Identification of type IV conjugative systems that are systematically excluded from metagenomic bins
Bibliographic record
Abstract
Abstract BackgroundConjugation enables the exchange of genetic elements throughout environments, including the human gut microbiome. Conjugative elements can carry and transfer clinically relevant metabolic pathways which makes precise identification of these systems in metagenomic samples clinically important. ResultsHere, we outline two related methods to identify conjugative elements in the human gut microbiome. Conjugative systems can effectively be identified from metagenomic assemblies either using a curated sets of profile hidden Markov models or by searching against large-scale databases, such as UniRef90. Both methods were successful at identifying type IV conjugative systems with profile hidden Markov models being faster, but less sensitive than alignment to the UniRef database. Finally, we demonstrate that the majority of assembled conjugative elements are not included within metagenomic bins, and that only a small proportion of the binned conjugative systems are included in "high-quality" metagenomic bins.ConclusionsAnalysis of the human gut microbiome by shotgun metagenomic sequencing has revealed numerous connections to human health outcomes. Our findings emphasize the need to identify and analyze conjugative systems outside of standard metagenomic binning pipelines. We suggest that analysis of type IV conjugative systems should be added to the current metagenomic analysis approaches as they contain much information that could explain differences between cohorts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".