In this short tutorial we will describe how you can use the 3D-GNOME web server and demonstrate the sample results.
Suppose that we are interested in a 8966211-11059337 region of chromosome 1. To start go to the New request page. There is a number of options available there, but it is enough to provide the genomic region of interest. Enter the value "chr1:8966211-11059337" in the Region to reconstruct field and click the Send request button below. A notification of successful request submission should appear at the top of the page together with a link to the results page. The computations should take several seconds, but depending on the region size, the selected settings and the current server load it may take up to several hours. You are strongly advised to save the address to be able to access the results later.
If you would follow the link and go to the results page before the computations are done you will see the current status of the request (Received means that the request was sent, Started means that the processing of the request was started). Should there be any errors in your request they will also be shown on this page.
Please refresh the results page. If the request is not already processed you can visit the sample results page with the results already in place.
The results are presented on 2 pages - the first one contains some statistics, heatmaps and plots, and the second - a 3D structure visualization. The former will be described first.
At the top a graphical representation of the clusters contained in the specified region is presented (Fig 1). Every interaction is represented with an arc with the height corresponding to the interaction frequency. Here we can clearly see which loci are interacting with each other. In our case we can identify 2 main clusters of interactions (roughly 9-10Mb and 10.5-11Mb) connected by a less condensed region (10-10.5Mb). We can see that in the right cluster (10.5-11Mb) there are 3 main interaction hotspots with multiple anchors, while in the left one the interactions are distributed across multiple loci, with a number of shorter but much stronger interactions (count > 100)
Next, a table with some basic statistics is displayed (Fig 2):
Please note that by clicking on "singletons" or "clusters" you can download a file with a list of the corresponding interactions.
Interestingly, we can see the most frequent interaction is a singleton and not a PET cluster (248 vs. 238). This may seem a bit misleading at first - please see FAQ for an explanation.
Next we can take a look at the singleton interactions. Because of their sheer number they are presented in an aggregated form of a heatmap (Fig 3). In a raw heatmap each cell represent the number of interactions between the two corresponding genomic regions. As these numbers are biased (for example by a local region visibility), we normalize all the entries using a simple matrix balancing algorithm called Vanilla-Coverage normalization (for a discussion of different heatmap normalizations see [Rao et al, 2015]).
Heatmaps are useful for identification of contact domains (TADs). In our case we can see that there are 4 visible domains, with the first 3 forming something like a higher-hierarchy domain. This hierarchical nature of domains is something that is very often observed in the data, but there is still no consensus about how to treat them, and depending on your needs you may want to take a look at the individual domains or at the bigger, aggregated ones.
Please note that you can click the "Heatmap" links to download the file with numerical representation of the heatmaps
Next there are 2 plots showing the distribution of lengths of clusters and singletons (Fig. 4). Based on these plots the density, or looping of the analyzed region can be estimated. For example, a large number of short clusters means that there is a large number of short chromatin loops in the region. We can also study differences between interactions mediated by different transcription factors - here we see that RNAPII forms significantly shorter loops than CTCF does, although there are some longer loops, up to 400kb. Note that for clarity the singleton plot is capped to 1Mb, and the cluster one - to 600kb.
On the last plot (Fig. 5) we can see the distribution of the interactions across the region.
The 10-10.5Mb region seems to have significantly fewer interactions than other regions. Surprisingly, there is also much fewer interactions at the 10.7Mb loci.
The 3D visualization is located on a separate page, accessible by clicking on the Open 3D view link at the top of the results page. When you arrive at the page you should see a black background of the visualization plugin and a progress bar with the current progress of model loading. After the model is loaded you can play with it using your mouse (left-click and drag to rotate, right-click and drag to translate, scroll to zoom in and out). There is also a number of options in the collapsible menu on the right.
Here we present the visualization for the selected region (Fig 6, left) with a number of chromatin loops. To better understand how the simulation works and how loops are represented we can show the beads used in the simulation using the Chromatin Options->Show spheres checkbox.
It may be interesting to see where this region is located in respect to the whole chromosome. For this reason we can visualize the whole chromosome (we can create the structure in the same way as for this region, but this time you need to specify "chr1:1-250000000" as a region).
To highlight a region please expand the Chromatin Options submenu, enter "8966211-11059337" in the Select region field and check the Highlight checkbox. Now the selected region is highlighted. You can control the strength of highlight using the Dim opacity slider. Finally, click on the Save image button on top of the menu. Now you can save the screenshot of the structure to your computer.