Relative and quantitative rhizosphere microbiome profiling result in distinct abundance patterns
Bibliographic record
Abstract
Abstract Next-generation sequencing is recognized as one of the most popular and cost-effective way of characterizing microbiome in multiple samples. However, most of the currently available amplicon sequencing approaches are inherently limited, as they are often presented based on the relative abundance of microbial taxa, which may not fully represent actual microbiome profiles. Here, we combined amplicon sequencing (16S rRNA gene for bacteria and ITS region for fungi) with real-time quantitative PCR (qPCR) to characterize the rhizosphere microbiome of wheat. We show that the increase in relative abundance of major microbial phyla does not necessarily result in an increase in abundance. One striking observation when comparing relative and quantitative abundances was a substantial increase in the abundance of almost all phyla associated with the rhizosphere of plants grown in soil with no history of water stress as compared with the rhizosphere of plants growing in soil with a history of water stress, which was in contradiction with the trends observed in the relative abundance data. Our results suggest that the estimated absolute abundance approach gives a different perspective than the relative abundance approach, providing complementary information that helps to better understand the rhizosphere microbiome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".