click, let's click, let's see, the right mouse. Yeah, okay, great. Here if I'm going to speak them out, but then I also want to talk about revealing non-classicality inaccessible object. Okay, thank you, Philippe. Oh, bad move. So apparently there is, this is something. Very nice. Yeah, I can do it on the white board, on the black board actually. Hopefully we'll get back. Yeah, so it's revealing non-classicality of inaccessible objects. It's so inaccessible that we cannot even see it. Very weak measurement. It was up at some point, am I right? Yeah, I'm even, should the cable be fine? I can dance. The best projection is the sealant left touch can you switch off here? It's left touch, it's okay. I will come back, that is very weak. There we go, I don't touch anything, right? It's okay, so hopefully it will work this time. Sorry for the inconvenience. So yeah, I'm going to talk about the revelation of non-classicality in systems that you cannot access directly. And really this is say an account of the resolution of our frustration. Now resolving frustrations can help in having fun with quantum mechanics. So just as a plan for the discussion, technology is fading me today. So as the plan for this discussion, let's go through what I'm going to present. So the basic result will be basically a scheme that is inspired by quantum communication to probe quantumness of a system, so quantum features of a system that you cannot really put your hands on directly. And by proposing this scheme, we will go together through which are the implications of this indirect inference protocol for mesoscopic physics, in particular discussing some details, a scheme of quantum optomechanics, but we will also address a couple more examples in different experimental contexts. And finally just to get a little bit more exotic, a little bit more crazy, let's go to the say possible implications that what we are discussing on point one and two will have for very ambitious topics, very ambitious problems, such as for instance the investigation of the possible quantum nature of gravity or the inference of general system environment correlations in open system dynamics. It will be very unfair to say, give this presentation without acknowledging, say main contributors to this work, which are these three people. So Margarita Zupardo, she used to work in Singapore at Nanyang Technical University, but she's now in Reykjavik. Tanjom Krijnanda, who is a PhD student at Nanyang Technical University and Tomak Paterek, who is the leader of the Quantum Information Group in Nanyang, and a very good friend and all collaborator of mine. Just for the sake of credit, yeah, again, I have to say that there are other contributors to the results that I will go and show and they will appear at some point in these slides when you. So a bit of motivations, okay, so the historical context and why the frustration that I was mentioning at the beginning of this presentation. So in 2003, I was first year PhD student working on the possibility of manipulating and transmitting entanglement, transferring entanglement in quantum networks. And I was under the apparently and evidently actually very wrong impression that if you want to entangle two systems through a third party, a little bit towards what is illustrated here. You have these two guys that you want to entangle. They don't interact with each other. They are remote, they are separated, far apart. And you want to entangle them through this third party, this C guy. Well, I was under the impression that you have to entangle C with either I, A or B at some point in the protocol in a way to transfer this entanglement to B. So if C gets entangled to A, then it can transfer this entanglement to B and therefore establish a long or remote entangled network between A and B, very natural. Now I have to transfer, I have to correlate stuff to transfer this correlation to remote systems. And then I'm very unhappy myself on the archive found this paper. One morning, separable states can be used to distribute entanglement. And that really upset me a bit because it changed completely the perspective that I had on entanglement distribution. And it was, say, the abstract itself was enough to, say, turn upside down my convincement because what this gentleman showed is that no entanglement is necessary to distribute entanglement. That is, to distant particles can be entangled by sending a third particle that is never entangled with the other two. Okay, so distributing entanglement without no entanglement, with no entanglement, actually, you know, with separable states. The frustration went on for quite a long time. No, I liked the paper. I liked the paper so much that I wanted to understand it in depth. And I couldn't. I mean, the maths worked. I reproduced the results. It was great. It was good. But I couldn't understand why you could distribute entanglement without actually having an entangled particle and entangled messenger at no time in the protocol itself. And they did what mature human beings do when you have a problem. You just ignore it, right? So I just decided, okay, yeah, it works, but I don't get it, yeah. And I went on until 2011. So almost 10 years. When I had these two trips, one to Singapore and one to Canada, where these three gentlemen were at that time. No, Cavamodi and Tomek Patrek, they were at CQT in Singapore at that time. And Marco Piani was at the Institute for Quantum Computing in Waterloo at that time. Now they have moved. Well, Tomek didn't match, but anyway, it changed the university. And what in these two different research visits, these three guys did was welcoming me with a cup of coffee and this paper. So distributing, separable state can be used to distribute entanglement. So apparently the same frustration was present at different locations for quite some time. So we decided to work on that problem trying to understand where the crux was and what made the scheme by Qubit et al possible. And so it took a little bit of time, but in the end, say in 2012, we came up with at least something that was instrumental to resolve my frustration and the take home message of that and there here is the following. Entanglement game along the lines of what these guys in this paper by Qubit et al had in mind. So the possibility of distributing entanglement in a quantum communication protocol such as the one that they had in mind where C interacts with A and B to distribute entanglement is bound by the degree of pre-available Quantum Discord. So you must have something, a resource of a quantum nature like Discord not as demanding as entanglement, a little bit loser, so it's Discord, but that is key if you want a scheme such as that to work. So we have particle A, we have particle B and as I said, you want these two guys to be quantum correlated, to be entangled at some point. You have this messenger boy, see, that interacts first with A and this is what I will call the encoding operation then stops interacting with A, it's transferred to B where there is a decoding operation and your hope is that at the end of this decoding operation you get entanglement between A and B but C on its own remains separable, remains separated from A and B all together. Now, a bit of say natural expectations, no the natural expectations is that if you take a measure of information, no say for instance, mutual information, measure of general correlations between A and B, the mutual information between A and B at the end of the protocol would differ from what they shared at the beginning, right? I'm talking about gain, so I'm not excluding the possibility that at the beginning of my protocol, A and B shared already some correlation. I just want to, for this correlation to increase, so not necessarily establishing them say from scratch, from zero, but maybe just an increase, right? So the difference between initial and final correlations will be basically dependent on how much correlations I'll be able to transfer from A to B. This goes along the lines of my naive expectation back in 2023, right? So you have to establish some entanglement somewhere to transfer it, to be able to transfer it to the remote station. What you would expect is that say the final entanglement minus the initial entanglement, okay, so the gain that you have in this communication protocol will be dependent on the amount of entanglement that you're able to transfer. Again, along the lines of what at least myself was expecting back in 2023. The actual truth is that in such a scheme, and this is an actual theorem, no? For any tripartite system, and there is no limitation the dimension of the systems themselves, of the birth space of the systems themselves, the gain that you have in entanglement does not depend on the amount of entanglement that you communicate, but depends on the amount of pre-avail of discord. So a quantum correlation figure of mate, but of a different nature. And again, just for the sake of completeness and credit, I have to say that, well, come on, go back, yeah, good. I have to say that again, the same idea condensed at different places, because almost simultaneously when writing up the paper we learned that the group in Neusendorf, by the group led by Dagmar Bruce, Alex Trelsov should be around, Alex, Alex is there, so they had, sorry, I didn't see you before, so they had more or less the same, they reached more or less the same conclusions, the same results, and then Alistair Kay in the UK he had a very related, a very related set of results. So if you are interested in, yeah, more on a personal level and the resolution of that sort of frustration, more generally and scientifically on how you can distribute entanglement through separable carriers, then every that these three papers you should be able to get a semi-complete picture of the problem. And now say, I mean, this is not the topic of what of this presentation, right? So this is the foundations, right? That result is the foundation for what we are going to see what I would like to present at this point, okay? And therefore, so how the resolution of frustration helps you going forward, no? The results of what I'm going to, so basically an account of what I'm going to discuss is in this paper on the archive last year and this is going to appear in PRL recently, we got the proof two days ago. So given that somehow the logic towards inferring this, say, quantumness of inaccessible systems is a little bit convoluted, let's go through it together step by step, okay? So let's go through the logic of the result that I would like to show. So what is the goal? The goal is to determine if the state of a system that you cannot access directly, so you cannot put your finger on it, you cannot poke it. Whether or not the state is classical. So you want to determine whether or not it's classical without touching it, okay? So without measuring, without doing anything on it because you simply can't. And this matches most of the situations in the lab that we have, not in our quantum labs, not always you have the possibility of accessing directly the system that you want to probe, manipulate and say what property you want to infer. You need some form of the indirect access. How much can you learn about the state of this guy if you cannot access it directly? The tool that we are going to use is precisely the sort of entanglement gain that was at the basis of the distribution of entanglement through separable state that we went through before, right? So I'm going to use the entanglement gain between remote systems, remote probes as a tool to infer the non-classicality of the system that I cannot access. What is the result that I want to report? I want to show that one can actually say, one can actually infer this non-classicality through this gain, through this entanglement gain, only if during the evolution, during the interaction between your probes and the system that you cannot access, there is some discourse that gets built up, that gets established. Okay, so between the system that you cannot approach and the probes themselves. Why should we put the emphasis on discord and why is that a good quantity to analyze, to study when one wants to infer the non-classicality of the state of such a non-accessible system? Well, by its own definition, it's very definition, this code is non-zero if there is no projective measurement that leaves the state unchanged, right? So I perform a projective measurement on a party of a discordant state, of a multi-partite state in general. If there is not such measurement, then this code is non-zero. So this is precisely the key because such a possibility is there only if the state of the party that you are measuring contains needs non-orthogonal states to be described. So this statement, which is at the basis of the definition of discord, is posse is true only if I have say non-orthogonal state and it's precisely this non-orthogonality that we are going to, this lack of distinguishability that we are going to use as the non-classicality that we want to infer, okay? So let's see what is the context which attempts and conditions for this protocol to be effective and for the protocol that I want to illustrate to work. So as I said, I have a guy in the middle, I have C and I cannot access C directly. I have A and B and these are my two probes and what I am allowing for is just bi-local interactions, so I am allowing for A to be interacting with C through an Hamiltonian HAC, which I am not going to specify. I allow for the same thing between B and C so in this subsystem, so there is an Hamiltonian model that describes the interaction between B and C but there is no term that connects A and B, no Hamiltonian, no interaction that connects A to B directly, okay? Do I want to put any constraint on the dimension of C? No, I don't want to do that, I want to set it completely to leave it entirely free. Therefore, the scheme that I am going to show and the results that I will discuss, okay? They are valid, whether you have an elementary system or a very complex, a very complicated one as C, okay? So you can have a single particle, like one of the systems that we have seen this morning or a spin chain, like say, a many-body system in general, like the scenarios addressed in these morning's talks. It doesn't matter. What I can also allow for is the interaction between each of these subsystems with their own environment. So my scheme doesn't work only for unitary closed system dynamics but actually it works in general for any Markovian-like dynamics of probes and inaccessible system. And yes, I want C to be completely inaccessible, so it's in a cage, I don't touch it. I'm not going to do anything on it while I allow myself the luxury and the freedom to be able to do anything I want on A and B, including the possibility of performing measurement, arbitrary measurements on that, okay? And reconstruct the state of A and B fully. Does it make sense? Okay, so picture is clear so far. If you lose me here, you're not going to get what I want to illustrate later on.  Yeah, good. Okay, so this is the scenario. And just to make things easy and approachable at the beginning, I'm going to consider for now the case where there is no environment and this is just for the sake of the argument. The paper illustrates the full open system dynamics case, so this is not cheating, it's simply because I have 35 minutes plus questions and I want to go straight to the point, okay? So let's address the closed system case and also allow me to make a simplification. Again, this is our first step, we are going to the full case in a bit. So my first assumption is that the Hamiltonian between A and C commutes with the Hamiltonian between C and B, okay? So if you now consider the time evolution operator, right? The total time evolution operator of the system, this will be something like this. If you can read my handwriting, okay? So in general, that's what we have but under this assumption, under the assumption of commutation between these two Hamiltonians, then I can exactly break it down into that, okay? So into, can you read from that corner, yeah? Into two time evolution operators determined by HAC and HBC only, makes sense. Moreover, I can decide which is the order of the two, so it's completely in material because they commute, so it's completely in material if C interacts with A first and B later or vice versa. A bit of notation, whatever is primed in these slides is what results from the interaction between A and C. So I'm assuming that it's actually disordering, okay? So A first and B later, okay? So C interacts with A first and then with B, okay? So rodash is this form of intermediate state that I get when I apply the interaction between A and C only. And this is the evolved state. Now, if I go back to a couple of slides before, so if I go back to the result that Modi, myself, Patrek and Piani were able to prove to prove to understand how it is possible to distribute entanglement without any entanglement. Well, I can make use of that bound to the entanglement game based on this code. And what I do is that I apply it, I use it twice. I use it at the beginning, so this must be true at the beginning. That relation was true at any time. And this must also be true after the application of HAC. So these two conditions are simply the statement of my theorem back in 2012, and also say the reformulation of Alex's results. Not back in 2012, nothing else. So at this point I did nothing but just using what we knew in 2012. Now, the entanglement between B and AC is invariant if I do something on AC only, right? In that bipartition, a joint unitary on A and C, it's a local operation in this bipartition, so this entanglement cannot increase. Therefore, if I apply the Hamiltonian HAC to get something primed, that entanglement cannot change. So the entanglement between B and AC at the beginning is the same entanglement that I get between B and AC after the application of this unitary. Similarly, the entanglement between A and BC after I applied this Hamiltonian remains unchanged at the end of the protocol. So when B interacts with C, because with respect to this bipartition, the unitary between B and C is a local operation. So I have these two relations again, okay, these two relations that I can now sum, I can add mutually. There are two terms that cancel, so this guy gets canceled by this guy, and what I get is that the entanglement gain in this bipartition, so between A and BC, right? I'll begin, no, between initial and final time of my protocol, depends on these two quantities. Depends on the initial discord between C and AB, and depends on the amount of discord that I'm able to generate in this bipartition due to the interaction, AC, makes sense. Now, let's simplify things. Let's assume that at the beginning, I had no discord whatsoever between C and AB, so I had a completely classically correlated state between C and AB, and this tells us that in order to observe, again, in the entanglement between A and BC, I need to have some discord that gets generated by the interaction established between A and C. Does it make sense, guys? So what we have at this stage is the suggestion that entanglement gain in this bipartition is strongly determined by the mediated discord, not the quantum correlations of the discord form mediated by C. And the non-classicality of C along the lines of what I've introduced a couple of slides ago entails the entanglement gain. Of course, see that discord just upper-bounds this entanglement gain, so the gain might be zero. I might have discord in this bipartition after the interaction without no entanglement gain. Let's see if we can state something a little bit more precise, but before doing that, I just want to kill, pre-empt the question, yeah, but how about C interacts continuously with A and B? Now I'm doing this sort of shuttling. C interacts with A, then with B. But most of the time, C is always interacting with A and B. It's tough to gate, to gate the interaction between the particle that I have to probe and the two probes themselves, right? So, well, if I have a continuous interaction, if I have a continuous interaction, what I can do, and if I have non-commuting Hamiltonians, what I can do is to use precisely the trick that is typically used in order to understand the physical picture behind continuous interaction, which is Suzuki-Trotter. I can trotterize my evolution. I can chop it in small slices of length delta T with delta T, a very short time interval, and therefore having, again, a discretization of my dynamics, which is only approximately true, but if my time intervals are very small and are many, it's a very good approximation of what happens in the continuous limit, and therefore replace my statement, the statement that I showed you in the previous slide, with something that instead of depending on the final time T, depends on the time interval delta T that every time I'm using. Makes sense? So, my take-home message at this point is that the result holds unaffected whether you have a discrete interaction between probes and system, or a continuous one. And now the statement, so the two theorems that are proven in this paper. So, and the answer, the question, can I increase, no, can I get entanglement gain if C is always classical? So, if I don't have any gain, and any creation of discord, and the answer is no. So, there are two theorems, one for the closed system dynamics and one for the open one that I'm going to illustrate. So, the first one states that for a three-particle system with this sort of bilocal interaction, whether the commuter or not is immaterial, the gain in entanglement is zero. Actually, okay, the entanglement in this by partition is constant, so the gain is zero. If this discord is zero at all times, okay? So, unless I get some discord in the dynamics, I cannot gain entanglement. And this puts us in a perfect position to be able to assess the non-classicality of C by using, see, the gain in entanglement between A and B. Is there a question down there? No, okay. How about the open system picture? Well, we can generalize the protocol, we can generalize the result, and address just the case of open system dynamics. So, if I have A, B, and C, each couple to its own local environment, then the gain in entanglement is zero, if this discord is zero at all times, okay? So, this is the formal result that we use then to analyze some examples of, say, some experimental interest, okay? So, I'm going to use the picture where A, B, and C are open to their own environment, and what we did was focusing on an optomechanical device, on an optomechanical system. Now, as far as I understand the way, I wasn't here, unfortunately, Monday and Tuesday, but there were talks on optomechanics. Am I right, Fabrizio? Yeah? So, by now you are familiar with the system. I have a slightly more convoluted optomechanical system than what was probably presented to you earlier in this workshop. I have what is called a membrane in the middle configuration. Thank you so much. So, I have a double, say, a double cavity, a double Fabripero cavity, with a vibrating membrane in the middle. And as I hope the speakers, before me, illustrated, well, this system is typically open, right? So, there is electromagnetic noise surrounding the cavity, affecting each mode of this double cavity, and there is thermal noise affecting Brownian-like noise, actually, affecting the movable mirror, okay? So, this is an explicitly open system, and if you ask my experimentalist colleague, they are working in optomechanics, they tell you, well, it's impossible to know exactly what is happening to the mirror without the field. So, it's the perfect scenario. A doesn't interact with B. They both interact with C, which is my mechanical oscillator, and they want to infer the non-classical nature of this mechanical oscillator. This is the interaction in Newtonian, so it's radiation pressure. You have a pressure, you have seen it in previous talks, and these are the results, okay? So, this is the entanglement that you establish between the two fields in time for some parameters which fit the experimental reality at increasing power of the pumps that you are using from outside. And this is the disco that you establish between the mirror and the two cavity fields in time. So, at the beginning and at the end, you have no discord whatsoever between your mechanical oscillator and the two cavity fields. At some point in the interaction, you build up discord, and this establishes entanglement between A and B. This result can be applied used to other configuration, other physical configurations. Actually, I'm very important to experiments that have been conducted recently. These guys published this paper in Nature Physics two years ago where they reported the entanglement between these two spins mediated by a spin chain, an anti-ferromagnetic spin chain, a many-body system, of which they didn't know absolutely anything. So, this scheme can be used to understand the nature of the state of the spin chain itself. So, instead of focusing only on these two guys, one can understand what is happening to the many-body system that passes, that transfers, establishes the entanglement between the remote spins. And the similar result was reported in the group of van der Seeven two... Yeah, van der Seeven, two years... No, this year, yes. So, the paper appeared on the archive a bit ago. Where they have two remote quantum dots mediated by a third one in the middle, and they were able to entangle them by using this mediator. Okay, so I'm done. I just have two very speculative, extremely qualitative slides that show that one can understand a little bit more than just entanglement distribution and entanglement gain through this scheme. Because one can learn something about system environment correlations. I think I have, say, one of my major accomplishments is that I now have two papers on APS with sex on it.  So, you can understand system environment correlations by pretending that C is some form of environment, right? So, if it's something that you cannot access, if only AMB you can access, then C is akin to an environment, is not different to an environment. And, if you establish gain in entanglement between AMB, this means that at some point in the evolution, you must have had some correlations of some nature, yeah, these could so, of some quantum nature between your environment and the system, AMB. And, you don't need any tomography. You can understand, you can infer this non-classicality, so the system environment correlations, through a simple entanglement witness that witnesses the entanglement between AMB. And, finally, and this is definitely my last slide, well, you can reverse the picture, say, how about I cannot access AMB, how about I have them completely isolated from the environment in such a way that I must be sure, I can't be sure that the only interaction between AMB, if any, is of a gravitational nature. Then, if you probe the entanglement gain between AMB, if you see a gain in entanglement between AMB, then you must necessarily conclude that this can come only from the quantum nature, the non-classical nature of the gravitational field that mediates the interaction between AMB. And, this was the basis of another paper that we have on the archive. I'm done, so this is the group in Belfast plus edition, so Bina is around, Bina is there, and it was not present in this picture, so these are the guys that say suffer my tantrums, these are the guys that put bread on our table, I thank you for your patience, and I'll take your question if you have any.


your certificates of participation. The survey is important because we take your feedback and make adjustments to the course. There's few of you who've been at the workshop for other previous years and you'll know about me. Sorry for that slight delay. Yeah, so my name is Guillaume Bouch. I actually work at the McGill Genome Center in Montreal and I'm responsible for my informatics platform there and all of the analysis. So I'll be, well, covering the two modules this morning that deal with small variant calling and annotation and then also strong flow variant. You'll see that there's quite a bit of link with what you did yesterday. So I trust that you all had a good time yesterday and learned a lot and you'll see. So I'm going to make a lot of back and forward references that you've seen yesterday and you'll see the actual impact that some of the steps that you've done on variant calling as part of my module. So let's get started. So the objectives of this first module this morning is to really get an overview of the variant calling analysis where it flow, right? So that's really the key message for this particular module. So we want to understand the basic principles of variant calling, know what the effects and how we can improve variant calls. Once you actually have the variant calls, learn how you can filter and annotate those variants and then in the practical we'll actually go through all of that. So we'll sort of split the time between really going over the general principles and then in the lab you'll actually, we'll go through the steps and do it. So, and then one of the links to what you've seen yesterday is that we're going to be using IGV in parallel to really look at the results. Okay, so this is a simplified view of variant calling and how the various steps that are associated with variant calling. So at the top you have, so at the top you have just, you know, looking at the raw reads and in some case trimming, so we'll go over that a little bit. You've done as part of module two really many of these steps that are the alignment. So first step, so you look at the data, you clean the data in some case, you align all the reads onto the genome. So this is the main alignment step. And then you can do the actual variant calling. In parallel to that, you know, there's going to be all sorts of statistics that can be generated. So another way of looking at this, so again it's really a sort of a workflow of analysis. You're looking at the reads. Mapping the reads is really what you did as part of module two. And then you get to the variant calling itself. So this is what we're going to be doing this morning. And then also the structural, the larger variants which we'll cover in module six. So before I jump in module five, I want to cover just a little bit some aspects, some additional aspects of quality control and pre-processing because in some cases this, and not in some cases, in all cases this will have an impact on the variant calling itself. So it's pretty important that, and so again some of these things were discussed already yesterday but given how important it is, I wanted to go over some of these messages again. So it's really quite critical, you know, when you're doing bioinformatic analysis just like if you're working in the lab that you actually look and follow each step and do some control and quality control at every step of your analysis. And that's the first step of looking at the raw data before you run through a whole pipeline and run through a whole series of command lines or commands is really important. So you want to be able, and so again, you've covered that a little bit but I want to re-emphasize that that's quite critical to know where the samples that you're going to be analyzing are coming from, where they use, especially if you're going to be doing a comparison of different samples, where they all sent a sequence at the same time using similar protocols and the same instruments. Are there any technical issues that are affecting some of the sample?  So, yeah, and in most case you're going to be comparing multiple samples and not just one, so I mean, this is quite relevant. So you've seen, or this tool was mentioned already yesterday but it's a pretty critical tool that's used quite a bit, I think. There's alternative tool but this one already packages many components that allow quality control. It's FastQC. So this in the practical, again, before we actually go through the variant calling step we'll actually run the samples themselves through this tool and you can look at the specific results. So the samples that we're going to be analyzing in the lab actually look quite good. So this is one example of the read. So you see the typical profile where this is the quality scores of the reads per base and you see that the quality although goes down overall the profile is quite good. So I mean, it's too small to actually see the values but you see that the quality of the read is quite good. So you have all sorts of different metrics that you can look at. This is a particular view that shows where on the slide the quality of the reads depending on their position on the slide and you see again all of the green and yellow which is a good sign. The second read on, again, one of these data sets that we're going to be studying in the practical is also pretty good but there is one thing that's flag which this is still overall quite good you get a sense that the information of the per tile quality values sort of there are some regions in the tile that had overall lower quality score. So that already sort of indicate that maybe it's still I mean in this case it's still quite good but again if you're going to be comparing multiple sample it is a good idea to double check some of them or go back after you've called if you see that you have lots of variance. Yes, so I guess I'll get to that. So depending on the patterns that you see from these results then you can definitely trim or remove based on... I mean one challenge to be honest when you look at this I mean again these are very good reads you'll frequently get so profile where you have some red and then it's like is this a problem or it's not a problem again I think it's for the most part the variant calling itself will be robust to some of these slightly lower quality reads but it's really a question of seeing for instance if one of the sample has an horrible profile compared to the other ones you want to know that upfront before you start doing all the variant calling. So this is... I put the emphasis here on the quality of the reads but again I mean even if you sequence you get sequences from the genome center where I work I mean don't trust us and look at the reads that we send you before you start your analysis. So the quality score is one thing in some case there's the presence of adapter sequences in your reads and then that's another thing I mean this is a I guess an old figure of a Selexa read but basically in the sequencing process you'll have adapter that are alligated to the sequence that you're interested in in some case the read actually goes into the adapter so looking for adapter sequences in your reads is another thing to watch out for that would be flagged by a tool like FastQC. So looking for an overrepresentation of sequences this also can be an indication that there was a problem with the sample that you're looking at. So what happens to answer your question and please don't hesitate if you have other questions but to answer your question if you do identify issues with some of your reads there's lots of tools Trim Pneumatic is one of them there are many others that actually allow you to remove some reads or trim some of the reads you know one thing that used to be quite common was to trim from the end of the read that typically are lower quality so you would actually start trimming your reads from the three prime end based on different quality metrics so again you know for most purpose actually reads now are very high quality and it's not as necessary to trim the reads because again the downstream bearing calder will be robust to having some lower quality reads and we'll still be able to have high accuracy but this is still something to keep in mind again given that in some case you might have some samples that are older or lower quality okay so this was really again just sort of an introduction and warm up before we jump into the actual variant calling so again you know you've covered the mapping quite extensively yesterday so but I'll go back to some components that were presented in the context of the mapping module to explain and show the impact that those have on variant calling so this I'm talking about variant calling variant calling so I mean maybe I should have had this slide first this is really the goal of this module right so we it's quite small I hope you can see or on the handout but this is a view from IGV at the top you have potentially a tumor sample you see all of the reads and they all have a difference or most of them have a difference at a particular position in this contrast with the normal sample from the same individual where all the reads actually map that reference genome so this is really the information that we want to be extracting from all of the reads so we've mapped them and now we want to be able to distinguish cases like this where there's clearly a difference I mean you don't even need a fancy algorithm to identify that there's clearly a difference at this position in this sample relative to the reference and this contrast with the sequencing errors that are more randomly distributed sort of much more sparse in our spread throughout so this is what we're after so how do we extract this systematically from our data so this is another representation or cartoonish representation of the same thing so all of these are reads we want to be able to detect whether they're SNPs, whether they're variants or they're somatic mutation where multiple reads are supporting the fact that this is a variant relative to the reference and distinguish that from the sequencing errors that we expect to be randomly distributed as I said so there's so we'll be using a few things I mean one of the things that we'll be using to actually call variants will be multiple the amount of reads that are supporting a change so multiple reads that'll be one piece of information we're also going to be using the base quality itself so that's why I was saying actually most read caller, variant caller the trimming step is less critical because you do take into account the base quality of the base within the read whether it's supporting a variant or not such that lower so we can make use of the fact that with every read and every position there is a quality score that's associated to how clear the signal was and we can embed that in the variant calling algorithm uses that information so in other words reads that actually are calling a variant with a high quality score will be weighted up more strongly than when the base itself within the read was already also ambiguous so actually the next slide is slightly different from what you have in your handout and that's pretty much the only equations that I'll be showing but I wanted to at least scare you a little bit in terms of so this is not going to be part of the practical per se but again there's a number of variant caller we're going to be using the GATK variant calling variant caller but most of them work on some of the same principles so that's why I wanted to at least give you a sense of what those principles are so most of the variant caller are using this Bayesian modeling they view this as a Bayesian problem where you've got all of this data and what is the probability of a particular genotype given that data we don't need to go into that too much but what we're interested in is knowing the probability of the genotype being a variant given that all of the read data that we have and what this does is really again it's not so critical the Bayesian rule actually reverse the problems and says a given genotype was the probability of the data and then you integrate over all the possibilities again that's not so critical one thing that's also shown here is that you have to take into account that the human genome is deployed so you've got heterozygous sites so you need to be able to call sites that are knowing that there's actually two haplotypes in the genome but again what is the probability of a given genotype given all of the data we have another component that goes into that are these quality scores that I mentioned so the fact that the particular basis of a low quality is going to be taken into account in the caller so again the purpose of those two slides were just to scare you a little bit from looking in the algorithm itself what's coming now so again the general principle is really just this is a way of integrating all the read information and then giving a p-value of there's a variant here so what I think is probably more relevant to you is really looking at what actually is affecting the quality of variant calling so I'm going to go over four of the things some of which you actually were doing yesterday in the mapping step to explain how that actually improves and affects variant calling and in the lab that's also what we're going to be covering so local so I'll go through them one by one so local realignment after you've mapped the reads you know one step that is usually done is realign the reads locally because initially so initially all the reads are mapped independently to the genome and so you get a first mapping of those reads but one thing that's been shown is that when you actually have indels you have here the mapping of individual reads ends up being imperfect and often makes mistakes close to these insertion deletions so one step that's quite important is this indel or local realignment which is typically done around indels or around places where there's lots of variants so after you read individually to the genome you actually look at all the reads all at once and figure out what's the best local mapping of these reads and you see the problem is that before the realignment you actually end up making systematic mistakes in the read alignment that are going to lead to false variant calling. These look just like quite similar to real variants you've got multiple reads that are saying the same thing but they're actually all making the same mistakes so local realignment is one step that affects variant calling quite a bit and so in the practical we'll actually I'll show you the effect of that realignment step so that's one thing that's important for variant calling is duplicate marking so another thing that can make so if you don't do this step you might get multiple reads that are actually really the same reads that's been PCR amplified for instance and that you've got multiple reads that are identical but because you're going to be feeding in how many times I see this variant into the algorithm you're going to think again that this is a variant even though it's just the same read that you've read multiple times so it's important in the context of variant calling in particular if you have amplified DNA that's been sequenced you can remove reads that are identical with their start and end as you see here because you actually only have one observation here and so you don't want to be pretending or sending multiple copies of that observation to the variant caller because you're likely to get again a false call so that's a good question so for any seek it's trickier so typically you don't want to be removing duplicates because for highly expressed gene you might actually get multiple observations that are identical in terms of their start depending on the type of read that you have variants well then I would still recommend removing the duplicates because you would expect or hope to see multiple independent reads covering the variant variant calling in RNAseq is actually quite tricky in part because because of the alignment steps so in RNAseq data you have because of the exon boundaries this effect happening quite a bit as well where the alignment of RNAseq reads on the genome around the edges of the exons are very similar to this and sometimes will lead to also false calls but to answer your question if to do variant calling in RNAseq data I would still then remove the duplicates because you would want to have multiple independent reads supporting that variant because otherwise I think you quantify differences in expression or quantified expression yeah same thing I think because again for the most part right so I mean it's only the highly expressed gene that will give you really identical reads right so by removing the duplicates what you do is you're actually perhaps lowering the expression of the most highly expressed genes because now you've got identical start in repeatedly but I think that's less of an issue than actually getting false calls and false positives okay so we talked about local realignment duplicate marking another one is base quality recalibration so another thing that's been shown to be important is not to trust Illumina I guess or whatever sequencing company provides you with quality scores I mean I'm sort of have joking but it's just it's been shown that you know so the instruments makes an estimate of the quality of the reads but what's been shown is that it makes systematic mistakes so so depending so they're based on the reported so they've tested and they've shown that the reported quality is really higher than the actual quality right so it's overly optimistic and that is not the end of the world but the problem is that it's overly optimistic in systematic ways as well so if you look here for instance there are specific types of calls where again the quality specific dinucleotides where the quality score are systematically off so again so this step actually you know looks at all the reads that are mapped on the genome looks at known SNPs and actually adjusts and corrects the quality scores to remove these artifacts so again this is sort of a subtle difference but it's it also improves the varying calling to adjust the quality scores based on real you know based on the data itself so the last the last step that can improve varying calling and this is I guess slightly more advanced and maybe not used as much because for this you need to know a bit more about the population structure so I'm putting it here to give you so that at least you know that this exists and that this is there so everything I've said so far I was really talking about doing varying calling one side at a time right you look at one base you look at how many reads are saying this base is different but I have the first mini-practical of the day I guess let's see if you're awake and if the coffee is kicked in so I mean as you most of you probably know you know the genome so we all share you know blocks of DNA right so we inherit blocks of DNA such that everything is inherited as a haplotype so you've got real whole blocks of DNA that are passed on from generation to generation so in a given population you'll only have number of haplotypes that are available in that population so if you have that knowledge that there's only these two haplotypes in that particular population you can use the information about flanking positions to improve your calling at a given position so if you see that all of your reads at other positions for instance are this A and G so can you figure out what is it that's right so you can so this is making use of so you have to make a hypothesis I guess in terms of the population structure and things like that but you can feed that information to actually improve the quality of your calls and I say that this is a bit more advanced because this is I mean again so then you have to make more hypothesis about your data this has a big impact especially if you have low coverage so if all you have is 3x or something like that of your genome it becomes very difficult to call positions because you don't have a lot of reads that tell you it's an A or it's a G so then any trick you can come up with as much information as possible makes a difference so this again if you have low coverage in particular then using this type of population structure information can help you in varying calling and again this is something that in the context of the GATK framework was shown to have a big impact on the accuracy but this is especially when you have low coverage in your sample otherwise it's not nearly as critical so I've been talking quite a bit about GATK because this is really in the lab the framework that will follow so so the main components of that software suite is really starting from the raw reads you've got this mapping the local realignment and the duplicate marking so again all of this is what you covered yesterday in the context of module 2 I think the base quality recalibration that I mentioned we didn't do on this sample but after that you've got your line reads and then you move on to the varying calling itself which is what we're going to be doing in the practical here again if you have population information this is something that you could also add but we won't do this because this is not so much needed if you have high coverage samples so we're going to be working with a toy example just so that we can actually run things in the lab itself but I wanted to give you a sense of what doing it on the real whole genome data set would be like in the practical we're only going to be doing this in a very small region of chromosome 1 in the real setting in the genome data set after the alignment you would have the BAM files this typically for whole genome of 30x is roughly in your order of 200 gigs so this is again you've recalibrated you've removed the duplicates and you've done the realignment step so once you have the map reads you're doing the variant calling itself would probably take many hours even on the cluster with significant resources well you'll see how long it's going to take with just a tiny region of genome it's going to be quick but if you were doing it on the whole genome it would for sure take some amount of resources but basically after this step this is really the algorithm or the tool that we're going to be using for this but there's other tools that also do similar things integrate the information from all the reads in a slightly different way to call the variants SAM tools is another very common one free-paces another but after this we actually the raw variant file which is this the calls themselves and this is going to be much smaller in size so any questions so far? all good so again just to re-emphasize this in the practical I'll show you how different the variant calls are going to be depending on whether you're doing this correctly or not so why do the realignment? why do the duplicate marking at some level? you'll see how it's going to change a little bit the calls so just finishing this introduction before the lab I guess so we'll talk now about what do we do once we get this list of variants so the file so the BAM format is the file the read with the location, the mapping information the variants that we're going to get are in the format called the VCF format so we'll get to see these files so the VCF format so there's lots of headers the key thing is that all of the variants that are called are going to be called you'll have information about the specific position and then information about what is the reference allele at that position and then what was the variant that was observed and importantly you also get a quality score for the variant so the output of the variant caller is not just a variant of course but also a quality score so this is different than the quality scores of the individual bases within the read this is information about how confident the caller observed the variant there so one slightly tricky thing is that the quality score varies so the interpretation of these quality scores depends on the aligner that you use the variant caller that you use so the scale of scores of course the higher the better but the actual interpretation of these quality scores you actually have to look into the variant caller in terms of what it represents so one tricky thing is how do you filter what's a good quality score for the variants that's partially defined by the variant caller that you've used and then you have additional information on each of these but again this is going to be a file where every line after the header will be a particular variant with the information on the variant itself so it can get quite complicated in terms of the different info fields and things like that and all the details are in the VCF file format that's linked here so another so typically especially if you do this whole genome or whole exome you're going to get the very long list of variants and the first thing that you're going to wonder is how do you filter which ones are the good variants so there's really two main strategies for this so you can definitely filter your list of variants based on different sort of manual cutoffs you may be obviously the quality score is an obvious one so maybe you'll say I only trust variants that are above a particular quality score or you require a certain depth of coverage you only trust the variant so the output is really so anything that's variable will be reported and then it's a question of sort of filtering the calls themselves based on the quality score and depth of coverage for instance I mean you can do that and again it's quite reasonable to say that you want quality score again above 30 and depth of coverage of 10 or something like that to get it sort of depends on whether so if you are looking for a particular variant in a particular gene maybe you want to have everything that's potentially a variant because you're going to be going through it in detail if you're doing this genome wide and you want to start with top candidates then maybe you want to start with the more a stricter criteria for filtering and then really just saying I need 10x coverage on that particular variant and I want high quality scores it really depends on what you want to be doing another quite well another nice feature is that it has what's called a variant recalibrator which is a way of re-ranking or re-ordering the calls so each call was done individually and again it produced a score but one strategy that you can use is you actually feed in to the algorithm known variants known sites that are variable so taking for instance high quality SNPs from the HapMap project so these are true variable sites in the genome so if you feed that in to the algorithm and you can also feed in in this case of the variant recalibrator tool of GATK so you feed in high quality SNPs that are so that you can use to basically train the algorithm to see how good are my scores or how bad are my scores so you feed in these variants to know what you're missing which should be there and you feed in and DBSNP actually contains both very high quality SNPs but also contains a lot of false positive SNPs so again in the context of GATK using this information to sort of tune its scoring has been shown to actually improve the quality scores so again this variant recalibrator all you have to do is to be able to use that component of the package is to feed in a case of variants that you expect to have in your sample and variants that potentially contain some false positives and it's going to use that to re-rank your variants from high quality to lower quality but again this is perhaps a bit tricky but to go back to this you can use and for the most part this is fine some manual filtering based on some criteria that are reasonable to you but another option is to use a more advanced ranking where you feed in some data sets and GATK can learn what are the good cutoffs automatically that's right so it has both very good quality SNPs but it's also within this set there are many false positives so within this what they've shown is that SNPs that are in the half map that are in DBSNP but things that are only in DBSNP and not in half map are enriched for some dramatic false positives that have been observed in the past and have been reported but have never been validated except for that so it uses that information to know how many of that particular set am I calling versus how many of the half map SNPs am I calling and it improves the score in the ranking okay but another thing so this is actually an example from a project that I was part of where we sequenced 100 olginal kidney tumors and even after quite I think good merengue calling and stringent filtering we are still left with a lot of SNPs and I wanted to show you that just so that you know what you're getting yourself into if you're doing olginal sequencing and you're interested in doing variant calling so in this case this is 100 tumors these are the somatic variants in total we had more than half a million of these calls with a significant number of somatic calls so even after you have your set of good quality variants you need to start annotating them I don't know what on this display you've got a few things that disappear but one key type of annotation for instance to begin with is the cody mutations for instance right so within all of these variants you want to know which ones are hitting genes you want to know which ones are potentially modifying the coding sequence of course all the other ones are also quite interesting potentially the annotation of the non-coding variant is quite tricky but after the variant calling and filtering the next step is really annotating which ones are potentially affecting coding genes or which ones are potentially affecting enhancer elements and so on so for this there's again a number of tools the one that we're going to be doing in the practical is SNPF so figuring out the effect of SNPs or of mutations so here you're actually feeding in annotation from the reference genome that your sample is coming from and it's going to predict the effect on whether it's hitting the known gene whether it's synonymous, non-synonymous it's all of your variant with this kind of basic prioritization so you'll have so again there's lots of tools to do this and it's not the perfect science whether it's synonymous and non-synonymous some of these things are I guess more robust for sure but you still have issues where you have multiple transcripts so I mean this is going to be informative information but there's different ways of doing it so to continue with this workflow starting from the original BAM files now we had our raw variants so we can use GATK again to filter the variants and sort of prioritize them and identify the better one and then you can also use tools like SNPF to annotate the variants and this is going to actually give you this list of annotated variants I guess so one thing that's actually quite subtle the difference and it depends on the quality of the sample again and most of the difference are especially as I was saying when you have lower coverage and there it starts to make more of a difference one thing that we use that I don't have here but I would really recommend is that there are some tools that allow you to compare different versions so you download a particular data set and then you can use GATK on using whatever set of parameters or you can use GATK and then you can actually compare the results so again the differences will be especially if you're looking at low coverage but there are some one of the nice things about the GATK you can run it into this sort of normal mode that I mentioned this variant recalibration for instance and some of these additional more advanced features are also quite useful in terms of so that the variant calls are well yeah so the question is what would you do if you had samples with different coverage I mean I think so it depends a little bit on what you do downstream and how you interpret the variant list that you get that has higher coverage you do expect to be able to detect more variants but for most purpose you know unless what you're interested in is the rate of variants or things like that typically you know you want to extract as much information it might be that in that other sample you know I think it wouldn't be so if you were doing a comparison of RNA-seq data that's a much bigger issue because then you're comparing levels right well but with RNA-seq if you're comparing different samples that have very different coverage it's going to be trickier there potentially down sampling is going to help making a more fair comparison with the variant calls if you don't down sample all you have is basically more detection in the sample to have more coverage you know I'm not sure that would be a big issue well if that's the analysis that you're doing then yes you're right so I think that's definitely correct if you're doing cancer discovery project and the main thing is not you know looking at a total number of variants but really as a discovery of what kinds of mutations are in your sample then it would be slightly different and probably wouldn't so the next step is going to be the lab so any other questions on the overall workflow and pipeline?


Steve, this week we're all wondering about a bizarre plot that the Iranians allegedly hatched to assassinate the Saudi ambassador of the United States, Adol Jaber. Allegedly, Iran enlisted Mexican drug traffickers, a group called Los Zetas, to carry out the act. Does this raise greater alarm about Iranian activities in the Americas? And does it also raise more alarm about the state of Mexican security? Well, I think it certainly points out that Iran is still active in the Americas. To what extent this is indicative of what's really going on, it's very hard to tell because it looks like it's either amateur hour or it's a change in the modus operandi of Iran and its clandestine operations in the Americas. Who is Los Zetas and are they capable of carrying out such an assassination here in Washington? Los Zetas is a violent drug cartel in Mexico. It consists mainly of people that have left Mexico's security forces and then went into the trafficking business and compete against some of the other violent cartels in that country. And they have reached, as do all of the cartels now, into almost every city throughout, major city throughout the Americas. So they do pose a risk, but they pose a different kind of a risk, a drug trafficking risk and an attendant violence risk to people that get caught in the crossfire of some of their crimes. But they're not necessarily known for conducting high risk operations to assassinate diplomats or to conduct bombings and things of this nature. You have a report coming out soon looking at Iran's relationship, various relationships in the Americas. How concerned should we be about their reach and their influence in this hemisphere? Well, we may not learn very much from this particular incident or we could end up learning quite a bit about perhaps a change in operations and ways that Iran is going about things. It could be a total fain. We don't know that just yet. But I think there is some concern over a long period of time ever since the Iranian Revolution and the break in relations between Iran and the United States. There have been periods of rapprochement and periods in which relations have gotten tense and it appears that we're in one of those periods where relations are getting tense again. Secretary Clinton said they actually crossed a serious line here. If it's true what is being alleged, if that's true then they have. The question is, what else have they been doing in the hemisphere during this period of time that there has been an estrangement between our two countries? And it's interesting to note that Iran has become interested in the Americas because of really three things. First of all, it offers an opportunity to poke Uncle Sam in the eye in an area which is essentially the region in which the United States is located, which is close to our shores. The other thing is that it offers an opportunity with some countries that are willing to do business with Iran like Venezuela, Ecuador, Bolivia, Nicaragua and some countries in the Caribbean. It offers an opportunity for them to evade some of the sanctions that the United Nations and the United States have placed on their banking and also dealings with their petroleum and nuclear industries. And the other thing is that it offers an opportunity for them to conduct public diplomacy or a kind of theocratic diplomacy to do an outreach through some of the clerics, and you have to understand that it is a theocracy that very much Shia Islam is part of the scheme with government in Iran and that in conducting an outreach that tries to explain Shiaism, to evangelize it, if it's possible to use that term, is one way that it can begin to make friends and converts in our hemisphere. And that's important in broadening the base of support that it may have in certain countries in the region. Do you think this incident changes the debate about border security in the United States? Does it make it more necessary to secure our border with Mexico? Well, because the Zetas weren't really involved in this and it doesn't really change things that much, but border security always has been important. It's going to remain an important question that the United States has to get right, but it has to get it right in a certain way. We can't live isolated from other countries and particularly countries that are trade partners with us in this hemisphere and in other parts of the world. So we have to do it in a smart way, which means using technology to avoid draconian measures that would cut off trade, cut off intercourse that we have with other peoples in the world and the friendships that we've developed. Steve Johnson, thank you very much for your time. Thank you.