Dear all,
I think with this final question I have covered all the open ends from the CoP meeting. Please drop anything I missed on the list!
This interesting question was brought up by several people in the meeting: to what extent are we able to detect novel resistance genes (or virulence genes, or plasmids)?
Given that our current tools are all based on mapping sequence data on reference databases, to what extent does this generalise beyond relatively similar sequences?
An obvious direction to look in are deep learning models, which appear to have a remarkable capacity to generalise beyond their training data - but could they actually predict genes coding for a hitherto unknown resistance mechanism, or even predict resistance for a highly diverged gene, while never having seen the phenotypic "ground truth"?
This explains my interest in the concordance of genotypic prediction and phenotypic AMR, and collecting the false negatives: isolates with positive AST but negative ResFinder prediction are precisely the ones we'd like to be able to generalise to!
In my very limited experience (we did one study in 2021, looking at GPlas / MLPlasmid and Deeplasmid for predicting plasmids), the tools essentially predicted (poorly) what was already in the PlasmidFinder database.
Clearly though there has been a lot of development since, and I would be very interested to hear of your experiences!
Marco
Dear all,
Guess what, on deep learning for AMR detection, this just in:
Pei Y, Shum MH-H, Liao Y, Leung VW, Gong Y-N, Smith DK, et al. ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. Microbiome. 2024;12: 84. doi:10.1186/s40168-024-01805-0 https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-024-0180...
Cheers, Marco
Awesome! Thanks for sharing!
From: Marco van Zwetselaar via Bioinfo List bioinfo-list@seqshare.org Reply to: Marco van Zwetselaar io@zwets.it Date: Tuesday, 14 May 2024 at 13:35 To: "bioinfo-list@seqshare.org" bioinfo-list@seqshare.org Subject: [Bioinfo-list] ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences
Dear all,
Guess what, on deep learning for AMR detection, this just in:
Pei Y, Shum MH-H, Liao Y, Leung VW, Gong Y-N, Smith DK, et al. ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. Microbiome. 2024;12: 84. doi:10.1186/s40168-024-01805-0 https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-024-0180...
Cheers, Marco
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Cebile Lekhuleni -
Marco van Zwetselaar