Data from: The transcriptomics of sympatric dwarf and normal lake whitefish (Coregonus clupeaformis spp., Salmonidae) divergence as revealed by next-generation sequencing
Bibliographic record
Abstract
Gene expression divergence is one of the mechanisms thought to be involved in the emergence of incipient species. Next-generation sequencing has become an extremely valuable tool for the study of this process by allowing whole transcriptome sequencing, or RNA-Seq. We have conducted a 454 GS-FLX pyrosequencing experiment in order to refine our understanding of adaptive divergence between dwarf and normal lake whitefish species (Coregonus clupeaformis spp.). The objectives were to: (1) investigate transcriptomic divergence as measured by liver RNA-Seq; (2) test the correlation between divergence in expression and sequence polymorphism and (3) investigate the extent of allelic imbalance. We also compared the results of RNA-seq with those of a previous microarray study performed on the same fish. Following de novo assembly, results showed that normal whitefish over-expressed more contigs associated with protein synthesis while dwarf fish over-expressed more contigs related to energy metabolism, immunity and DNA replication and repair. Moreover, 63 SNPs showed significant allelic imbalance, and this phenomenon prevailed in the recently diverged dwarf whitefish. Results also showed an absence of correlation between gene expression divergence as measured by RNA-Seq and either polymorphism rate or sequence divergence between normal and dwarf whitefish. This study reiterates an important role for gene expression divergence, and provides evidence for allele-specific expression divergence as well as evolutionary decoupling of regulatory and coding sequences in the adaptive divergence of normal and dwarf whitefish. It also demonstrates how next-generation sequencing can lead to a more comprehensive understanding of transcriptomic divergence in a young species pair.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".