37  funcscan versus Pathogenwatch

Learning Objectives
  • Investigate and compare the results from different AMR-detection tools.
  • Recognise the importance that background databases and algorithms have in bioinformatic approaches for AMR detection.

37.1 Which AMR do my isolates have?

At this stage you may notice that different tools will give you a different answer to this question and it is therefore recommended to compare the results across multiple tools. For example, Pathogenwatch generally detects AMR for comparatively more antimicrobial drugs (ten in this case) compared to the funcscan analysis. When we filtered the hamronization_combined_report.tsv table we found that funcscan had only identified resistance to Tetracycline (table below, showing some of the columns from the hAMRonization table):

input_file_name gene_symbol reference_accession antimicrobial_agent coverage_percentage sequence_identity
ERX1501203_ERR1430825_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501204_ERR1430826_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501217_ERR1430839_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 99.84
ERX1501229_ERR1430851_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 99.84
ERX1501230_ERR1430852_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501238_ERR1430860_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501242_ERR1430864_T1.tsv.amrfinderplus  tet(M)  WP_000691741.1  TETRACYCLINE    100 100
ERX1501243_ERR1430865_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501248_ERR1430870_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501250_ERR1430872_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100
ERX1501252_ERR1430874_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 99.84
ERX1501254_ERR1430876_T1.tsv.amrfinderplus  tet(M)  WP_002414694.1  TETRACYCLINE    100 100

The main reason for funcscan only identifying resistance to Tetracycline whilst Pathogenwatch identified resistance to for up to ten drugs is the resistance-determinant databases used for the predictions. For species such as S. pneumoniae, Pathogenwatch uses a curated database specific to the species whilst funcscan uses databases such as amrfinderplus which contain variants for all species and may not contain the variants in the Pathogenwatch database.

In conclusion, always be critical of the analysis of your results at this stage, comparing the output from different tools as well as considering the quality of your assemblies. Ultimately, the safest way to assess AMR is with experimental validation, by testing those strains against the relevant antimicrobial agents in the lab. However, computational analysis such as what we did can help inform these experiments and treatment decisions.

Exercise: AMR with Pathogenwatch

Following from the Pathogenwatch exercise in Analysing Pneumococcal genomes with Pathogenwatch, open the “Chaguza Serotype 1” collection that you created and answer the following questions:

  • Open the antibiotics summary table.
  • Do all your samples have evidence for antibiotic resistance?
  • If any samples have resistance to much fewer antibiotics compared to the others, do you think this could be related to assembly quality?
  • How do the results from Pathogenwatch compare to those from nf-core/funcscan?

We can open the “Antibiotics” table from the top-left dropdown menu, as shown in the image below.

We can see that Pathogenwatch identified resistance to several antibiotics. We can see that there are mainly two distinct AMR-profiles in our samples.The first group is resistant to Tetracycline, Trimethoprim, Sulfamethoxazole and Co-Trimoxazole and susceptible to the other drugs. The second group is resistant to Fluroquinolones, Sulfamethoxazole and only intermediate resistant to Co-Trimoxazole. Additionally sample ERX1501242_ERR1430864 is only resistant to Tetracycline and sample ERX1501229_ERR1430851 is resistant to Tetracycline, Sulfamethoxazole intermediate resistant to Co-Trimoxazole.

If we look at the funcscan results we can see that it identified Tetracycline resistance in the same 12 samples as Pathogenwatch.

37.2 Summary

Key Points
  • While there is overlap in the AMR results of different software, these results can be sometimes widely different.
  • Besides differences in the algorithms used, one key difference is the databases used to infer the presence of antimicrobial resistance.
    • Tools designed to be more widely applicable, such as those used by nf-core/funcscan, may have less power to detect AMR.
    • Tools such as Pathogenwatch, which uses curated databases for specific species, may identify a higher number of AMR genes.
  • The best way to validate bioinformatic AMR results is with experimental validation.