and brings in a number of VMware executives. Pat Gelsinger's here as last year wearing a baseball shirt. Last year we captured Pat on the pitchers mound. John Furrier is actually talking to Pat right now, trying to drag him into the cube. Raja Desgupta is here. He is the director of IT at ExoStar, a NetApp customer. Raja, welcome to the cube. Welcome to the team. Thank you very much. I appreciate it. So you just came here, right? I just got here. I walked here from my hotel, actually. The traffic's pretty tough. The traffic was too jammed. I said, you know what? I'll just walk over here. But what do you think of the venue? It's fantastic. Fantastic. The once in a lifetime type. Yeah, this is a fantastic facility. The ballpark here is great. Have you ever seen a game at AT&T? I have not. I'm from Virginia, so we've, you know, Washington Nationals, Baltimore Orioles. We never really have a team that wins, so we try to, you know, switch back and forth every year. Well, I'm older than you. I remember when the Orioles used to win. So anyway, tell us a little bit about ExoStar and what's going on there. So ExoStar is a SaaS provider of solutions in the cloud for aerospace and defense, and also for life sciences. So our focus is things like identity management, collaboration, secure collaboration, and supply chain solutions in the cloud. Okay, so you've got some pretty intense security and privacy requirements going on over there. Well, tell me a little bit about the history of the company and its offering. So ExoStar was founded back in 2000, the kind of the dot-com heyday, and the idea was that you'd bring the biggest aerospace and defense companies together, and they'd form this big exchange, this business-to-business exchange, where billions of dollars would flow through the systems.  You hit critical mass? We hit critical mass. So now we're kind of looking at other verticals like life sciences, you know, big drug-type companies. And so, you know, it's been a fun ride for us in terms of, you know, working with our vendors to deliver these solutions in the cloud. Well, we've seen this in a number of industries.  It's an infrastructure play, but we've seen, you know, exchanges, industries gathered together, multiple customers coming together within an industry. And the key there is, and I wonder if this is what's going on with ExoStars, you start to get birds of a feather, you get best practice, you get data, there's certain analytics to go, and you now start to get this flywheel effect, this critical mass. I wonder if you could talk about that specifically in the defense sector, and then maybe we could talk a little bit more about life sciences. Right, so for us, there's definitely economies of scale, so companies like Boeing, Lockheed Martin, Raytheon, Rolls-Royce, BAE, these are the biggest aerospace and defense companies. So there's definitely economies of scale in terms of understanding their best practices, deploying their best practices in our solutions, and what we've done over time is basically get to a point now that we're kind of seeing the exponential 30, 40, 50% year over year growth in terms of our technology footprint, and we want to basically go with the best of breed to scale as we grow. And they want to be with a company, with a provider that understands their business, so that's an advantage for you, but at the same time, I'm sure they ask you all, I want to make sure my competitors aren't seeing my data, so talk about that conversation, the data governance conversation, and what you're seeing there. So with aerospace and defense, there's a lot of concern about security, securing the data. We are able to host these solutions in kind of a multi-tenant environment while still maintaining the security, and NetApp is a great partner for us in doing that. We can segregate data, make sure we have the demarcation between one customer's data and another, all while keeping it secure, keeping it encrypted, and being able to scale as we continue to grow. We were talking to Dan Timko, a previous guest, and we were talking about the whole prison thing, and the Snowden debacle, and so forth, and what he was saying is it's created an increased awareness within his customer base of that whole security. Now, you're serving a very security conscious crowd, so maybe there's been no change in your business, but presumably in the life sciences business you might see this, what are your expectations as a result of sort of the new discourse that's going on right now in the country? So there's a lot of concerns about healthcare, patient data, those types of things. So what we've tried to do is kind of take some of the tenets that we've used in aerospace and defense and use those in our life sciences vertical in those solutions. So things like identifying, if someone is really who they say they are, for companies like Boeing and Lockheed Martin, they have thousands of suppliers, thousands of anywhere from mom and pop shops up to huge suppliers that they deal with. And so we basically secure that network using digital credentials, using one-time password type tokens. So that's kind of our focus, adding that security overlay over all of our solutions. Describe your infrastructure a little bit. Actually, John, you got a question. I'm sorry. I mean, as usual, I'm dominating that this question here, so.  I didn't even notice you were there, John. I knew this. I was like, I can sneak out and get a chance to talk to Pat Gelsinger and Radu from VMware. Great presentation. Pat Gelsinger again with a NetApp shirt on. Really good form by VMware guys. I'll tell you, they're obviously very committed to NetApp. It shows, they're walking the talk. I've got to say, just put that update in. What I want to know is, is that we talked to a lot of the vendors selling stuff and in the day, people have built in solutions, but we would like to know, and the audience would like to know, what are the biggest inhibitors right now to a full adoption of cloud? Is it security, is it fear, all of the above? What's your take on it? Yeah, I think it's mostly fear and mostly kind of lack of industry standards for that secure overlay, that secure bubble in the cloud for admission critical. So we see the kind of the non-sensitive type services going to the cloud very easily, the non-core services, but in terms of those top tier, it's going to be a slow progression, because IT managers are not going to want to risk their business by putting their critical data in the cloud and then having an incident and putting themselves at risk. They're used to remember the old vaporware days, someone would announce a product and ship some beta code. You can't afford that kind of mistake, not that it's people are doing that, but that's a fear. Cloud is this potential risk. Just the potential scares people. That's what we're seeing. The reality is the economics are just insane. So what's your take on some of the economics, not like the number numbers, but like order of magnitudes of simplicity and ease of use. Right, so for us, we've heavily invested in the FlexSpot solution and what we were doing before is taking a mishmash of different products and technologies and thinking we were smarter than the vendors and what happened was our TCO was terrible. I had six or seven engineers had to be involved in every single deployment and it was borderline ridiculous. And so fast forward two years, now you have integrated solutions like FlexSpot with NetApp and Cisco and VMware in a tightly integrated solution. And that allows us to cut the time of deployment weeks and months to literally 15, 20 days for a full solution from design all the way to implementation. Yeah, it's interesting you were said, thinking we were smarter than the vendors. That had to be a cultural shift, a mindset shift, but you guys are in the business so you've realized early on, hey, you know what, we could probably do some things better. Right. So how did you shift those resources? Where did you shift them? Yeah, so Maria are NetApp contact. One of the things I tell her is that as kind of an IT technology manager, you know the three dirty words are I don't know. If an engineer comes to you, there's a big issue with your solution and you ask them what's going on and they say I don't know. That's pretty much a death knell for you. So for us, having these tightly integrated solutions gives you telemetry, it gives you kind of a dashboard, a single pane of glass. So that you don't have that issue anymore. The other thing too, the other comment you hear is we don't do it that way. That's a classic killer phrase, right? Oh no, no, we don't do it that way. That's not the one that's down. Right. And those people fall behind, definitely. All right, let's take a pause for a second here and let's just reflect on where we are right now. Sure. Share with the audience where we are right now. Where are we? Where are we? So we're kind of, I think we're kind of in the second inning here. You know, the first inning was right outside the infield. We're a short infield outside of second base here at AT&T Park. Paint the picture of the folks out there. What's going on around here at the park? Right. So here, I mean, this is just more than I can imagine. It's like a party on steroids here. You know, this is my first time to VMworld and to a NetApp event like this. So it definitely, it's hard to talk to you guys because I'm distracted by everything that's going on. So we are live here in San Francisco at AT&T Park home of the San Francisco Giants. We're actually outside of the infield, right outside, short, outside second base, short outfield between short and third. I'm with Dave Vellotti. This is theCUBE. It's a special presentation with the tech athletes and it's a party going on all around us. We got guys doing the 40 yard dash, right? We got people seeing how high they can jump. We got the San Francisco 49 cheerleaders over here. Food, drink, people hitting golf balls, people hitting baseballs, throwing baseballs, seeing how fast they can throw. Olympic soccer, a star, signing autographs. We had pro athletes. This is an amazing event and NetApp is really laying it all out for their customers. So one, let's talk about NetApp since we have the NetApp on the mind. What about NetApp? It gets you excited about FlexPod and the solutions. NetApp is, you know, from our perspective, one of the leading storage vendors, leading the best of breed. And we had a lot of good customer references before we even went down that route, who had gone with NetApp and had great success in terms of managing the quagmire that storage sometimes becomes in cloud type companies. So for us, it was kind of a no-brainer because we were already looking at VMware. We were already looking at Cisco. And so that tight integration for us really was a no-brainer that we could basically use this as kind of a Lego, a building block to scale for the future. Dave, what's your take as an analyst? You see all the players. What is NetApp doing right now? What are they doing? And I want you to break it down into a baseball analogy. What are they knocking out of the park? What are singles? What are doubles?  Are they going to hit slugging percentage? What's your take? Well, I think I have to think about the baseball analogy, but I guess they're, in a way, you know, a specialist in that they're focused really on the storage. NetApp's the last, you know, large independent storage company. I mean, a lot of independent storage companies, but NetApp's the last public independent storage company. I don't consider EMC one anymore. EMC's got the hands and so many different things. They're this, you know, federation, they own VMware, et cetera. So NetApp is exclusively focused on storage. So I guess in baseball terms, they're like the best shortstop in the league. You know what I mean? And they do that better than even their jeeter. Yeah, but they're more, you know, their customers are very technical. One thing about NetApp is, you know, they do have technical customers. They're pretty savvy. They have a good, savvy customer base. So, you know, make them more of a running team, small ball, maybe, a competitor like Monster. Well, the interesting thing about NetApp is they've changed over the years, right? They've transformed themselves. A lot of companies have, but especially NetApp. I mean, they really started out selling into the sort of workstation market, right?  So they've made these big bets that they talk about a lot, and they've been a survivor. Okay, we got to get the break here. Raj, final word. Hey, how are you? Welcome to theCUBE. This is the kind of thing we got at theCUBE. We got people cheering for us. We got the NetApp tech athletes. We're going to break in this segment. What is the big thing about just, we'll end on a note here. What's the big thing about NetApp? What should people know about what you've learned and enjoyed with NetApp?  All right, this is live. Come on in, girls. Hey, show what we got here. We got, this is what we're all about here. Center camera. That's what we're talking about. Guys, come in here. This is David Lante, this is theCUBE. This is the NetApp customer. What are you, big happy customer. This is theCUBE. How's it going? This is where we extract the signal from the noise. This is our special presentation live at AT&T Park, outside on the outfield. This is a special edition of theCUBE, live in San Francisco. How about a cheer for the NetApp customer? All right, this is theCUBE. We'll be right back with Thomas Stanley, S.B.P. of Global Sales and Alliance right after this short break.


Is it possible for anyone to seek after God? Well, the answer is no and yes. I want to pose a question that comes up fairly often, and so I want to pose a question for you. Kind of get you to think about this a little bit. It's not really a trick question, but it's a question that does make you think a little bit what's being stated and what we see in the scriptures. In Romans 3, we see this statement that Paul is making. He's contrasting being Jewish and being Gentile, asking about, is there any difference? Is it any better being a Jew or what have you? He says, what then verse 9? Are we better that is the Jew than they the Gentiles? Not at all, for we have already been charged that both Jews and Greeks are all in the same. That's important. We'll come back to that in a second. As it is written, there is none righteous, not even one. That applies to Jew and Greek. Notice what he says here though. There is none who understands. There is none who seeks God. That's the issue. There are none who seek God. These all have turned aside. Together, they have become useless. There is none who does good. There is not even one. So, just by looking at that, it would seem to say that there is no one that seeks after God. You would probably concur with that. Obviously, you would concur with the statement. The question is, what did he mean by the statement? Is it true that no one can seek after God? Well, let me give you a scenario and then go back and explain it and then look at the scriptures as well. Look at the Old Testament, the New Testament. Look at the Greek as well. That's important as well. Imagine you going after a car. Someone is driving away and you run after it. You seek after that car. So, just be using the same verbiage. Well, how long are you going to do so? You can go after it, but not for very long. You just don't have the energy nor the speed to do so. Now, the issue of ability and desire comes up. Do we have the ability to seek after him and do we even have the desire? Well, to get to the heart of the matter, let's go back and I want to look through a few things to see. Is it possible for someone to seek after God? Certain people of certain theological persuasion will say, no, it's literally impossible. You cannot do it. Mankind is so depraved, so messed up that he will not even attempt to seek after God. Can't be done. However, let's look at some passages here in the Bible and let's see could it be done. So, what I just did was just to type in sought after the Lord, sought the Lord. We see in Exodus 33, Moses used to take the tent and pitch outside the camp a good distance from the camp and he called it the tent of meeting. And everyone who sought the Lord would go out of the tent of meeting which was outside the camp. So, here's a verse where it seems to say they sought after the Lord. What about 2 Chronicles 14-7? For he said to Judah, let us build these cities and surround them with them, walls and towers, gates and bars. The land is still ours because we have sought the Lord our God. We sought him and he has given us rest on every side. Seems in the case they sought the Lord. So, now let's go to the next passage and see the same thing. 2 Chronicles 22-9, he says, he also sought Ahaziah and they caught him while he was hiding in Samaria.  Who sought the Lord, Jehoshaphat, with all his heart? So, here we see some passages that seem to indicate that people can seek or have sought the Lord. 2 Chronicles 26-5, he continued to seek God in the days of Zechariah who had understanding through the visions of God. And as long as he sought the Lord, God prospered him. So, in the Old Testament, we see examples of people who sought the Lord, who went out after the Lord. So, how does that compare when Roman says there are none that seek after him? Just wait a minute, we'll come to there in just a second, but I want to also put up some other passages. What about Jeremiah 29-13?  That's a key. I'll put a tag on that because that's very important. It helps us to understand what's being stated in Romans 3.  There is the issues, ladies and gentlemen. The issue is if you do so with all your heart and all your soul, but wait a second though, we saw that also in other passages. Well, then how does that help us to understand what was being spoken of in Romans 3, where he says that there are none that seek him? Well, let's go back to that passage in Romans 3. Notice what he says. First of all, he says, speaking about, and this is why I brought the issue about Jews and Greek and so forth and kind of contrasting. He says this, there is none righteous, nobody, not even one. There is none who understands. And I want you to notice the tense of the words or the verbs that are used here. This is in the participle form. So you'll see that there are none who are understanding. There is none who seeks and the word that's used here is this is a present active participle. So in other words, what he's saying is not that there are none who seek or none that have sought, he uses the present active participle to say that there are none who are seeking on a continual basis. This is why I use kind of the analogy of the car. Going after the car, you're not going to keep doing that. There are those in the Old Testament who might seek for a while, but did not continue to seek after him. So is it possible momentarily to seek after the Lord? Yes, it is. Is it possible to continue seeking? Ah, therein lies the problem. And when you look at the Bible, when you see how the Bible describes us as believers, then you'll kind of get an understanding of what's being stated here. Is it possible? Here's another question. Is it possible to believe without the Lord having anything to do with it? Is it possible for the Lord, is it possible for you to believe without the Lord interfering? Is it possible to believe outside of the Holy Spirit working in you? Well, the answer is yes and no. It depends on what you mean by belief. Remember, the Bible describes us as not just a temporary belief, but believing in one of our most famous passages. As a matter of fact, the most famous passage in the Bible, John 3.16. That's what he says. For God so loved the world, you can probably repeat it without even sitting on the screen.  I think that's a wonderful passage. But notice what it says here. The believe. The word that's used here is piste duan. This is a present active part of it. This is the one that is believing. Notice this. Whenever you read the Bible, whenever you see the word believe, almost always you're going to see the see the believer being referred as someone who is believing in a state of constant believing, continual belief. That's how we refer it. As a matter of fact, I want to do something real nerdy here for a second. And I want to go to Dr. Daniel Wallace, one of the foremost authorities on Greek grammar and notice what he talks about as it relates to us as believers. He says this in regards to, really this is kind of in picking up this piste duan. He says that a spectral force of the present piste duan, that's the one that's believing, the believing one, seems to be in contrast with the piste duas. That's the heiress tense. The heiress is used only eight times plus two in the long Indian Marks. So probably just six times. And then how you see those also matters because they have to be used in a couple of cases. There's no other tense that is proper to be used. But I'll leave that alone. He says the heiress is sometimes used to describe the believers as such as and thus has a generic force. For the clearest examples, Mark 16, 16 also, 2 Thessalonians, I'll let you go to those. But that's what he says. He says the present occurs six times as often, 43 times, most often in sotereological forms. In other words, when he talks about the believing ones, he's speaking about it in sotereological forms, meaning about us having salvation. He says thus it seems, here it is, that since the heiress participle was a live option to describe the believers, it is unlikely that when the present was used, it was aspectually flat. The present was the tense of choice, most likely because the New Testament writers by large saw continual belief as a necessary condition of salvation. Along these lines, it seems significant that the promise of salvation is almost always given to the hapistuan. Now, why is that important? Because you can describe the believer in a generic way, with an aspectually flat way, meaning that they believed. And sometimes it comes across when you read the English, just believe. No, be believing. In order to be saved, you have to be believing in a continual state. Now, he's not necessarily addressing the issue of will a Christian be continued believing. He's just saying that a Christian is one that is believing, continuing believing, not stopping, which is why when someone says you have to remain, that's fine, have no problem with that. You have to remain in him. You have to keep believing. You have to keep following. You have to keep trusting. All of those things, sure, all of those things you have to have, you have to be in the ING sense. Bad English, but you get what I'm saying. You have to be believing. You have to be following. You have to be hearing. Now, the issue of it, will you do so? That's a different matter. But that's why Jesus says that of the believing ones, John 3.16, in order that all the believing ones are pissed you on, not that those who believed, but the believing ones that those that are continuing believing, that's the one that shall have eternal life. Now, the issue is this though, is it possible to believe temporarily and then stop believing? And if so, is that the belief that God is speaking of about salvation? Well, no, because we look to Luke chapter 8. He's speaking about the parable of the seed and the soils, and he speaks about those who heard the word, which is a seed, and the word fell on bad soil, which is their heart. He says chapter 8, start in verse, let's start in verse 12, those beside the road are those who have heard, then the devil comes and takes where the word from their heart so that they will not believe. And this word believe is so that they will not be believing. This is the participle. This is actually, excuse me, an Eris active participle, which means that they is still the state of believing. But they notice verse 13, those on the rocky soil are those who when they hear, received the word with joy, and these have no firm root. They believe, not the participle believe, believing, they believe for a while, and in time of temptation fall away. So it's possible to believe for a while or have temporary belief and then fall away, but this is not the belief that he equates with the saving belief. No, because it depends on the heart. Now we can kind of understand where the writer of, or Paul is speaking of in Romans 3. Those that are saved are those that are believing. Those that are saved are those that are seeking. You cannot seek on your own outside the Holy Spirit. So yes, you could seek temporarily, just like, yes, you can believe temporarily. That's not saving you, though. What must be done is a continual state. And how that works about is if the heart is changed. And so the only way to bat English here to be believing or to be seeking, that's why he says, let's go back to the Old Testament.  Do the right thing.  Well, how does that happen? The heart has to be changed. And the only way that that's done as far as we're concerned is the Holy Spirit must do something to the heart. He comes in and now our heart is born from above. We have a regenerated heart. And then we are constantly in a state of constantly seeking him in a state of constantly believing him in a state of constantly following him in a state of constantly hearing him. The ING is important. So is there anyone that seek temporarily? Sure, all the time. That's just not the word that he uses in Romans 3. He uses the word as there anyone that is seeking. There's none that are seeking except when we have the Spirit in us when our hearts have been regenerated. Then we become like those in the Old Testament and the new that we become those that are seeking. Those that are following those that are hearing those that are believing. Amen.


Right, so I guess during the past few days you've learned a lot how to construct gene lists from your experimental data. The learning objectives of this module are the following. Is this going without my intervention? Yes, I can't speak that fast. That's better. So, by the end of this module, you should be able to identify situations and places where you can use pathway enrichment analysis or pathway network analysis. You should be able to understand the main components of the simplest pathway analysis, these being gene lists from your experiments and then pathways that are also often represented as gene lists. One major hurdle, which is a bit boring but you have to do it nevertheless, is to manage all these different gene IDs and names and symbols and so on. We have a quick look at that and then we go through how those gene lists corresponding to pathways and networks are actually created or where do they come from. So the premise is the following. You've performed just your new super cool screen that nobody has even heard of or maybe you sequenced a lot of genomes or you did some RNA sequencing in order to understand transcriptomics and then that screen or experiment produces a hundred or a thousand genes and your PI or your collaborators tell you, so what is interesting about these hundreds or thousands of genes, how did they fit our experiment or why should we care about those hundreds or thousands of genes. And then probably the first thing that you want to do is you want to go to the PubMed database to see existing literature about those genes, which is fine if you have a couple of genes but you don't want to do that systematically when you have hundreds of thousands of genes because it will take so much of your time. So generating gene lists depends on what type of data you're looking at. It may be proteomics data or sequencing data and the techniques are different to extract gene lists from those different sources of data. However some techniques can be the same such as ranking or filtering or clustering. They all end up in lists of genes. And all the pathway network analysis is how to go forward from these lists of genes back into the biological functions or processes that are involved. So you can use a wide variety of analysis tools that are publicly available to perform pathway enrichment analysis to understand the gene lists and to drill down to some biological mechanism and maybe if you're lucky you find something new and exciting about the biology. Pathway enrichment analysis will help you to find candidate genes to follow up with. You explain some causes of disease, understand some biological functions and so on. And as I mentioned, you don't want to do this manually because every gene has been, when many genes have been studied very comprehensively, there can be hundreds of papers available for any particular gene and instead of curating the literature manually, pathway enrichment analysis or network analysis will provide a shortcut to analyzing gene function because much of this information has been systematically annotated into databases and you can access these databases using statistical means rather than going through papers one by one. Pathway analysis and network analysis may provide a missing link between interpreting the genotype and phenotype, explaining phenotypes using genotype information. So on the one hand, you can collect information from the genomes through, for example, whole genome sequencing or exome sequencing. On the other hand, you have some sort of phenotypic information. Maybe this is proteomics. Maybe this is patient survival curves from a clinical study. Maybe this is inheritance trees. Now pathways are the complex component in the middle, which actually synthesizes knowledge from previous experiments, multiple different databases, literature and the expert curation. And you can predict the effects of variation in the genome on the pathways and try to explain your phenotypic observations. So a bit more formally, pathway and network analysis is generally any computational or statistical technique that takes advantage of network and pathway information from previous studies or previous literature. Most commonly it is applied to interpret lists of genes. The most popular type is pathway enrichment analysis, but there are many other more complex types covered during this workshop. And it helps gain mechanistic insights into omics data. So omics data could come from diverse sources, but generally if it boils down to a list of genes, many pathway and network tools are available to understand those data. So there's a bit of a difference between pathways and networks. I'll try to give my perspective into it and every other person you talk to that will have their own understanding. So my understanding is the following. Pathways and networks both include systems of genes or proteins or molecules, transcripts and so on. And there are systems in the sense that there are interactions between those components. So genes or proteins interact with one another. But there are also somewhat, there's also some differences between pathways and networks. Pathways often being called small scale systems that are fairly well described. They may be a consensus synthesis of many years of knowledge and research. The interactions in pathways are often biochemical reactions. So they're very detailed, there's a lot of knowledge about them. They're collected into a specific pathway databases. Maybe they contain dozens of genes and proteins, maybe hundreds, like say a few hundred, but no more. Networks on the other hand are usually a large scale, they're more noisy. They contain perhaps hidden information that's not well characterized in pathway databases. There are simply their abstractions of simplified cellular logic. The edges in the pathway in the networks are not that clear what they often mean. Maybe they're physical interactions, maybe they're genetic interactions, maybe they're some sort of activating or inhibitory patterns. But they may contain stuff that we don't really know about yet. Networks often are derived from high throughput experiments or maybe even statistical integration of multiple types of data. So network data may be available for all genes in the genome, pathway data is available for a much smaller fraction. So here's a busy slide trying to summarize multiple types of pathway network of an analysis in a sort of a simple way. There are several, there are many, many different techniques and you can summarize these perhaps in three different categories. The first one which is the focus of this seminar is the enrichment of fixed gene sets. So this is shown here. And this is basically identification of pre-built pathways in your gene list of interest that are somehow either significantly enriched or significantly modified, but this is based on existing information about pathways from the literature. Now the second type of analysis is called de novo subnetwork construction and clustering. So this refers to still your gene list of interest from your experiment, but instead of looking at predefined pathways, we look at large-scale predefined networks and then try to extract some certain areas of the networks which connect your list of genes. And then the third most complex type of analysis is called pathway or network-based modeling in which we collect some pathway rules of activation inhibition, for example, from pathway databases and then we see if our gene list is consistent with these rules or maybe instead our gene list represents some new unrecognized pathway rules. So I've been looking a lot into cancer genomics and perhaps an explanation of when you analyze cancer genomes, then these different types of analysis try to answer the following questions. So the first analysis of fixed gene set enrichment tries to tell you what types of biological processes are represented or enriched or altered in this particular cancer type. So the second type of analysis tries to figure out whether there are new pathways or new associations that are relevant to this particular cancer and maybe there are some clinical subtypes where different pathways are activated or inhibited. And the third most complex modeling which evaluates those pathway rules sees if there are pathway activities altered in a particular patient and maybe there are some drug-targetable pathways available in this patient because we see that some rules, some well-known pathway rules are broken in that particular patient. So in this particular lecture we're mostly looking at the enrichment of fixed gene sets because this is most applicable, you don't need a lot of detailed data to it and you can apply to all kinds of different experimental data sets. So there are multiple benefits of analyzing data on the level of pathways rather than on the level of the individual genes. So the first idea is improved statistical power. So for example when you look at gene expression data then you're probably focusing on say 20,000 human genes or maybe 6,000 yeast genes. When you do pathway enrichment analysis you can probably focus on say 2,000 pathways or maybe 500 pathways which is much smaller number and therefore you do less statistical tests and you need to apply the multiple testing correction in the less strict manner. So you will have more chance of finding biologically significant results if these are present in your data. So viewer test is generally good if you're doing high throughput analysis. Also pathway data may be more reproducible. So for any particular sample you may be seeing some genes being up-regulated, some genes being down-regulated in the gene expression data set. If you're looking at the level of pathways then you're analyzing many components of the pathway at the same time. So if you encounter a new set of samples and the biology really involves that pathway you may see the pathway over again. Because maybe different components are ordered but it's still the same pathway so that increases your reproducibility. You're basically doing a set of tests again for all the pathways when analyzing your restricted set of genes. Right? Every single gene. So it will increase power by that speed. Yeah. Although I do understand the question and I agree, in such a set up you apply multiple testing twice. First for all your genes to filter out your gene list and then for all the pathways. So in a sense, yes, you're testing totally or testing more. But when you compare it to say testing your individual genes and your gene list being the final result versus your pathway list being your final result then you're still encountering better power. Yes. So where was I? I think the next point is about easier interpretation. And this is more comfortable because when you're looking at pathway data you can use concepts from cell and molecular biology in order to explain your data. For example you can say that in my experiment cell cycle genes are up-regulated and that's much easier to understand than dealing with an alphabet soup of different symbols being up-regulated, down-regulated and modified. And then pathway enrichment analysis also allows you to get a step closer to the mechanism because by conducting that type of analysis you'll be able to identify processes that may be responsible for the changes you see in your experiment. And then you can also use guilt by association or birds of a feather principle in order to predict new functions to your genes. For example if you see a large enrichment of a particular pathway in your gene list that you derived from an experiment and then there's a few other genes that are not well known to get associated to that pathway you may be able to predict that maybe these new uncharacterized genes also have a role in that process or pathway. And that can point you to some pretty detailed experiments what to do next to maybe find out about the role of those genes. I'm sure that this before analysis has been covered well in the previous lectures but I still need to emphasize the importance of it because pathway analysis is sensitive to the garbage in, garbage out principle. So if you haven't performed high quality analysis of your input data or the experimental data pathway analysis may reveal stuff that looks really interesting but is actually an artifact or a technical or biological artifact of the way the data were processed. So it's important to normalize your data, perform background adjustment for microarrays or proper sequence alignment for RNA-seq. It's important to look at quality control, make sure that the samples you're analyzing are really the samples you're analyzing not due to some say sample mislabeling. You need to use specific statistical tests that depend on which type of data you're looking at whether it's count data or whether it's normally distributive intensity data. Genes list size also matters. Okay, is it bad in the back? Is it better now? One, two, no? I'll try to speak up. So gene set size also matters. Personally I find that there's a sweet spot between say up to beginning from low hundreds to a thousand. If your gene list is in the thousands then maybe something's wrong then you need to look at the gene list in a different way. Lists, very small lists like dozens of genes or a dozen of genes doesn't often result in high quality pathway analysis and so on. And gene IDs need to be comfortable with the software. This is a big problem because there are so many gene IDs and there are more and more coming out every day. Here's a quick overview of how pathway analysis looks like.  And the gene list is the main input to pathway analysis where you use statistical techniques in order to distinguish which pathways are important regarding your gene list. You can use visualization techniques in order to summarize these pathways in a smart way. Then you can focus on particular pathways and then try to drill down to the mechanism, link them back to the genes, understand what's known but then you don't need to do hundreds of PubMed queries but maybe dozens or even less. And you can use pathway analysis in order to give you further follow-up experiments and then hopefully you'll be able to publish this really well. It depends. So what is the reasonable size of a gene list? I would say if you have, say, 20 genes, then you're likely that the pathway analysis won't highlight a lot. If you have a large gene list, say, hundreds to thousands, then you need to have a rank to that. So if you just have a gene list with 5,000 genes, it's probably not going to highlight anything in the pathway analysis unless you allow it to be ranked. So the first genes would be more important, the following ones a little less important and then you use a prioritized ranked list analysis. So if you just compare one list to another, then probably, like, say, low hundreds would be around the sweet spot. So from a statistical viewpoint, the simplest type of pathway enrichment analysis is a comparison of two sets of genes conveniently shown with this Venn diagram. So on the one hand you have a list of genes from your experiment.  And on the other hand, you have another list of genes and potentially many list of genes that correspond to public knowledge about gene function. So for example, that could be all the genes that are known to be involved in neurotransmittal signaling. And then you compare those two gene lists with a statistical test, such as the Fisher's Exact Test, or a test based on the hypergeometric distribution, to determine whether there are more annotations or more neurotransmitter function in the genes in your gene list than would be expected by random chance. And then you choose a pre-defined cutoff. Very often this pre-defined cutoff is 5% p-value. And then if you see that there are more than expected number of neurotransmitter genes, then you propose a hypothesis that may be drug-sensitivity in brain cancer has something to do with a reduced neurotransmitter signaling. You perform this analysis over all your pathways and then apply multiple testing correction and anything that has a significant p-value after this multiple testing correction will be deemed significant and will be added to your potential pot of hypothesis. There are many tools that perform this type of basic analysis of gene lists. One of them is GProfiler that I developed during my PhD. This is a typical output of GProfiler where you have enriched pathways from top to bottom and then your input gene list is shown from left to right. And then there's a certain amount of information that's being displayed. These are the pathways that were found. There are some numbers involved, how large is the pathway, how large is the gene set, and how many genes are in common. There's a p-value to it and then there's a matrix of annotations showing which genes are associated to which pathway. I'll be talking about that tool a little more during the next lectures. So this is an excellent way of summarizing public literature and public databases and public knowledge in order to characterize gene function. So instead of going through all these papers, you have a kind of gene set approach to storing pathway information. However, this can quickly grow overwhelming. So you have this well-performed experiment that gives you all these neat pathways. And then you get this massive list of highly significant pathways that characterize your experimental gene list. And this is for multiple reasons. There's a lot of redundancy in gene function biologically, so genes do multiple things. And on the other hand, there's a lot of redundancy in how people store biological data and databases. So part of that is because functions, biological functions are different, but it's still quite similar and then genes are annotated to those functions in a redundant way. So instead of staring at these tables endlessly in, say, Excel spreadsheets, we can use visualization techniques that allow you to compress all these highly similar but somewhat different pathways into network-related maps. So this technique is called the enrichment map. And this is a network of pathways, so to say. So each node here in this network represents a gene set that's a pathway or a process. And these pathways or processes are linked to one another if they share large numbers of genes. So if they're similar in a particular way. And when we apply network visualization techniques, then we can group together those groups of pathways or processes that share many genes. And then instead of looking at the list or a spreadsheet of all these pathways, we can look at the network and visually or manually identify major functional themes that are present in your gene list. So this is a great technique to do after you've done your pathway enrichment analysis and you want to know how to summarize the results. There's a bit of a longer motivating example how to characterize the genetics of autism spectrum disorder. This was a paper where our lab collaborated in the analysis. So autism is an autism spectrum disorder, so ASD, it's a highly inheritable disorder. In monosagoric twins, the genetics, they share a diagnosis in 60 to 90 percent of cases depending on the stringency of the diagnosis. About 5 to 15 percent are known to be single gene disorders and chromosomal arrangements. Yes? This one? What is the edge? Yes. So the edge means that one pathway and the other pathway, they share a large number of genes. Yes. So that pulls together the pathways that are somewhat similar because they involve similar genes. The edge definitions, we will also go through that later. You can set the way the edge is defined, whether it's like 30 percent or 50 percent genes shared and the higher the percentage, the more granular the map will become. So if you want to have a large map with many different, with small but large modules, then you define your map by a linear edge definition. If you want the stringent edge definition, you will have many different small functional themes that represent the biological pathways in your data. More of that will come during the last session of this tutorial. Any other questions on the previous slide? Yes. Exactly. So it's a network of pathways or a network of gene sets. So the larger nodes represent larger networks. Okay, I'll repeat all the questions. Right. So what was your question first? Right. The question is whether the way the networks are connected also represents redundancy. It does. Because when you see that, for example, the blue, big cluster here, it's quite likely connected and then there are these outliers forming almost another new cluster, which is not exactly related to the bigger one. That means that the genes shared within here, it's probably one big gene set that's shared among all these different pathways, and then it branches out to a smaller different pathway where the gene sets are less shared. Does that answer? It's a bit of magic because the moment you change the threshold of the edge definition, it will visually change a lot in the map. And it's quite likely, for example, that this cluster here will become its own cluster because the weak edges will be removed as you change the threshold. Right. So how to keep the threshold? In most cases, you try the default threshold first and then you see whether you are able to interpret the map right away. By interpreting, I mean you eyeball the cluster and see if you can assign a single functional theme to it. For example, can you say, this is the apoptosis cluster? If you cannot say that, then you probably need to fine-tune the way the weights are defined, the age weights are defined. For example, maybe it's partly an apoptosis cluster and partly a differentiation cluster, then that doesn't, you can't put a single label to it. Then you try to split them a little bit apart. Or in other cases, maybe there are so many different apoptosis clusters all over the place and then you reset the threshold for the edges and then they all will merge because they are all based on the same gene set. So is there a way to set different types of edges? Absolutely. But you need to tweak that a little bit more and it's all going through the cytoscape software so you can add more edges to it and relabel the edges and do a lot of things but it's like artistry. Any other questions? So why don't we, so about the example of how to generate the really fancy enrichment map, I guess the motivation to study autism is that not a lot is known about the genetics. There's about five to 15% of the cases that come from known gene alterations or copy number alterations and then there's also so-called de novo copy number variation that's not apparent to the parents but that has emerged in children. And in this particular study about 2,000 individuals were profiled for copy number variations, about 900 cases and 1100 controls of European origin using a SNP chip from Illumina and they produced some highly high quality, rare copy number variations with a high validation rate and it turned out that on average a person had two copy number variations with a median size of about 280 and many, well a good fraction of ASD individuals had at least one de novo copy number variation and the top 10 genes already were quite convincing because they were known to be related to copy number to autism spectrum disorders. And here's a pretty nice looking enrichment map with a lot of detailed information you can see and it greatly highlights all the different aspects of a neuro system development for example that are potentially altered by all these copy number variations. And this is a great leap from the standard enrichment map that you can produce because there's all these different additional visual aspects that reflect aspects of data. Yes, but it's not the default output of the plug-in so many, many other things have been added for example nodes of different shapes and sizes and we can zoom in into the cluster and then this gives you an idea what the enrichment map is supposed to do. On the one hand you call it a major functional theme such as the central neuro system development which represents this cluster but you can highlight individual members of this cluster in order to say well these are the specific pathways and networks that are part of this greater functional theme. So again instead of looking at the table of pathway output you visualize that as a network and then you can choose which information to highlight and how to summarize it and represent it as a figure. Do we look at the size of the base of the pages? So all of that information is important. So the question was what you should look at when you look at the enrichment map. The default enrichment map, the size of the node says how many genes in your experiment are related to that pathway and then the weight of the edge tells you how many genes are shared between two pathways that are connected. So that information already tells you something. On top of that for example you may be able to say which genes specifically are part of that pathway that's been there as a node but for that you need to do a little bit of manual look up in the cytoscape. I think that by default whether there is a score that allows you to rank or look at the pathway enrichment map to rank which ones are on the top. I think the brighter colors by default tell you the higher enriched pathways in the map. Any other questions? Okay, I'm moving on. So where do gene lists come from? And I'm sure that you guys have even a better idea than I do because everyone is working on their own experiments and they generate their gene list. It may seem that it's very different and difficult to analyze gene lists for different platforms and data sets and techniques but there are all these statistical ways of say ranking and clustering and network analysis that all produce lists of genes. So for the simplest type of pathway enrichment analysis you just work with a list of genes. No strings attached, a plain list of genes. However, many biological experiments give you naturally ordered lists. For example, when you analyze gene expression data some genes have a higher fold change relative to the control. They can be ranked first. You can also quantify these gene lists and for each gene attach a number to it and there are advanced pathway analysis techniques that take these numbers into account and then perform qualitative analysis or quantitative, sorry. And whatever type of data set you have you may be able to use some of the more well-known statistical techniques that are broadly applicable. For example, ranking, filtering, clustering, principal component analysis that all can produce some types of meaningful lists from your data. You can also look at already pre-existing data sets such as networks, protein-protein interaction networks, microRNA, target gene interactions, transcription factor binding sites. All these data sets provide meaningful ways to analyze to create lists and analyze them in a pathway context. You can study a genetic screen such as a knockout library of all yeast mutants of genes and then plenty of information can be extracted from genome-wide association studies. So what do gene lists mean? This is probably one of the major topics of this lecture. Gene lists could mean a biological system that's been altered or affected by your experiment or maybe a biological process that's affected in a group of deceased individuals versus controls. A gene list could mean genes with similar functions such as transcription factors or kinases that all become activated or deactivated due to some condition. Gene lists could mean genes apparent in a particular cellular location such as all nuclear proteins. Or they could mean all the genes that are co-located in a particular chromosomal region maybe due to a copy number variation. And then all the biological questions that you want to answer when analyzing things with pathways. First of all, you want to understand what you want to achieve with your biological experiment. And this is hopefully even before you go and do the experiment.  What pathways are there in deceased individuals that are not there in healthy controls? Maybe you want to find a controller for a process and that's also part of a pathway analysis. Maybe a microRNA, maybe a transcription factor. You can use the birth of a further principle to assign functions to genes and perform validation experiments or you may want to prioritize genes for those. In the first lecture, I will cover pathway enrichment analysis, just comparison of gene sets. But further, we can also do network analysis to pick gene function or maybe find new interactors for a specific list of genes or regulatory network analysis where we find transcription factor binding sites, for example. So as I mentioned, there are two major inputs to pathway enrichment analysis. One of them is the list of genes and one of them is a group of gene sets corresponding to pathways. The first topic will be dealing with gene identifiers. There are so many different gene identifiers and it's often kind of overwhelming to deal with them. And ideal identifier is a unique, stable identifier that links a particular gene or a protein of interest. By unique, I mean that it hasn't been used for a different identifier at a different time. And gene and protein information is stored in many databases and many databases focus on their particular aspects. For example, Uniprot is clearly a protein database and they don't really provide information about genes. And therefore, as there are many databases, each one of them has their own type of ID and it becomes difficult to convert between those two, between them in general. And it's important to understand about what different databases are about. For example, Andre gene doesn't really store the sequence of that gene. It just provides a pointer to a sequence database. And as these pointers change, you can also see how things change and the protein or gene IDs are not stable over time. So here's a few common identifiers that are used. It's a long list, but believe me, the list is even much longer. There are certain IDs that belong to genes. Others that are focusing on RNAs. Others yet that are focusing on proteins. And then there are all these species-specific databases that have their own IDs. For example, either the human genome database or mouse or rat or so on. And then there are yet other types of IDs that relate to experimental platform, for example, affymetrics or Illumina or so on. The problem is that most software tools only support their limited lists of IDs and others are not supported. And then you need to map your gene list or ID list to a standard ID list. And the main use is finding your favorite genes and locating other resources that are available for analyzing genes. You need to translate things, even the biological sense. Sometimes you need to translate things from probe set IDs and microarrays to protein IDs in order to perform interaction network analysis. And you need to merge data from different sources. You need a common reference. So the problem with many IDs is that there are one to many. So one ID could be many actual proteins. There are ambiguities, often coming from historical reasons. For example, TP53, a known cancer gene, has all these different symbols in different organisms and different platforms. So you need to, most cases, you need to use one standard symbol that's widely acknowledged. Then if you're using Excel, then this is a notorious case where the stem cell regulator oct4 is converted to october4, which you don't really want. So you need to paste and copy and paste this list carefully. And there are always problems reaching 100% coverage because things change over time. Here's a cautionary example where a particular nature paper was retracted because they thought they were analyzing one gene, but it turned out to be another gene that was regulated by a microRNA in their experiment. So be really careful about those types of situations. Here's an ID mapping service provided by the G profiler tool. It's pretty straightforward. It's based on the Ensembl Biomart database that gives us all these indexes between converting IDs. And all you need to do is insert the gene list. It can be a mixed set of different IDs. And select the type of output that you wish. There's a long list of them. This is for human. And you'll get back a table. And when you go to Ensembl Biomart, you can actually download all these comprehensive lists of translation tables. It's really useful. So as I said, the question is about losing genes while converting between a rat and human. So one question is, if you were using that particular toolset, then I wonder whether you'd want to So if you're doing all ortholog conversions, then I assume it's expected that some genes are lost. But I would say I need to understand more about the problem so maybe we can talk about it offline. Yeah, so for example, you can have a gene list coming from your microarrays. And some of the map identifiers won't be mappable by any tool at all. Because some they will either be missed or there will be some aliases that are ambiguous. And then in order to get rid of those missing aliases, you may need to do some manual work. For example, go check that aliase is in multiple different gene databases and make sure that you understand to which gene it's actually pointing to. So the example with the nature paper, things like that happened where exactly the same symbol has been used for completely different genes and different chromosomes with different functions and so on. So this work will probably never be precisely done by any computational tool. OK, so on top of that ambiguous mapping, there's now in G-profile, there's a dialogue that allows you to manually check if there's ambiguity which gene to choose to proceed with. So a couple of recommendations, how to deal with these IDs, one of them being focused on a particular well-identified or well-acknowledged set of IDs such as Andre G's IDs or official gene symbols. And when you really want 100% coverage, use a spreadsheet, make sure that the copy paste works well so you don't get October 4. Then you need to perhaps look at the multiple databases such as gene cards, ensembles, species databases in order to make sure that your missing symbols are mapped correctly. And I mentioned the Excel thing already. So to summarize this part quickly, many IDs have been invented for different genes and proteins and molecules and so on. And when you do any kind of genomics analysis, you may need to convert from one to another repeatedly. And this is a task that all bioinformaticians sort of despise. But it's required to do, and not always, the software tools will do all the work for you. And they use common IDs such as symbols in order to do your mapping. So meanwhile, I heard that there was a question about defining a pathway. In the context of this lecture, I would like to define a pathway as a list of genes that has been previously annotated to a particular function. So in this case, pathway doesn't involve the interactions between the genes. But we're talking about a set of genes such as these are all neurotransmitter genes. Let's call that the neurotransmitter pathway. In further lectures, people will talk about how to consider pathways when there are interactions between. So one pathway member regulates another pathway member. In this particular gene set, enrichment analysis is a pathway, is a list of genes that's associated with a common function. So the main purpose of analyzing pathways is that we don't need to go back to the literature to understand primary research that was performed to understand gene function. But instead, we can go to a pathway where all that information has been stored, gene by gene, function by function, and then analyze that using computational techniques to better highlight the biology in our experiment. Pathways are mostly collected into several resources. The more famous one is called the genontology, which we will focus on mostly during this presentation. Genontology has biological processes, cell components, molecular function stored in it. There are many other pathway databases, about 500, that store information about pathways. For example, reactome and keg are commonly used pathways for multiple species. And then there are all these other annotations, such as so chromosomal positions, or protein domains, or disease associations, and so on and so on. All of that can be used systematically in order to perform gene set enrichment analysis. In most common cases, people look at the genontology and perhaps a few pathway databases. So the genontology is like a dictionary. It's like a book when you open up and you see definitions of different terms. It's a dictionary for biological phrases, such as a protein kinases, which is a type of a protein, or apoptosis, which is a type of a process, or membrane, which is a type of a cell component. So genontology is a dictionary of terms, but it also has terms and definitions. It's a formal system for describing knowledge. And it's also like a hierarchy. So there's a system how those different terms are arranged between one another. So this is an example of the hierarchy. For example, we can talk about B cell apoptosis, which is a type of an apoptosis, which is a type of cell death, which is a biological process. So all these terms or ideas are arranged in the ontology in a hierarchical manner, using relationships such as part of or is a. And it describes multiple levels of detail of processes and pathways. So this is where the redundancy comes in. All these terms can have multiple parents or multiple children. So it's a structured way of representing knowledge. So genontology covers three major trees or three major structures, one of them representing cell components, the other molecular functions, and then biological processes. The last one is usually most convenient for analyzing your experimental lists for biological pathways and processes. So where do go terms come from? They are added by editors at the European Bioinformatics Institute in collaboration with multiple groups who are annotating genome data. Terms are added by requests, and experts help with those major developments. And here's a quick graph showing that go is a live and evolving structure of knowledge. For example, between now and a few years back, it has grown 16%. So 16% more new terms were added to the structure. Another part of go is the part of annotations, where genes are linked or associated with go terms as the scientific knowledge improves. And these are known as gene annotations or gene associations. And importantly, every gene can have multiple annotations. And some of those are created by manual curation by experts, and others are created automatically. So you can already see that there's a difference in quality how these annotations are created. Here's an example how genes and proteins get annotated to the tree. And this is important to understand when you look at pathway enrichment analysis, because you see a lot of overlapping processes and things coming out. For example, there's a kinase called Aurora kinase B that is known to be part of B cell apoptosis. And then in the process, it is actually automatically added to all the parent terms of that process. So it's not only associated to B cell apoptosis, it's associated to apoptosis in general, as well as cell death, as well as death, as well as biological process. So it's sort of growing up in the tree annotations. Annotation sources? Yeah? Yes. So the question is whether this hierarchical annotation up to the top of the tree is also used in pathway enrichment analysis? And yes, that's correct. So this is where the redundancy comes from. This is why you always see not one cell cycle process enriched in your gene set, but dozens. Because all these terms in the bottom of the hierarchy are basically the same thing with subtle differences. And they get annotated up the tree so they become more and more general, but also more and more redundant. I'm sorry, I didn't get that. You can. Yes, you can find out which go term is associated primarily. In most cases, you need to go to the original table to look it up. So the question is how large can a go category B? And it can be for a single gene if you manage to convince the go editor that it's really novel and it has to be there and so on. And then in the future, more genes will be added. In the pathway enrichment step, you can actually set filters of how large gene sets you want to look at. And that helps you to better fine tune the results. So annotation sources are diverse. They're basically based on what types of experiments were used in order to link that gene to that function. And manual annotations are curated by scientists. But the manual annotations are unfortunately a minority because it takes a lot of time to curate bio-primary literature. And then instead, the go team also uses reviewed computation analysis where they run algorithms and they validate that the algorithms are fine. But there's also a fraction of go annotations that are made purely electronically by downloading data from other databases or by annotating gene functions from different species. And then that is the part of go that is less annotated, less taken care of, and also probably a little bit more noisy. So when conducting pathway enrichment analysis or any other pathway analysis, you should be aware of the type of annotations that were used in order to link genes and pathways. And just that helps you to also validate and prioritize your findings. So there is an indicator in some tools, including G-profile that we cover, what type of evidence was given in the annotation process. So go itself maintains all these evidence types that you can see here. Here are some experimental evidence types, such as mutant phenotype. Here are some computational evidence types, such as sequence similarity. Then there are author statements, such as author X said that gene X is related to process Y, and so on. And then there's the electronic annotation format. In G-profile, we use colors in order to indicate which type of evidence was used. And then the more red tones represent more stronger experimental evidence. And then the blue-artones represent electronic evidence. So you can also just quickly look what is the type of evidence that was used in order to infer the pathways for the gene list. Go covers a lot of species. And the species information about the species is initiated by species-specific databases. There's a lot of information about human, a lot of information about model organisms. There are some bacterial and parasite species, from tiger and so on. And then the ensemble database takes good care of annotating further species, for which they have genome sequences available to have even more data from go. Pardon? So the TP53 in rat is a TRP53, I believe. So yes. No. So the question is whether, when you do species-specific analysis, whether you have to convert manually between IDs from human to other species, no, you don't. But you may have to take care that you're not giving the wrong input yourself. So at least in G-profiler, all the IDs are mapped. Right. I would say that the question is about IDs and capital letters. I would say many tools convert the input IDs to capital letters anyway, so you shouldn't worry about capital letters. Yes, you're right. Yes. So the question is what do you do when you have several names for the same gene? You should choose a standard one. For human, I'm sure there's a list of genes that are called standard, so the nomenclature, you should always refer to that. Yes. Right. So maybe we can continue the discussion in one of the next sessions because I really need to finish this deck before you go on a break in 15 minutes. Yes. That will be a practical part where you can go and test these gene ID tools. So to get back to the point how a lot of that information is experimental, but much, much more is still computational, here's a chart that shows you how different species, how many annotations are available and what fraction of them are actually experimental and what fraction of them are derived from bi-computational means. So you can see that most species, while all of them actually have a majority of the information coming from computational predictions, which is fine because there are certain types of experiments that you can never do for human, but you can infer them from mouse, for example. There are many contributing databases, there are species-specific databases, and then there are these large-scale databases such as Ensemble that try to incorporate information from many species. And there's a type of gene ontology called the ghost limb that attempts to minimize and trim the entire GO tree for some certain uses where there's clearly too much information, for example. If you have this complex tree of different terms, it's very difficult to draw a pie chart of things represented in your gene set, but it's more feasible to do when you look at the ghost limb set, while the vast majority of detail has been pruned in order to provide a simplified version of annotations. There are many software tools allowing you to analyze GO. GO itself and all the associated tools are publicly available for people to use without restrictions, and then many other groups have created gene ontology analysis tools for various tasks. So, to access GO, there are many tools. Some of them are developed by the GO Consortium. Here's one, it's called QuickGo. You can go look up your terms of interest in the structure. You can visualize this tree. You can look at different associations that are part of the tree, and you can look up individual proteins and their functions in the tree. GO is not the only ontology that uses systematic representation of biological knowledge. Here's a cell type ontology that represents different types of cells and their organization. And besides GO, you have all these pathway databases. There are more than 500 of them, so going through them one by one is not very optimal. There's a database called Pathway Commons, which is a meta database of databases, and it merges the major pathway databases in a smart way. And then besides pathways, you can use any type of gene annotations in order to perform pathway enrichment analysis. For example, you can use protein domains or transcription factor binding sites or chromosomal positions, depending on really your application what you're interested in.  Ensembled biomarkers are a really convenient way of extracting information from Ensembled. You can get similar type of features from Entregene, and if you're looking at the particular model organism, you may find that model organism database your best source of knowledge. Here's a quick overview of how to browse the Ensembled biomarker. It has a pretty neat user interface. It's updated every three months or so. Here you can just first select the genome that you're interested in and the set of annotations for that genome. You can select from a large variety of filters in order to determine which genes you're interested in and then select attributes you wish to download, that they could be all types of attributes, including protein level features or regulatory features or sequences or whatever. Summarize the points of the second part of pathway analysis inputs. Pathways and other gene attributes are available in different databases. Genontology is one of the major ones that people use, but there are also many databases for pathways. Some of them are species specific. Genontology has a wide coverage of different species. It is the genontology classification system and dictionary for biological concepts. So there are two parts. It's the dictionary, so biological terms, and then the annotations to these terms associations with different genes. Keep in mind that every gene can have multiple annotations, and it will have multiple annotations due to the hierarchical rule of more general and more specific terms. Some genomes are more annotated than others, and some annotations are better quality than others. And for particular reasons, you may look at the Go Slim data set that provides simplified structure of Go. Here's a small overview of the pathway enrichment analysis diagram, which starts with a raw data, goes to a gene list, looks at pathways in a statistical way, and then looks at pathways in a visual way. You can choose some of those pathways to drill down to mechanism, bring them back to the genes, and then perhaps perform validation experiments and publish an experimental paper. However, no diagram like that is always as simple as it seems. There's way more to it. You can analyze pathways just by gene set analysis, but you can also look at interactions within pathways. You can look at, instead of small-scale pathways or gene sets of a common function, you can look at large-scale interaction networks such as protein-protein interaction networks covering all proteins. And there's more to it, so a lot of this will be repeated over the coming lectures and a lot of new information will be added as well. So maybe we can continue all the discussions that we had, and I'm a bit early.


What's going on, everyone? Sam here, late in sports cards, ripping 2022, Panini Mosaic, no football, no huddle, five box break, number 12, random team style. So look again at your random results. As always, you can find these on the website under the results tab, as well as the link in chat. To be fair, to be fair, the only reason we have cherry pepsi is because we got pizza and it came with a two-liter and Kenzie was the one who just decided what soda we got. So I didn't have a hand in selecting the soda. That was Kenzie. She did say it was going to be Dr. Pepper. Oh really? Yeah, and then when I see the cherry pepsi I'm like, oh, what's going on? It's not Dr. Pepper. Oh, there's plenty of excuses, big-time excuses. Oh, excuse central here. Hey, at least we got the soda I ordered. We would have never got anything. Oh, we would have gotten like sewer water. Yeah. Dye drinking that? Oh, god. I don't want to drink that anymore, then. Time to just go tell. I hope we got five minutes, four minutes to rip this entire break. Could have took that to a dark place by this time. Uh, Skyler Thompson Silver. That is, uh, Roger McCreary. Stair masters, Mack Jones. Uh, Xavier and Howard. Purple, Aiden Hutchinson. Nice one there. 14 out of 50 going to the lines. Grats on that lines. When I hear like the stair insert or the stair master insert, I just think of like them running up flights of stairs. Flights of stairs, yeah. I agree on that one. It's disturbing. Disrupting. Oh, Coke's fine. I'd rather have Coke too. Gabriel Davis.  That's like probably my favorite soda. Coke Cola is probably second. Coke's only good with a rum. Yes, also true. Ooh, diet, cherry, seven up. I remember seeing that in stores, Donald. That was really good. Justin Jeperson. Uh, it's a 50 on the purple. Yeah, cherry, seven up. That's pretty good. Yeah, cherry, seven up. Short Prince this year in mosaic is Glass Mosaic. Um, I forget the other ones. Razzle Dazzles, the new one. And there's one other one too. John Mechie, blue to 75. Forget what the other one is too. I don't remember. Oh, Cherry Vanilla, Dr. Pepper. So good. There's Razzle Dazzle, Glass Mosaic and something else. Randy Moss silver. Oh, busted, busted. That is Danny Grant. Busted is the other one. What is busted?  Oh, Storm Chaser, yeah. There's Glass Mosaic, Storm Chaser, Razzle Dazzle and busted. Those are all the like case hits. Patch Mahomes, blue to 75. Yeah, Genesis Retail only though, illegal. I like how Mosaic took elites inserts. So we're like, we're going to make it. Yeah, we're going to make Razzle Dazzle. That one's kind of not necessary. The other three are good. Wait, Storm Chasers are in absolute... Isn't there? TJ, I think Storm Chaser's in this. No, maybe not. There's a Storm Chaser or something in... Or is it... I might be thinking of Dark Horse. Dark Horse. I don't even remember Justin Simmons silver. One of them's in absolute. I just don't remember which one. I don't... The Sprinkle Parallel in Phoenix. That is going to be cool, Vaughn. There's a Sprinkle Parallel coming out in Phoenix. Marquez Valdes, Scantling. Nice MVS going to the Chiefs. Wait, what's the... I haven't seen this. I actually like Phoenix. Sean Alexander. I think it's like a new one that's coming out this year. Is it from Phoenix? Do they like the color of it? I don't know. It might be. No, there's Razzle Dazzle in this and there's Razzle Dazzle in Elite as well. Devontre Campbell, unless what I'm thinking of is a different... Unless I'm thinking Razzle Dazzle is a different like chrome product that came out recently. Yeah, it's in Elite, but it's in this too, I think. What's Storm Chasers in this for sure? Storm Chasers is in... I thought it was like another one in... Absolute's only got three. It's got Explosive Glass and Kaboom. Eric Dickerson, nice one there. 250. Oh, wow. Look at this. Certified. Which one? Is Storm Chasers? You see their Storm Chasers or Dark Horse? I don't think... Dark Horse, but that's just like an insert though. It's a good point though. I forgot about that one. There's like the Silver Presale and Christian Watson, nice one there. We've got Bailey Zappy going to the Patriots. Make sure you re-cap this, Zappy. Jared Gawd. You're already turning into Sean, dude. How does that make you feel? At least you feel like you're trash. Nice trash. Trash is for your... That's trash. Marshawn Lattimore to 75. Whoa, whoa, whoa, whoa, Snipes. Whoa, whoa, whoa, whoa, whoa, whoa, whoa. I just read that one again. Sauce Gardener, that's variation, right? Yep, variation for the Jets. That is AJ Turrell. I think we'd all turn into Sean Toploating for Sam multiple days a week. Very true. Now it's my full-time gig. Yay. There's Joe Montanamozé, blogger and server from Absolute. Silver Regi Wayne and Disco and Malik Willis, nice one there. True, fair, fair. An underarmored guy, so big time Raven's family. There we go. Kenny Pickett-Ricky. That is Mahomes, Montage, Disco, and Disco and David Montanamozé. A blue-worn moon to 75. Zitans. Rich Carter Laftis. There's a gold. Jelani Woods on the rookie to 10. Seven of 10 for the Colts. Congrats, Indy. Nice one there. Jelani Woods. Let me type it in. Somebody get this guy out of here. Let's go, Jack. Jack's a Ravens fan. Let's go. That's what we like to see. Let's go Ravens. Dalvin Cook. Disco. That's pretty cool, actually. That's awesome, Snipes. There's Silver Rondelemore and LT. You stink, dummy. I could walk out the door right now. I'm going to lose Donnie forever. I was going to be like, yep. Never top-loading. He's like, I want to go back to somebody who's shipping. He's like, he's going to close it. Diet Coke or Coke Zero? Coke Zero. Trey Lance to 75. I kind of agree with that, actually. I think that tastes more like true Coke. Ravens for me, neither. That's Kirk Cousins to 75. I'm in on Coke Zero. Yeah. No, regular Coke for sure, Brandon. I agree with that. Absolutely. Silver or Derek Henry? Eli Manning, Disco. Okay. Absolute has a insert called BISORM. Yes. Okay. That's what you're thinking? Yeah. Okay. That's what I was thinking. There is a Stormchaser's insert, like a case hit and something. Silver is Kenneth Walker and Nicole Bideen. Yup. Send everything. All-Card ship, Snipes. Unless it's like Bowman, then we usually don't ship the paper. That's like generally, I'd say like 98% of our breaks. It's all-Card ship. Bowman's 2% of our breaks. Michael Parsons and Robert Quinn. I honestly, probably more. Probably more than 2% of our breaks. That's what I was thinking. Yeah. But then there's, oh, another gold. Then there's like contenders.  There's Cooper Kopp on the gold. Nice one there. Nice. That is 7 of 10. Nice one there for the ramps. Ramps on the combo spot. Chad Dully. Not a triple crown this here, but... Here's 975 cards. Oh yeah. I had to do for personals a few nights ago, I had to do 16 Super Jumbo boxes. Can he pick it? Yeah. It took me like two and a half hours. Hey, we got a dome of olives. Tom Brady. Nice one there. What's up? We got a dome of olives. Thank you for staying for those like two extra boxes. Yeah. I'm thinking about it. Maybe I should back off next time. Next time's what it's called me trash. Why am I helping this guy? Why am I being nice? Tyler Algier. Juju on the purple. 250. Yeah. What's the like there? So I guess there's four case hits in mosaic. There's razzle dazzle, which is new this year. It's different from the elite one. It looks completely different. Let's debut. Two Jumbo chases. Oh yeah. Okay. James Cook. Disco. Laramie tonsil. Yeah. So there's what? There's razzle dazzle, storm chasers, busted, and what's it called? Oh, don't forget about honeycombs too. I mean, honeycombs. That looks pretty similar to the elite. I mean, I know it's so hard, but it looks pretty close. I know how much you want that jersey. True. Yeah. I mean, yeah, I know what you mean. Oh yeah. Choice Peacock. You know what I'm saying? We've got Damien Pierce, rookie auto going to the Texans. Nice one there. Houston. Congrats on that. And then know how to water. Remember why? Mark Andrews. I forgot all about Damien Pierce. Dude, I know. Already like. He's like one of the better rookies of the year. He was. He's like a theoretical rookie of the year candidate. Garrett Wilson. Come on, baby. Let's see a big Raven. Big Raven. Yeah. Pierce was great after contacts. Great after contacts. Dude, that's very true. John Harbaugh auto. Oh yeah. I mean, not John Harbaugh auto. Absolutely, dude. You get the Rex Ryan of prison. Yeah, right. Mac Jones, purple. The Patriots to 50. Another gold, dude. Wow. I love gold. Chase. Sam Howell. Wow. Brandon L with the commanders. 10 of 10. Nice one there. You can make this one. I was going to say, man. That's big, actually, right there. He's going to be the starter next year, bro. I don't know. Kind of keep. Probably will. I think that back. But Sam Howell probably the second. Probably might be there. I think they're going to have a battle for it. They might, honestly. But again, it depends what they do in the draft, too. Skymore and Justin Fields. They're probably not going to get a quarterback. I don't know, man. They probably won't. I mean, I might give this. How long? If I'm, well, assuming we're on a various days in Washington. That's why he's still there. I would almost give the reins to Howell and see what happens. Yeah. Trevor Lawrence on the mosaic. Like I would, because it's worse comes to worse.  Exactly. That it gives you an idea of like, okay, do we need to draft a quarterback? Yeah. We're going to have a good world. Introduction straight on Berks. There's Daniel Jones on the blue to 75 for the Giants. Color match. Silver, Roger McCreary. Titans. Mike Woods. I need to hunt Dawson to have a big year in this year. That'd be cool. For your, what's it called? Freshman. Freshman fabrics. Yeah. I ended up getting a year one. Kenneth Walker. Nice one there on the no huddle auto for the Seahawks. Congrats Seahawks. Ended up getting a year one luminance auto. Brad Johnson. Nice. So I got like a non-wanted Johan Dawson collection. What do you mean, Patrick? What number what? What number for what? The break maybe? Can you pick it? Justin Herbert. Nice one there. What's up, Hunter? I wouldn't be the worst idea, Donald. I wouldn't be the worst idea in the world. Rock party. And Cooper Cup and Travis ETN. Purple, Malik Willis. Nice one there for the Titans. 16 out of 50. You could throw this one in the recap. Very nice. For cousins, Disco. I almost feel like you have to, if you keep Lance, I feel like you have to start 30 though. Like as much as he has done this season, like it's coming in. Like it'd be hard for me to be like as a coach back, okay, we're going to give it to Lance, even though 30 came in as a rookie. One potentially two playoff games or a Super Bowl. And give it to Lance. Like that doesn't make any sense. Oh, it'd be, I mean, theoretic, like in a perfect world to be Peter Boulware, who played back in like the late 90s, early 2000s. He's number 58. Aaron Rodgers. Johan Dawson. Stroud would be solid. Stroud would be a, would be a good fit in what's it called in Indy. To David's question. Clint Portis. That is to 20. David to your question. I obviously would say Eagle Cowboys, but I know that's, like all time, I feel like that's pretty high. Yeah, that's a top one. Jordan Davis. And Patrick Mahomes. That's Sugg, so it's close. Both linebackers. Got Eli Mitchell. On the auto. No huddle. Going to the Niners. Nice one there. And Sean Watson. Jose. Randy Niners. Do you keep Lance? I think you have to start birdie. Yeah. Silver Marquis Brown. Disco, Joe Namath. I mean, yeah, I agree. He's still on a rookie contract. He doesn't cost you anything. Yeah, it costs you a lot less than Lance does too. Even on a rookie contract. Love it, Lance. Oh, yeah, yeah. True, true, true. True. That is Christian McCaffrey. And Disco Deck. Is he a free agent, Donald? Or is he still under contract? Groffalo. Zealand.  Donald's a big Minnesota guy. Oh, okay. My buddy's a Minnesota guy, so. Uh, nice. Kyle Hamilton, blue. On the rookie. 68 out of 75. There we go, Tracy. Grats on that. That's true too, Dario. That's true too. There we go. That's a nice one. Another blue coming up here. It's a W right there.  That's a nice, nice looking Hamilton too. That's like, that's like the rookie you want out of the Ravens class this year. And what else you got for Jobo? Yeah, Jobo and the Linderbaum and Charlie Kolar. That's Keechon Johnson. Hamilton's like good. Hamilton's a stud. Hamilton's a stud in that secondary. Because he doesn't have to do that much. That's like the big thing with Hamilton. Because that's secondary so good. Cooper cup purple now. That is to 50. Eli Mann. Hope you get all of your chance. I mean like previously he was able to sling it, but... I don't know, I think... Maybe give him some reps, I don't know. Where they, where they're drafting, I think I might. That is Brandon Ike and Drake London. They are a lot of worth it. Yeah, they're like top five or six. I think you got to go quarter back there. Three, yeah. All right guys, do a quick recap. We've got purple Malik Willis to 50. Eli Mitchell. Kenneth Walker. Damian Pierce. Bailey Zappi. Marquette's Valdez. Scantling. Sam Howe. 10 of 10 for the commanders. That'll do it for the break guys. Thanks again for the fill. Next up, choice.