Commercial DNA extraction kits impact observed microbial community composition in permafrost samples
Bibliographic record
Abstract
The total community genomic DNA (gDNA) from permafrost was extracted using four commercial DNA extraction kits. The gDNAs were compared using quantitative real-time PCR (qPCR) targeting 16S rRNA genes and bacterial diversity analyses obtained via 454 pyrosequencing of the 16S rRNA (V3 region) amplified in single or nested PCR. The FastDNA(®) SPIN (FDS) Kit provided the highest gDNA yields and 16S rRNA gene concentrations, followed by MoBio PowerSoil(®) (PS) and MoBio PowerLyzer™ (PL) kits. The lowest gDNA yields and 16S rRNA gene concentrations were from the Meta-G-Nome™ (MGN) DNA Isolation Kit. Bacterial phyla identified in all DNA extracts were similar to that found in other soils and were dominated by Actinobacteria, Firmicutes, Gemmatimonadetes, Proteobacteria, and Acidobacteria. Weighted UniFrac and statistical analyses indicated that bacterial community compositions derived from FDS, PS, and PL extracts were similar to each other. However, the bacterial community structure from the MGN extracts differed from other kits exhibiting higher proportions of easily lysed β- and γ-Proteobacteria and lower proportions of Actinobacteria and Methylocystaceae important in carbon cycling. These results indicate that gDNA yields differ between the extraction kits, but reproducible bacterial community structure analysis may be accomplished using gDNAs from the three bead-beating lysis extraction kits.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".