Further courses for training

Expanding your skills after the course

As mentioned in earlier weeks, there are many options for investigation of AMR mechanisms in bacteria. We covered some of the well known and easily accessible databases last week, including CARD and ResFinder. However, there are times when you may need to take it a step further. Expanding your skillset to allow you to handle larger datasets and customise your workflows is important for growing as an independent AMR researcher. Below are some starting points for you to continue your journey after this course. Please note, a full explanation of all these approaches and tools is outside of the scope of this course. We only supply these as examples of the next steps for you to undertake your own continued professional development.

Command line interface

We mentioned in step 2.8 last week that the AMR tools are best used through the command line. This is also referred to as the Terminal if using the OSX operating system, or UNIX if using Linux or the integrated command line in Windows.

Below are some guides to get you started with learning the command line. An easy way to install many of the tools mentioned as well as a whole host of other tools is conda. We include some guides on how to set that up as well

• Beginners guide to the command-line interface (YouTube video)
• A guide to Conda (conda website guide)
• UNIX and Conda tutorial (step by step)

We strongly encourage you to start learning UNIX as it is a core skill for any budding bioinformatician. If this feels daunting, many people are using AI such as ChatGPT to help them learning how to code. This has advantages and disadvantages as laid out in this article. However, it can be helpful when learning the basics.

Once a little comfortable with UNIX, why not try installing abritAMR and using it to find some resistance genes. You can follow this tutorial to do so. If comfortable with that, install AMRFinderPlus via Conda and see if you can start predicting AMR resistance in a more automated and local way. You can find the how-to for this set-up on the AMRFinderPlus Github Wiki page.

Galaxy

As mentioned last week, many of the AMR tools, and indeed a whole host of other tools, are available on Galaxy. This is a free to use web server for doing many different bioinformatics analyses without having to use the command line. It is not as powerful, flexible or high throughput as command line tools, but it is very useful for smaller datasets.

The Galaxy Training Academy has many great tutorials for getting started with Galaxy tools. You could start with the Loading data tutorial to upload the plasmid sequence we downloaded earlier in the week and then use ABRicate in galaxy to predict AMR patterns. The community is very vibrant and new courses are run regularly so keep an eye out.

Ariba: a powerful alternative AMR tool

For even more flexibility in your predictions and databases, Ariba is a command-line interface tool which detects genes and mutations using local assemblies.

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More information on how ariba works can be found in the original publication https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000131

Ariba has the benefit of being extremely flexible in how it is used, candidate genes can be interrogated for variants, novel gene families can be identified. However, it does require some set-up, including identification of gene sequences to query and collation of any appropriate metadata, however, there are some pre-built databases for some species including E. faecium, N. gonorrhoeae and S. sonnei.

The output of ariba is extensive and details the quality of the observed matches, the heterogeneity of any observed variants and many other metrics that are extremely useful for understanding AMR mechanisms. Extensive documentation can be found here.

Conclusion

Analysing pathogens involves becoming comfortable with data, particularly genomic data. Take the time to explore your datasets and use the tools we’ve discussed, as well as others available to you. There are many tutorials that can help you develop various skills. After this course, we hope you are well on your way to understanding antimicrobial resistance (AMR) databases and how to apply them in your own work.