 Okay, welcome. My name is Selena Rasha and together with Sasuke Hilton we built the Circus and Galaxy tool, which we're very happy about and we're very excited to share with you today. So to get started with the Circus tool, you really need to be familiar with Galaxy first. It leverages a lot of the different features of Galaxy and you need to be familiar with it beforehand. In this slide deck, however, we'll cover what is Circus, how can you use it in Galaxy, and what are some of the different track types you can plot. We'll talk primarily about Circus for genomics visualization. However, Circus can be used for any type of visualization where you have some tabular data that you want to express within a plot. As long as you can convert your data into a tabular format that Circus can process, which we'll cover in the tutorial, then you can plot it with Circus. So what is Circus? For those of you who aren't familiar with it, Circus makes circular graphics. They're highly flexible, but it's also very, very complex piece of software. And in exchange for these really beautiful eye catching plots, you have to spend a lot of time investing in the configuration of the plot. And this is a problem because these plots are fantastic. Everyone should have access to them. PIs love these plots. Publishers love these plots. They're very eye catching. And so we built the Circus and Galaxy tool, Sasuke and I, to make this configuration easier. The Circus and Galaxy tool takes in all of your different data formats. You may need to pre-process some of it, but then it'll let you build these complex plots directly within Galaxy with a browser without having to set up Circus or anything like that. And if you need to go further, Circus and Galaxy lets you download all the configuration you need to reproduce the plot locally. It's not designed to make 100% production ready or publication ready plots. It limits some of the features for security reasons or just because Galaxy has nice components for selecting which color you want to use in a different plot type. It doesn't always expose all of the options that Circus has and you might want to take advantage of some of these advanced options. So what we do is when we make a plot in Circus, we also automatically share all of the configuration files you need if you want to download them and continue working on them further. So most of the features are supported. We won't go into that too much detail for now. We'll skip ahead to track types. These are the different ways you can plot data on a Circus plot. The first type and the most important one of the most used ones is Histogram plot. This is simply a Histogram plot as you know it except it goes from going radially around your genome or other data set. We commonly use these things for things like gene density or GCSQ, sequencing depth, all of those sort of things where you might want to quantify whether the value is a large value or a small value, top 10%, something like that. These plots are great if you want to do visual quantification of the results. Alternatively, you can plot all of the same data sets using the heat map track type. This will produce a more visually pleasing, but harder to quantify. Harder to quantify a track. So when you need sort of a general feeling of like, are there a lot of genes in this region or something like this, then you can use a heat map where you might not need that more precise like, oh, this is an outlier top 10% value, something like that. Next up is the tile track type. This is often used for showing genomic regions. On display here is also the label track type. You can see the different text labels are arranged around the edge. This is great for genes on small genomes. Once you get to larger genomes, this track type is not so useful. Again is the scatter track type. This is very similar to the heat map in Histogram except with individual data points rather than a connected data series. If your data is not continuous, if it has gaps in it, then a scatter plot can be the most appropriate track type. Additionally, you can do a line track type. This is again very similar to the heat map except instead of being stepped up and down in a square, square increments. The lines will just go from point to point. One of the most commonly used and most important track types however is ribbons. These are used to connect two different regions of a genome. You can use these for things like, oh, these two different regions of a genome are related according to last Z or blast something like this. They are often used for sent me mapping as a result. We use them in one of the plots for showing translocations between different regions where bits of gene or DNA has been exchanged between two different regions. And there this gives us a feeling of which regions have a lot of different translocations. One of the key points we want to drive home, however, is that Circos is an iterative process. You don't start at the end, you don't start with, I know exactly how I want my final plot to look like. We've included an animated screenshot here of what it took to produce one of the plots that you'll be producing during the lesson. I think this is from the paper that we released. It's a lot of, okay, well, I want this feature to look like this, and maybe I want this data to be expressed in this way or this track type. You can see us editing things over time changing the coloring changing scales to make the track the data easier to visualize for people. And just remember this when you go into starting making your own Circos plots. It's not, you often don't come with a completed idea. You start with some data that you want to plot and you build it up over time. In this graphic, you can see a lot of scatter plots as well as some ribbon plots in the center. They don't look like the ribbon plots that we saw earlier, but they're just thinner. So key points to this is that Circos is very powerful, but it's also very complex and creating plots will take time, be patient. And when you're done with your plot in Galaxy, you can download all the configuration in order to improve upon it locally. Thank you.