Lessons and considerations for the creation of universal primers targeting non-conserved, horizontally mobile genes
Bibliographic record
Abstract
Abstract Effective and accurate primer design is an increasingly important skill as the use of PCR-based diagnostics in clinical and environmental settings is on the rise. While universal primer sets have been successfully designed for highly conserved core genes such as 16S rRNA and characteristic genes such as dsrAB and dnaJ , primer sets for mobile, accessory genes such as multidrug resistance efflux pumps (MDREP) have not been explored. Here, we describe an approach to create universal primer sets for select MDREP genes chosen from five superfamilies (SMR, MFS, MATE, ABC and RND) identified in a model community of six members (Acetobacterium woodii, Bacillus subtilis, Desulfovibrio vulgaris, Geoalkalibacter subterraneus, Pseudomonas putida and Thauera aromatica). Using sequence alignments and in silico PCR analyses, a new approach for creating universal primers sets targeting mobile, non-conserved genes has been developed and compared to more traditional approaches used for highly conserved genes. A discussion of the potential shortfalls of the primer sets designed this way are described. The approach described here can be adapted to any unique gene set and aid in creating a wider, more robust library of primer sets to detect less conserved genes and improve the field of PCR-based screening research. Importance Increasing use of molecular detection methods, specifically PCR and qPCR, requires utmost confidence in the results while minimizing false positives and negatives due to poor primer designs. Frequently, these detection methods are focused on conserved, core genes which limits their applications. These screening methods are being used in various industries for specific genetic targets or key organisms such as viral or infectious strains, or characteristic genes indicating the presence of key metabolic processes. The significance of this work is to improve primer design approaches to broaden the scope of detectable genes. The use of the techniques explored here will improve detection of non-conserved genes through unique primer design approaches. Additionally, the approaches here highlight additional, important information which can be gleaned during the in silico phase of primer design which will improve our gene annotations based on sequence homologies.
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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 | 0.052 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".