 Welcome to this basic string tutorial. Here I'll give you a hands-on demonstration of how to use the string web interface. So if you're not already familiar with string, I highly recommend that you go watch my brief introduction to the string database before continuing. Specifically, I'll show you how to query for approach in, the different ways of viewing the resulting network, that is evidence versus confidence view, and the key parameters that affect which network you will get, such as the confidence cut-off, the number of interactors to show, and the type of network to query for, that is functional association network versus physical interaction network. I'll also show you how you can access enrichment analysis results and select key terms and highlight them on the network and finally export the image, for example, for use in a publication. So let's get on with it. This is how the front page of string looks the first time you go to stringdb.org. If you hit search, you get to the main search page, where you can type in the name of your approach in of interest. We'll search for INSR, Insulin Receptor in this example. Next you can select the organism, we search for homo, and then select homo sapiens from the list, to get a network of human insulin receptor. Then simply hit search to get to the disambiguation page, where you see the search results for INSR in human. The right protein was already selected, so we just hit continue to get to the network result page. Here you see that the query protein INSR is shown in red, and all the other nodes, the interaction partners, have other colors which allow you to easily cross-reference them with the evidence table down below. If you go to the settings tab, you'll see that the network is currently viewed in what is called evidence view. That is, each interaction can have multiple different colors showing the types of evidence in string that support this interaction. If you change to confidence mode, you will instead see just one line for each pair proteins, and the strength of this line shows the confidence score that it has in string. From this page, you can also change the confidence hot-off, for example, increasing it from 0.4 to 0.7, and when you update the network, you will then see that some of the interactions, the lowest confidence ones, have disappeared. You can also increase the number of proteins to show by increasing the maximum number of interactors from, for example, 10 to 20. When you do that, and again hit update, you will see a bigger network with more proteins, and of course more interactions. But still, all of them score above 0.7, since this was the confidence hot-off we selected. You can also change the type of network shown. Currently, we're using the default, which is the full string network of functional associations. You can instead select to use only the physical subnetwork to get a network of physical protein interactions. If you do that, and again update, you see that the network has much fewer interactions, but more importantly, the set-up proteins have changed, because now we're showing the top 20 physical interaction partners, rather than the top 20 functional interaction partners. If we update and say we want 50 interaction partners instead, you will see that nothing changes. The reason is that we don't have more than 20 proteins with a physical interaction confidence score above 0.7. So let's lower the confidence score back down to 0.4 and again update the network. Once you do that, you see that you now get a big network with almost 50 proteins and physical interactions among them. Let's say we're happy with this network and we can now move on to the analysis tab. In the analysis tab, you see enrichment analysis results. If we scroll down, we can select some terms of interest and have them highlighted. So the first term we select is highlighted in red and the second one we select is highlighted in blue. Once you've created a network with the desired terms highlighted, you can choose to go to the exports tab and download the resulting image in bitmap format, for example, for inclusion in an article. Thanks for watching this tutorial. I've linked the hands-on exercises down below. Also, if you want to learn more about network visualization, I suggest you watch this presentation next.