Rapid quantitative assessment of temporal and spatial variation in key functional genes of the microbial nitrogen cycle across multiple marine environments using the <scp>NanoString nCounter</scp>
Bibliographic record
Abstract
Abstract The marine nitrogen cycle controls oceanic productivity through enzymatic processes mediated by microbes. Here, we report the construction, evaluation, and application of the OceansN CodeSet for the NanoString nCounter, which quantifies a suite of protein‐coding genes that are central to microbially mediated nitrogen cycle processes in the ocean. We also placed emphasis on quantifying a diverse set of marine diazotrophs within known nifH phylogenetic clades. The OceansN CodeSet provided direct hybridization‐based quantitation of 48 probes in a single sample, presenting advantages in terms of reduced sample handling, elimination of amplification bias, minimal DNA sample requirements, and the ability to assess targets ranging from relatively rare to abundant, with a reliable quantitation limit of ~ 1000 gene copies per target per sample. As such, our approach fills a unique methodological niche between the scale of high‐throughput amplicon sequencing (a compositional method) and quantitative polymerase chain reaction (qPCR) (a targeted method with generally lower throughput). When applied to North Atlantic environmental DNA samples, the OceansN CodeSet revealed temporal and spatial patterns in nitrogen assimilation, nitrification, and denitrification, as well as the abundance and distribution of various nitrogen‐fixing microorganisms (diazotrophs). Data from the nCounter was validated via internal and external controls, and by comparison to qPCR, nifH amplicon sequencing, and shotgun metagenomic sequencing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".