The Greenland shark genome: insights into deep-sea ecology and lifespan extremes
Bibliographic record
Abstract
The Greenland shark ( Somniosus microcephalus ) is known for its slow metabolism and deep-sea habitat. It is considered the longest-lived vertebrate on Earth, with an estimated lifespan of 392±120 years. Despite its remarkable longevity and lifestyle, there have been no genomic studies on this species. Here, we report the first, chromosome-level assembly of the Greenland shark genome, which is 5.9 Gb in size with an N50 length of 233 Mb, and contains 37,125 predicted genes with a completeness score of 86.5%. We found that the copy numbers of three gene families ( TNF , TLR , LRRFIP ), which are involved in activating the NF-κB signaling pathway, are significantly increased in the Greenland shark compared to short-lived shark species. In the rhodopsin of this deep-sea dweller, we detected amino acid substitutions that result in spectral tuning for the so-called 'blue shift', suggesting adaptive evolution to dim-light vision. We also elucidate the dynamics of the effective population size ( N e ) of the Greenland shark and its close relative, the Pacific sleeper shark ( Somniosus pacificus ). These genomic analyses offer new insights into the molecular basis of the exceptional longevity of the Greenland shark and highlight potential genetic mechanisms that could inform future research into longevity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".