spyder, a new method for in silico design and assessment of 16S rRNA gene primers for molecular microbial ecology
Bibliographic record
Abstract
Molecular microbial ecology studies are heavily reliant on 'Universal' 16S rRNA gene primers for elucidating microbial community structure and composition, and yet primer design and optimization is often overlooked. Primers that exhibit minor biases due to primer-template mismatches can substantially alter the pool of amplicons from a community DNA sample, resulting in inaccurate conclusions. As a result, it is important that primers are critically evaluated against the most comprehensive data sets available before commencing molecular microbial community studies. We present a user-friendly, multi-platform (e.g. Windows, Linux, Mac) method named spyder for the in silico design and assessment of 16S rRNA gene primers. The method utilizes the Ribosomal Database Project's Probe Match feature coupled with a compact program (available at http://people.uleth.ca/~selibl/Spyder/Spyder.html) that aligns and identifies mismatches between primers and templates. To demonstrate the value of spyder, we assessed commonly used 'Universal' and phyla-specific primers and identified primer modifications that improved the coverage of target organisms by 5-42% as well as removed excessive degeneracies.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".