Comparison of the performance of ITS1 and ITS2 as barcodes in amplicon-based sequencing of bioaerosols
Bibliographic record
Abstract
This paper presents the performance of two eukaryotic genomic ribosomal regions, ITS1 and ITS2, in describing fungal diversity in aerosol samples using amplicon-based High-Throughput Sequencing (HTS). Composting sites, biomethanization facilities, and dairy farms, all affected by the presence of fungi, were visited to collect air samples. The amplicon-based HTS approach is a target enrichment method that relies on the amplification of a specific target using particular primers before sequencing. Thus, the results are highly dependent on the quality of amplification. For this reason, the authors of this paper used a shotgun metagenomic approach to compare its outcome with the amplicon-based method. Indeed, shotgun metagenomic does not rely on any amplification prior to sequencing, because all genes are sequenced without a specific target. In addition, culture methods were added to the analyses in biomethanization and dairy farms samples to validate their contribution to fungal diversity of aerosols. The results obtained are unequivocal towards ITS1 outperformance to ITS2 in terms of richness, and taxonomic coverage. The differential abundance analysis did demonstrate that some taxa were exclusively detected only by ITS2, and vice-versa for ITS1. However, the shotgun metagenomic approach showed a taxonomic profile more resembling to ITS1 than ITS2. Based on these results, neither of the barcodes evaluated is perfect in terms of distinguishing all species. Using both barcodes offers a broader view of the fungal aerosol population. However, with the actual knowledge, the authors strongly recommend using ITS1 as a universal fungal barcode for quick general analyses of diversity and when limited financial resources are available, primarily due its ability to capture taxonomic profiles similar to those obtained using the shotgun metagenomic. The culture comparison with amplicon-based sequencing showed the complementarity of both approaches in describing the most abundant taxa.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".