 Welcome to this brief introduction to Cytoscape String App Version 2. Cytoscape String App is, as the name suggests, an app that integrates the String database of protein interactions with the Cytoscape tool for network analysis and network visualization. This is really powerful if you want to do visualization of omics data on networks creating figures like what is shown here. I've already covered Cytoscape String App in an earlier presentation, so if you haven't seen that one, I suggest you see that one before continuing with this one. The reason for this presentation is that we've recently released version 2.0 of Cytoscape String App. I will therefore focus on new functionality, which mainly relates to host-parasite interaction networks, heterogeneous networks in general, and enrichment analysis, starting with host-parasite interactions. One of the new features in String App Version 2.0 is the cross-species query. It allows you to simply specify two different species, and the app will then first identify the cross-species interactors, those are the proteins that have interactions with proteins in the other species, and then retrieve all interactions for these, both intra-species and inter-species. Another way of creating a host-pathogen network is by using the Expand Network functionality. This functionality allows you to add interaction partners to your network of the proteins that are already in the network, and it can be used to add interactors from another species as well. This way you can create multi-species networks, not limited to just two species, such as the one shown here. This is an example network of the malaria-parasite, its human host, and the mosquito vector transmitting it. The cross-species interaction data shown in this figure is not in the string database itself. What we are doing is to integrate experimental data and text-mind associations using the string pipeline, and then using a new mythology-based transfer pipeline to transfer interactions between different host-parasite pairs. In addition, we're doing score calibration of all the interaction scores to make them as comparable as possible to all the interactions in the string database. Host-parasite interactions are just one example of heterogeneous networks, which is the major focus of string app version 2.0. Another way that you can create such networks is by using the stringify network functionality. It allows you to take a non-string network, for example, coming from your own interaction data, or from a dataset you downloaded from a published paper, and then map the proteins in the network to string. This gives you a string-like network in which the proteins that can be mapped have been mapped to string, but the interactions are still the original interactions from your network as shown in this example. This is an interaction network of the SARS coronavirus 2 with the human host, with the human host proteins mapped to string. However, as you see, there are also non-string nodes, in this case, the viral proteins. The advantage of doing this is that you get all the string app functionality for your network. This means that you can access protein information about the human host proteins in the example network, you get the visual style as you already saw, and you can retrieve additional interactions from string in addition to what you had in your own network. You can expand the network using the expand functionality I already mentioned earlier, and you can run enrichment analysis, which is the last topic I want to cover today. The enrichment functionality of string app allows you to retrieve enriched terms, filter the resulting terms, and visualize them on the network. This has already been covered in a separate presentation that you can go watch following the link in the corner. A very common use case of this is to work with clustered networks and select a cluster, run enrichment analysis to retrieve the enriched terms, and thereby characterize the cluster, and then simply move on to the next cluster and repeat. To make this easier, we've added a new functionality called group-wise enrichment. Here, you specify a node column which is used to define the groups. This could, for example, be the column containing the cluster numbers coming out or running MCL clustering on the network. It will then analyze every group and create an enrichment table per group. Finally, the enrichment functionality has a new visualization option that allows you to add specific terms as nodes in your network. This means that you can, for example, add enriched pathways or enriched diseases as separate nodes in the network, and you will get edges to the proteins that are involved in the pathway or disease. It will also have all the enrichment statistics on these edges. If you think about it, this is yet another way of making heterogeneous networks that are, for example, a mixture of protein nodes and pathway nodes. That's all I have to say about StringApp v2.0. If you want to learn more about how you can use Cytoscape, StringApp and other Cytoscape apps together, I suggest you take a look at this tutorial next. Thanks for your attention.