 Welcome to this brief introduction to String Enrichment Analysis. The String Database is best known for the protein interaction networks that you can retrieve from it via the weapons interface and which look like this. However, String is also a powerful tool for doing gene set enrichment analysis. If you're not already familiar with enrichment analysis, I strongly recommend that you go watch my introduction to the core concepts first. String provides a number of different gene sets that you can use for enrichment analysis. It of course has gene ontology terms, pathways, protein domains and uniprot keywords like most such enrichment tools. However, these are complemented by additional information or subcellular localizations of the proteins, where they are expressed in tissues and associated diseases, all of which come from integrative databases such as the diseases database which I've covered in a previous presentation linked up here. In addition to this, String provides so-called local network clusters which were generated by running hierarchical clustering on the global string network. And finally, String has publication enrichment, meaning that each publication in the vast biomedical literature has been turned into a gene set by mining for gene and protein themes in the text. With these gene sets you can do two types of enrichment analysis. The first is basic list enrichment. In this case, you provide a protein list, which would typically be the significantly regulated proteins from some study. You go to the multiple protein search option in String and in the web interface you simply paste in your list of proteins and hit search. When you do that, you first be provided with a protein network looking the way a String network looks. However, you may not have noticed there is a tab called analysis and if you go to that, you will see gene set enrichment analysis results provided immediately. These are listed in tables with a separate table for each type of term. Here I'm showing the pathways, but I already mentioned the many other kinds of terms that you can do enrichment for. If in this table you select a term like I've done here, it will be highlighted in the network above, so that you can see which proteins in your network correspond to the selected term. And you can also go select multiple terms like I've done here, selecting three different subcellular localizations and they will all be highlighted with different colors in the network and the method allows to highlight a single protein in multiple colors if it has more than one of the terms that you've selected. Both the figures and the tables can of course be exported so that you can easily use them in your papers. The second way of doing enrichment is ranked list enrichment. In this case, you provide a ranked genome-wide list, typically in the form of log ratios from some omics study and you input these via the search option called proteins with values or ranks. The interface looks like this and you can either paste in the data or upload a file which is typically what you would want to do when it's a genome-wide list. Once you hit search, it will perform the gene set enrichment analysis and you will see the results presented again in tables. The columns are a bit different, for example telling whether a certain gene set is enriched near the top of the list or the bottom of the list. You can inspect this further by selecting a term and when you do that, it will be both highlighted in the table of course and the corresponding proteins will be highlighted in the ranked input list showing it like this where you can see that indeed the proteins corresponding to the selected term are preferentially near the top of the list and it will be highlighted in the full proto network so that you can see that these proteins don't just have this term in common but they also tend to be localized near each other in the string network. Again, these results can be exported so that you can use them for further analysis. That's all I want to say about the enrichment functionality in string. If you want to learn more about the string database in general, I strongly recommend that you go watch this presentation. Thanks for your attention.