Snow alga <i>Sanguina aurantia</i> as revealed through de novo genome assembly and annotation
Bibliographic record
Abstract
To thrive on melting alpine and polar snow, some Chlorophytes produce an abundance of astaxanthin, causing red blooms, often dominated by genus Sanguina. The red cells have not been cultured, but we recently grew a green biciliate conspecific with Sanguina aurantia from a sample of watermelon snow. This culture provided source material for Oxford Nanopore Technology and Illumina sequencing. Our assembly pipeline exemplifies the value of a hybrid long- and short-read approach for the complexities of working with a culture grown from a field sample. Using bioinformatic tools, we separated assembled contigs into 2 genomic pools based on a difference in GC content (57.5 and 55.1%). We present the data as 2 assemblies of S. aurantia variants but explore other possibilities. High-throughput chromatin conformation capture analysis (Hi-C sequencing) was used to scaffold the assemblies into a 96-Mb genome designated as "A" and a 102-Mb genome designated as "B." Both assemblies are highly contiguous: genome A consists of 38 scaffolds with an N50 of 5.4 Mb, while genome B has 50 scaffolds with an N50 of 6.4 Mb. RNA sequencing was used to improve gene annotation.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".