Evaluating amplicon high-throughput sequencing data of microalgae living in melting snow: improvements and limitations
Bibliographic record
Abstract
Melting snow fields are an extremophilic habitat dominated by closely related Chlamydomonadaceae (Chlorophyta). Microscopy-based classification of these cryophilic microalgae is challenging and may not reveal the true diversity. High-throughput sequencing (HTS) allows for a more comprehensive evaluation of the community. However, HTS approaches have been rarely used in such ecosystems and the output of their application has not been evaluated. Furthermore, there is no consensus on the choice for a suitable DNA marker or data processing workflow. We found that the correct placement of taxonomic strings onto OTUs strongly depends on the quality of the reference databases. We improved the assignments of the HST data by generating additional reference sequences of the locally abundant taxa, guided by light microscopy. Furthermore, a manual inspection of all automated OTU assignments, oligotyping of the most abundant 18S OTUs, as well as ITS2 secondary structure analyses were necessary for accurate species assignments. Moreover, the sole use of one marker can cause misleading results, either because of insufficient variability within the locus (18S) or the scarcity of reference sequences (ITS2). Our evaluation reveals that HTS output needs to be thoroughly checked when the studied habitats or organisms are poorly represented in publicly available databases. We recommend an optimized workflow for an improved biodiversity evaluation of not only snow algal communities, but generally 'exotic' ecosystems where similar problems arise. A consistent sampling strategy, two- molecular marker approach, light microscopy-based guidance, generation of appropriate reference sequences and final manual verification of all taxonomic assignments are highly recommended.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".