Metabarcoding for plant pathologists
Bibliographic record
Abstract
Metabarcoding is a method that uses high-throughput sequencing to characterize microbial communities. This approach involves PCR-based amplification of a single locus from environmental DNA of communities of organisms, sequencing the resulting amplicons, and comparing those sequences to reference databases to infer the taxonomic identities of organisms present. By comparing the identity and number of associated sequences between samples, the data produced by metabarcoding can be used to reveal differences among sample types and identify biological variables that correlate with differences. However, metabarcoding is affected by a myriad of technical factors including sampling technique, DNA extraction methods, as well as choice of primers, locus for PCR amplification, and data analysis procedures. The resulting outputs will be both complex and semi-quantitative. The methods used can affect reliability of results including false positives and negatives, especially if the goal is to confidently survey the presence of plant pathogens. This article reviews the status of metabarcoding as it pertains to plant pathology and provides guidance on experimental design and analyses of bacterial, fungal, and oomycete datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.030 |
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".