Finding genes and lineages under selection in speciation
Bibliographic record
Abstract
What are the genes and traits that respond to selection and cause prezygotic reproductive isolation between species? This question has been hard to answer because genomes are large, the targets of selection may be scattered across the genome (Sabeti et al., ) and different genes may respond to the same selective pressure in different populations (Scheinfeldt et al., ). In this issue of Molecular Ecology, Weber et al. () use a clever comparative approach and leading-edge transcriptomic methods to identify the species and genes under positive selection for divergence between brittle stars (the echinoderm class Ophiuroidea) in the Ophioderma longicauda species complex. They found convincing evidence of positive or diversifying selection acting on two genes encoding ion channels that form part of the signal transduction cascade within the sperm in response to pheromones. Evidence for selection was concentrated in genes from one species (called C5, with internal fertilization and female parental care of brooded juveniles and not in the other species (called C3, with more conventional broadcast spawning and planktonic development of embryos and larvae). That analysis greatly extends the range of taxa, life history traits and molecules that are associated with positive selection in speciation. It also illustrates some of the current limitations on the application of RNAseq methods in the search for the targets of selection in nonmodel organisms like brittle stars. From both points of view, the new work by Weber et al. () has important implications for our understanding of speciation in the ocean.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".