Evaluation of denaturing gradient gel electrophoresis in the detection of 16S rDNA sequence variation in rhizobia and methanotrophs
Bibliographic record
Abstract
The ability of denaturing gradient gel electrophoresis (DGGE) technique to resolve 16S rDNA products generated from two different collections of bacteria using universal 16S primers was investigated. Alignments of 16S rDNA sequences of known species of rhizobia and methanotrophs were performed in order to determine the genetic variations within a 200 bp product obtained with PCR primers which amplify the 16S rRNA encoding genes from Eubacteria. Theoretical DNA melting curves were obtained with the Melt87 program and found to correlate with the ability to resolve fragments by DGGE. In the case of the rhizobia, the inability of DGGE analysis to resolve the PCR products from closely related species was in accordance with the low polymorphism observed amongst the sequences in the amplified area. In the case of the methanotrophs, the PCR products were surprisingly difficult to resolve given the high degree of sequence polymorphism of the amplified area in some distantly related species. The difference in sequence divergence within the two groups members allowed therefore to scale the resolution ability of the DGGE technique.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".