Barcoding lichen-forming fungi using 454 pyrosequencing is challenged by artifactual and biological sequence variation
Bibliographic record
Abstract
Although lichens (lichen-forming fungi) play an important role in the ecological integrity of many vulnerable landscapes, only a minority of lichen-forming fungi have been barcoded out of the currently accepted ∼18 000 species. Regular Sanger sequencing can be problematic when analyzing lichens since saprophytic, endophytic, and parasitic fungi live intimately admixed, resulting in low-quality sequencing reads. Here, high-throughput, long-read 454 pyrosequencing in a GS FLX+ System was tested to barcode the fungal partner of 100 epiphytic lichen species from Switzerland using fungal-specific primers when amplifying the full internal transcribed spacer region (ITS). The present study shows the potential of DNA barcoding using pyrosequencing, in that the expected lichen fungus was successfully sequenced for all samples except one. Alignment solutions such as BLAST were found to be largely adequate for the generated long reads. In addition, the NCBI nucleotide database-currently the most complete database for lichen-forming fungi-can be used as a reference database when identifying common species, since the majority of analyzed lichens were identified correctly to the species or at least to the genus level. However, several issues were encountered, including a high sequencing error rate, multiple ITS versions in a genome (incomplete concerted evolution), and in some samples the presence of mixed lichen-forming fungi (possible lichen chimeras).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".