 So we are going to start module five, which is going to use the gene mania application. So this module is called gene function projection. And this module was linked to a recorded video that had a whole the details about the theory about gene mania. So I hope that you could watch the video and you haven't, then you can still watch it tonight I suppose. So this video was pre-recorded by Craig Morris because we're going to use the software called gene mania as our, so yeah, here is some, oh, that's the copy. Okay. I thought it was the speaker. So the video is from Craig Morris because the software called gene mania was, has been developed a few years ago by Craig Morris in collaboration with Gary Bader. And gene mania is still maintained by the beta lab at the Donnelly Center. So I will do a brief summary of the theory, but the focus of the presentation as I did for the other modules would be on the applications of gene mania in our current project. So gene mania answer to type of questions as it has been explained in the recorded video. So two different type of questions. So one is just when you input one gene. So when you input one gene into gene mania, the question that you are trying to answer is what does my gene do? And you also can input like a few genes, 10 genes. And then your question that you want to answer with gene mania is give me more genes like this. So this is a summary of the keywords, some keywords defining gene mania. So gene mania is a functional interaction network. So I mean, today, the whole day, we are working with functional interaction networks, the same with the reactomify network. So nodes in the network are genes and they are connected by edges. So edges are the lines between the nodes if they are functionally associated. So for example, proteins may know to physically interact with each other or they can belong to the same biological pathway. So that's two ways to be connected functionally. And they can be also known to colloquialize in the same tissues, for example. So all functional networks may merge in gene mania as a global network that would gather all these different networks and their weights associated with them. So to predict the function of a gene, gene mania use the concept of built by association. And gene mania finds genes and networks associated with our genes of interest. So gene mania is available as a web app and a site escape app. So for the lab, we are going to start first with the site escape app. And then there is also the same lab with the web app if you want to try. And in the next slides, I'm going to present example where it's easier to use the web app when you just Google gene mania and then you enter a few genes or where you want to do more complex networks. And in this case, you use the site escape app. So just a summary of the theory. So here with the puzzle piece, I've represented the different networks that are used by gene mania to connect our genes. So it could be physical interaction, non-physical interaction between two proteins. It could be a predicted physical interaction, shared protein domain. So each protein have the same domain. So then we put a line between the two proteins, genetic interaction, collocalization or co-expression. And there is a weight associated with them. And then we construct a network. So each line is a connection from the network. And each different color comes from the different networks. So some tools, they would gather all the network and they would just put one line. That is the sum of the different networks. But gene mania, really, you keep the information of the source of the network. So that's why you have the different lines. So label propagation can be used to associate a gene with an unknown function. So here is a query gene, the gene with the unknown function to another set of genes. So here, this is an example of how it could work. So first we have small nodes and big nodes. So the large nodes, they are large in size because they are connected with each other. And this one is the query genes. And we want to guess the function of these genes. The red, let's say the red color would be a function. So we know that these two are a function A. But we don't know the function of this because these nodes here, like intermediate size, are related to the big red node. By this label propagation or heat diffusion, we can assign the function A to those genes. We are less confident for sure because it's a prediction, but we assign the function A. So that's the color orange. And because my query gene here is connected to this node, we can also, by guilt by association, predict that this gene A is also related to the function A because it is functionally related to the other genes that are known to have this function A. And for sure, we could not assign function A function. Let's say this one, these nodes have function B. We could not assign function B to this node because they are not related. So the network weights, so I've seen the weight here. So we've seen that there are different weights that are used to build the network and guess the gene function. And just to tell you that you have a parameters in gene mania where you can adjust the weighting. And by, so automatically the weighting is associated to give more weight to the pathway network. I think it's the blue network here. The pathway network come from the go BP, go biological process because we are trying to guess a function. So that's why it makes sense to give this network more weight. But also sometimes you can put equal by network. So all the networks in each individual main categories of networks would have the same weight. And it's you use it when you want to get all the information about your genes. You don't want to select just in one direction. And it's better to try it, to try the different parameters on the menu to understand the consequences on the network that you are going to create. So now some applications of of gene mania. So like mainly based on my projects and how I use it. And hopefully that gives you ideas on how to use it for your own project. And also I'm going to mention if I prefer to use it on the site escape app or on the on the website. So the first one, as we say, the first question with gene mania is we query one gene. We don't know the function and it predict the function. So here I tried it with IPO form because that was like in my work. My question was, well, do I mean, do a query of IPO four, which is an importing. So it's the role of IPO four is to import proteins into the nucleus. But IPO form is part of the family of importing. So I had to do not only the work of IPO four, but the question that I was asked to solve is to do it on the family of importance. And I had no knowledge of importance. That was my my first time working on IPO four. So what I did, I just copied and paste IPO four in my gene mania search box and gave me all these genes. And I've extracted all these genes and I did my my heat map and my analysis was all these genes. And then this is the comparison with a diagram that illustrates the importing family. And then you can see if you look at that, that's a lot of or many, maybe not all, but almost all the importing genes that were important were retrieved by gminia. So that saved me a lot of times instead of Google and PubMed and looking at the papers to gather all the genes of the importing. I just did it in five minutes. So this is one example. And I did the same with this one. So this one, these two genes is that H1 and SUSE 12, they were part of my hits, you know, when I did my RNA sequencing and they were part of my hits. So I looked at the function of these genes and I kind of understood that they were part of this PRC2 complex, which is the polycom repressive complex. But that was about this. I didn't have more knowledge than that. So I copy and paste these two genes now in gminia. And again, my goal, I wanted to retrieve all the protein complex for the PRC2 to do a more comprehensive analysis and my hit map and to make sure that all the complex was affected in my experiment. So this is also how gminia helped me with that. And I also did in the gminia web app because I just Google gminia and I copy and paste my gene. And then there is a function to export the gene list and that's the way I did it. So that's a third example. That's absolutely not my project. But I thought that it was also interesting. I think there is the paper, the reference paper here. And I think they did gminia in this reference paper. So they were working on Alzheimer's disease. And what they were doing is looking for mutations, indels, and they found 206 variants when they studied Alzheimer's disease patient resources. So early onset Alzheimer's disease patients versus control. And so these 260 variants, so that's NIPPS, deletions, mutations. They could associate this with 23 genes. So now they wanted to know the function of these 23 genes because they were not found together the 23 genes. They were found some in patient one, some in patient two. So that's the result of the global analysis. So they used gminia. So they input their 23 genes into gminia. And the first goal is to find the connection between these genes. And they found that actually these genes were known to be functionally related to each other. So that's the first result that they got. And then gminia, so using the function prediction, is going to add genes related to those genes. So the query genes are the ones that you see with the black border. And the other genes that were added by gminia are the smaller genes. So then what is useful is that it could connect two genes. So you have two genes for your query, for example, A and C, they are not connected. But by using the linker, now your two genes, A and C, are connected by this linker gene. So you expand your network. And also, now that you have this network, basically your goal is to find the genes that are important in the early onset isomers disease. So first, you started with your core of 23 genes. But maybe you retrieve these other genes as well that are important. And when you apply the functional enrichment on this, so on the left, you have the pathway enrichment for those genes. And then you see that it's also related to neuron function. So it gives you some reassurance that these genes work together in some brain function. And then you can further analysis them. And so this one was done in the app. But you could have done it in the web version. But it's starting to be a bigger network. So maybe you want to go back to the cytoscape app. So the cytoscape app will have the advantage that you can play with the visual style. You can merge networks. You can import attributes. So that would be one reason that you move to the cytoscape app. So that's another example. This is 43 genes implicated in cholesterol metabolism. And basically, they just wanted to do a nice figure for their paper. So they had their 43 genes, but they wanted to connect it in some sort of diagram as a network. And so they use G-menia. So I recognize the brown lines for shared domains. So all these proteins there, they seems to have a shared domain, which is useful information. The linker genes here, they could connect, for example. So this one is a gene that is added by G-menia and could connect these two nodes with the other nodes. So it adds some connection to make the link better and the figure better. And they added their visual styles. I think the center of the node was for the 24 time points or three hours time points. And the border node was for their 21 hour time point. So that's another example. And then you've seen it. So then we might have seen it yesterday at the end of module 3 G-profiler lab. Then when you have created your heat map, then you can right click and have directly the G-menia network. And why is it useful? It's that you could have a pathway that is very significant, but contains subnetworks in it. So it could be, I think it was like RNA, let's say RNA translation or RNA, I don't know, I cannot read it. But some RNA processing translations, it's kind of a big network. And then if you do G-menia, what you are going to do is you are going to find a subnetworks into your full networks. And you can, using the, a little bit like reactor my file. So once you do the functional enrichment, you can click on the function and it would highlight the genes corresponding to the function. So then in this case, in one node, I could extract sub five modules and annotate more precisely my network. And this is another example of when I use it. So maybe for some of you, this is a really advanced topic. This is not something personally I do, but there is the possibility to add your model organism. So G-menia, basically, there are a lot of organism possible, but you also can create your own database. So you just import your database and you can construct the networks. And there is possibility to use G-menia as an example to assemble different interaction networks that you might have for your research question. So basically use the algorithm and modify it for your own needs. So this one actually, so Ruth was implicated in this. This is MDX. It's a patient classifier algorithm that is based on the G-menia algorithm and you have the reference. So for me, it's really advanced, but I wanted to show it to you because we are working on different projects and that could be very useful for you. So I think this is, oh yeah. So this is the last slide for G-menia. Do you have any questions for G-menia? No. So then for the lab, we are going to also use the string app just a little bit. So the string app is also going to give you a functional interaction network. So you will have the choice between some different database. And so when we say functional association, it could be any relationships between the proteins like pathway relationships and physical interaction is when we know that the two proteins are physically connected to each other. So we are going to use the string app that is actually, I think included in the Insetterscape app and then you can also install it. And so first you create like a very beautiful network, but there are also options to do functional enrichment and so on. Yeah, so this is the functional enrichment on the string app. So this is these two that we are going to use in the lab of module five now. And we are going to see it also a little bit in module six. And this one is a new tool. So I just wanted to mention to you because I found it interesting. I've never used it in my research, but I was like doing some PubMed research on the different tools. And I say, oh, well, this one is interesting as well. So I wanted to be exhaustive and present you different tools. So FunCop seems to be very promising, very well maintained. It's a web app, but it has really the same idea as creating a functional network. As we are seeing today with different also with, you know, co-expression, pathway, protein, protein interaction, a lot of model organisms seems to be very user friendly. So if you're interested in your project and you need to construct some networks or just to analyze some genes, I would like to mention this tool to you as well. So that's it for the slides.