Coronavirus Mutations Revealed With Graph Visualization and Analysis

The internet has exploded with information about coronavirus and the disease it causes, COVID-19. The vast amount of data feels like too much to keep up with. But that’s where graph and data visualization thrives! So, we used the power of graph visualization and analysis to visualize the continually updated genomic epidemiology data provided by Nextstrain to reveal previously unseen coronavirus mutations.

Phylogenetic data of coronavirus genomes and mutations from mid-March 2020 shown in symmetric layout using Tom Sawyer Perspectives

Understanding Coronavirus Mutations

To find a cure or develop a vaccine, researchers examine the RNA of the virus to figure out how it works, grows, develops, and changes. To do this, they break down the RNA to its smallest parts, identify each part, and then look for changes or relationships—like cracking a complicated code. Because genome sequencing uses a series of letters representing the chemical alphabet, viewing a genome sequence can be an eye-crossing affair. For example, this image is just a portion of an isolated genome of coronavirus (click here to view the full sequence):

Snippet of an isolated genome of coronavirus from the National Center for Biotechnology Information

When comparing one strain to another, researchers look for changes in the sequence. Mutations are identified any time a letter in the sequence changes, the exact way the coronavirus mutations are identified. Typically, virologists display this kind of data in a phylogenetic tree.

What Is a Phylogenetic Tree?

Nextstrain has been integral in understanding the coronavirus mutations, spread, and geographical impact. It visualizes the data in a standard phylogenetic tree.

Phylogeny graph showing genomic epidemiology of novel coronavirus from nextstrain.org

Much like a genealogical or family tree, a phylogenetic tree shows how genetic matter evolves. It uses branches to show how individuals (or individual pieces of matter) relate to each other from one generation or mutation to the next. Node color indicates where that mutation was found.

It seems simple, but the sheer volume of genomes and mutations can make for a very crowded phylogenetic tree. We’re seeing similar overcrowding in graph visualizations of coronavirus mutations. There are so many nodes in the tree, it can be hard to see the individual elements of the visualization or where potential similarities or relationships exist.

Taking Nextstrain Data to the Next Level

As coronavirus mutations continue to evolve and spread across the world, they also mutate along the phylogenetic lineage. In other words, the phylogenetic trees are getting very big, very fast. To create more effective visualizations of these structures, our research team approached the problem from two sides. First, use the graph layouts in Tom Sawyer Perspectives that naturally distribute the data in a better way. Second, combine any phylogenetic tree nodes that share a mutation.

Step One: Change the Graph Layout

Here, each node represents a genome sampled around the world. The color of the node represents the lab where that genome was found. Similar to the phylogenetic tree, the edge shows the lineage of each mutation. To better organize this data visually, we redrew the graph using symmetric layout.

Phylogenetic data of coronavirus genomes and mutations from late March 2020 shown in symmetric layout using Tom Sawyer Perspectives

Step Two: Combine Nodes That Share a Coronavirus Mutation

Next, our team developed a method of combining nodes if they shared a mutation. By running a preprocessing step before importing the Nextstrain data into Tom Sawyer Perspectives, we were able to easily update the way nodes with the same mutation display. Here, the blue hexagon nodes show the adoption rate of the virus. The higher the degree of these nodes—or the more edges that are connected to them—the greater the number of genomes that share the same mutation. It seems simple, but by updating the visualization to give these special types of nodes a distinctive shape and color, we can identify them more easily.

Phylogenetic data of coronavirus genomes and mutations from early April 2020 with blue hexagon nodes to indicate shared mutations

With the phylogenetic tree, we don’t see that the same mutation has occurred multiple times, but in our new visualization, we can clearly see when a mutation has occurred more than once. And the duplications of the mutations in different genomes could be indicative that those particular mutations are important to understand. Perhaps these mutations cause an increase or decrease in the severity of symptoms. If so, should they be considered when studying vaccines? This new insight into the same data could help scientists identify things that are functionally impacting the virus.

Extracting Additional Data

You can explore different aspects of data by extracting more information from within the data source. For the previous coronavirus mutation graph, we added a blue hexagon node to repeating mutations and edges to the samples that newly contained the mutation. Now, the blue hexagon nodes show the adoption rate of the mutation. The higher the degree of these nodes—or the more edges that are connected to them—the greater the number of genomes that adopted the same mutation. This could indicate that these mutations are especially important to study.

In the visualization below, we added geospatial data to the phylogenetic tree data. Adding location nodes reveals the different locations to which COVID-19 patients recently traveled. Symmetric layout shows clusters of growing COVID-19 hotspots. These insights can be used to better understand which patients have a higher risk of a certain strain of the coronavirus. You can also identify which hotspots might need assistance with a potential surge.

Adding geospatial data to COVID-19 visualizations can help identify hotspots.

Visualizing the Importance of Social Distancing

Recently, phrases like safe at home, shelter in place, and social distancing have dominated conversation. So how does the addition of travel change our graphs?

Below, we added nodes indicating the country in which the genome was sampled to the Nextstrain data. Colored edges link the genomes to the country in which they were sampled. Clustering indicates how many genomes were sampled in each country. The overlaying grey edges are from the phylogenetic tree. Multiple grey edges between the colored clusters indicate that the genome line moved to another country. In other words, an infected person traveled to another country.

Symmetric graph of data from mid-April showing how genomes “traveled” to other countries

What’s Next?

The farther we dive into this data, the more questions we have–and the more answers we want to try to visualize! Questions like:

• What are the effects of different public health policy decisions?
• Can visualizing the phylogenetic data show us when the outbreak actually began?
• Can overlaying additional, complex data lead to insights on how the virus spreads?

Sources

Our graph visualizations are made possible by the sharing of genetic data by research groups from all over the world. We acknowledge and thank:

• Hadfield et al, Nextstrain: real-time tracking of pathogen evolution, Bioinformatics (2018)
• Sagulenko et al, TreeTime: Maximum-likelihood phylodynamic analysis, Virus Evolution (2017)
• The Nextstrain research team, including: Trevor Bedford, Richard Neher, James Hadfield, Emma Hodcroft, Thomas Sibley, John Huddleston, Jover Lee, Kairsten Fay, Sidney Bell, Colin Megil, Barney Potter, Pavel Sagulenko, Charlton Callender, Misja Ilcisin, Louise Moncla, Allison Black, Anderson Brito, and Nate Grubaugh
• Kristian Andersen, David Blazes, Peter Bogner, Matt Cotten, Ana Crisan, Gytis Dudas, Vivien Dugan, Karl Erlandson, Nuno Faria, Jennifer Gardy, Becky Kondor, Dylan George, Ian Goodfellow, Betz Halloran, Christian Happi, Jeff Joy, Paul Kellam, Philippe Lemey, Nick Loman, Sebastian Maurer-Stroh, Oliver Pybus, Andrew Rambaut, Colin Russell, Pardis Sabeti, Katherine Siddle, Kristof Theys, Dave Wentworth, Shirlee Wohl, and Nathan Yozwiak