In Silico Study Suggesting the Bias of Primers Choice in the Molecular Identification of Fungal Aerosols
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Bibliographic record
Abstract
This paper presents an in silico analysis to assess the current state of the fungal UNITE database in terms of the two eukaryote nuclear ribosomal regions, Internal Transcribed Spacers 1 and 2 (ITS1 and ITS2), used in describing fungal diversity. Microbial diversity is often evaluated with amplicon-based high-throughput sequencing approaches, which 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 the primers used for amplification. The goal of this study is to validate if the mismatches of the primers on the binding sites of the targeted taxa could explain the differences observed when using either ITS1 or ITS2 in describing airborne fungal diversity. Hence, the choice of the pairs of primers for each barcode concur with a study comparing the performance of ITS1 and ITS2 in three occupational environments. The sequence length varied between the amplicons retrieved from the UNITE database using the pair of primers targeting ITS1 and ITS2. However, the database contains an equal number of unidentified taxa from ITS1 and ITS2 regions in the six taxonomic levels employed (phylum, class, order, family, genus, species). The chosen ITS primers showed differences in their ability to amplify fungal sequences from the UNITE database. Eleven taxa consisting of Trichocomaceae, Dothioraceae, Botryosphaeriaceae, Mucorales, Saccharomycetes, Pucciniomycetes, Ophiocordyceps, Microsporidia, Archaeorhizomycetes, Mycenaceae, and Tulasnellaceae showed large variations between the two regions. Note that members of the latter taxa are not all typical fungi found in the air. As no universal method is currently available to cover all the fungal kingdom, continuous work in designing primers, and particularly combining multiple primers targeting the ITS region is the best way to compensate for the biases of each one to get a larger view of the fungal diversity.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it