PUPpy: a primer design pipeline for substrain-level microbial detection and absolute quantification
Bibliographic record
Abstract
Characterizing microbial communities at high-resolution is crucial to unravel the complexity and functional diversity of microbial ecosystems, providing valuable insights into human health and ecology. Over the last decades, advances in bulk sequencing assays, such as 16S rRNA and shotgun sequencing, have enabled unparalleled qualitative and quantitative investigations of microbial communities. However, these methods generally do not provide resolution beyond the genus- or species-level, only measure relative abundance, and do not account for spatial organization, overlooking intricate details and functional insights on the real gut microbial heterogeneity. PUPpy (Phylogenetically Unique Primers in python) is a fully automated pipeline to design taxon-specific primers for any defined bacterial community. PUPpy-designed primers can be used to detect individual microbes and quantify absolute microbial abundance, providing more resolved and accurate quantification in gnotobiotic communities than 16S and shotgun sequencing.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.056 |
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".