Classifying mobile genetic elements and their interactions from sequence data: The importance of existing biological knowledge
Bibliographic record
Abstract
We agree with Che et al. (1) that understanding how mobile genetic elements (MGEs) spread antimicrobial resistance (AMR) is important. However, decades of research have already characterized a diverse toolbox of MGEs involved in the emergence of AMR, including conjugative plasmids and insertion sequences (ISs), and their interactions. “Mobile” AMR generally arises following rare capture of a chromosomal gene by a particular “intracellularly mobile” MGE, e.g., an IS or a transposon (Tn), and translocation to “intercellularly mobile” MGEs [plasmids, phage, integrative elements (2, 3)]. Tools for systematic analysis of AMR gene−MGE interactions in mounting sequence data are needed, but incorporating existing biological knowledge into their design is vital, as are representative benchmarking datasets and recognition of data biases and resulting limitations. Che et al. screened bacterial plasmid sequences for markers to classify them as nonmobilizable (no relaxase), mobilizable (relaxase), or conjugative (relaxase+). Relying on the absence of a predicted feature is risky, and Plascad’s poor sensitivity in many species was not recognized, due to a taxonomically inadequate benchmark dataset. Numerous conjugative plasmids, and close relatives, from low-GC gram-positive organisms (dataset S1 in ref. 1; e.g., Staphylococcus, pSK41, pWBG4, pWBG749; Clostridium, pCW3, pCP13; Enterococcus/Streptococcus , pAMβ1, pCF10, pRE25, pMG1, pIP501) (3⇓–5 … [↵][1]1To whom correspondence may be addressed. Email: sally.partridge{at}health.nsw.gov.au or neville.firth{at}sydney.edu.au. [1]: #xref-corresp-1-1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".