Serial analysis of V6 ribosomal sequence tags (SARST‐V6): a method for efficient, high‐throughput analysis of microbial community composition
Bibliographic record
Abstract
Serial analysis of ribosomal sequence tags (SARST) is a novel technique for characterizing microbial community composition. The SARST method captures sequence information from concatemers of short 16S rDNA polymerase chain reaction (PCR) amplicons from complex populations of DNA. Here, we describe a similar method, serial analysis of V6 ribosomal sequence tags (SARST-V6), which targets the V6 hypervariable region of bacterial 16S rRNA genes. The SARST-V6 technique exploits internal primer sequences to generate compatible restriction digest overhangs, thereby improving upon the efficiency of SARST. Serial analysis of V6 ribosomal sequence tags of bacterial community composition in hydrothermal marine sediments from Guaymas Basin resembled results of cloning and sequencing of single, full-length PCR products from ribosomal RNA genes of the same microbial community. Both methods identified the same major bacterial groups, but only SARST-V6 recovered thermodesulfobacteria and gamma-proteobacteria sequences, while only full-length PCR product cloning recovered candidate division OP11 se-quences. There were differences in the relative frequencies of some phylotypes. The disparities reflect differences in the amplicon pool obtained during initial amplification that may result from different primer affinities or DNA degradation. These results demonstrate the utility of SARST-V6 in collecting taxonomically informative data for high-throughput analysis of microbial communities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".