Relative and Quantitative Rhizosphere Microbiome Profiling Results in Distinct Abundance Patterns
Bibliographic record
Abstract
Next-generation sequencing is one of the most popular and cost-effective ways of characterizing microbiome in multiple samples. However, most of the currently available amplicon sequencing approaches are limited, as they result in relative abundance profiles of microbial taxa, which does not represent actual abundance in the environment. 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 changes in the relative abundance of major microbial phyla do not necessarily follow the same pattern as the estimated quantitative abundance. Most of the bacterial phyla linked with the rhizosphere of plants grown in soil with no history of water stress showed enrichment patterns in their estimated absolute abundance, which was in contradiction with the trends observed in the relative abundance data. However, in the case of the fungal groups (except for Basidiomycota ), such an enrichment pattern was not observed and the abundance of fungi remained relatively unchanged under different soil water stress history when estimated absolute abundance was considered. Comparing relative and estimated absolute abundances of dominant bacterial and fungal phyla, as well as their correlation with the functional processes in the rhizosphere, our results suggest that the estimated absolute abundance approach gives a different and more realistic perspective than the relative abundance approach. Such a quantification approach provides complementary information that helps to better understand the rhizosphere microbiomes and their associated ecological functional processes.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".