Whole-Genome Sequencing Highlights Conservative Genomic Strategies of a Stress-Tolerant, Long-Lived Scleractinian Coral, <i>Porites australiensis</i> Vaughan, 1918
Bibliographic record
Abstract
Massive corals of the genus Porites, common, keystone reef builders in the Indo-Pacific Ocean, are distinguished by their relative stress tolerance and longevity. In order to identify genetic bases of these attributes, we sequenced the complete genome of a massive coral, Porites australiensis. We developed a genome assembly and gene models of comparable quality to those of other coral genomes. Proteome analysis identified 60 Porites skeletal matrix protein genes, all of which show significant similarities to genes from other corals and even to those from a sea anemone, which has no skeleton. Nonetheless, 30% of its skeletal matrix proteins were unique to Porites and were not present in the skeletons of other corals. Comparative genomic analyses showed that genes widely conserved among other organisms are selectively expanded in Porites. Specifically, comparisons of transcriptomic responses of P. australiensis and Acropora digitifera, a stress-sensitive coral, reveal significant differences in regard to genes that respond to increased water temperature, and some of the genes expanded exclusively in Porites may account for the different thermal tolerances of these corals. Taken together, widely shared genes may have given rise to unique biological characteristics of Porites, massive skeletons and stress tolerance.
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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 | 0.000 | 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 | 0.000 |
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 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".