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Visual Profiling

Composition Profiling

One of the most basic and first questions that arise in metagenomics-based resistome studies are what antimicrobial resistance genes (ARGs) are there and in what proportion are they present in our samples. Another way to phrase this question could also be: What is the resistome composition?

With regards to Patricia’s example, one can also be interested in finding whether some new ARGs have appeared after treatment in patients that were given antibiotics. Did some specific ARGs get enriched in abundance due to antibiotics?

One way of answering these questions is to open the resistome count table in any text editor or excel and manually inspect each ARGs across samples or groups. However, this approach will be very tiresome, time consuming, prone to error, and will be practically impossible especially when the count data have ~1000s of ARGs across hundreds of samples.

The first step in the analyses of resistome is the visualisation of ARG composition. The most frequent type of visualisation that you will encounter in the literature are stacked bar or area plots. In these, the differences in resistome composition can be viewed at different functional levels (ARG, class, mechanism, etc.). You may ask: why do we need to visualise the resistome profile at different functional levels? The answer to this question is relatively straightforward. Every type of visualisation has limits for how much it can display while still being useful. These limits are not due to the visualisation alone, they also come from the capabilities of our brains to perceive and interpret what our eyes see. Now imagine visualising ~100s or more ARGs and their relative abundances present in our resistome data at once. You will find it extremely challenging! It is practically impossible. The better approach in that case should be merging the data into higher-level functional categories such as class of drugs to which ARGs confer resistance (Class-level) or to their underlying molecular mechanism of resistance (Mechanism-level) to gain better and more meaningful understanding of the data. Once you have a clearer hypothesis, then you can ‘zoom in’ into the area of interest and better appreciate and explore its differences.

In addition, there are various other intuitive visualisation approaches in metagenomics that can also be used to explore the resistome composition such as Sankey diagram, zoomable sunburst, treemap, and others. The advantage with such visualisation is that one can look at resistome data while simultaneously showing the hierarchical relationships and connectivity between ARGs (features) at different functional levels all at once. However, these strategies are more suitable for resistome profiles having an acyclic and hierarchical functional annotation structure. For example, taxonomy and phylogenies are organised as trees, with items arranged in a hierarchy and each item having only one parent, such as bacterial phylogenetic trees.

ResistoXplorer supports all these visualisation approaches to facilitate the exploration of resistome composition. Let’s move forward to next step to learn how it can be done in ResistoXplorer.

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Exploring the Landscape of Antibiotic Resistance in Microbiomes

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