Mining transcriptome sequences towards identifying adaptive single nucleotide polymorphisms in lake whitefish species pairs (<i>Coregonus</i> spp. Salmonidae)
Bibliographic record
Abstract
Next-generation sequencing allows the discovery of large numbers of single nucleotide polymorphisms (SNPs) in species where little genomic information was previously available. Here, we assembled, de novo, over 130 mb of non-normalized cDNA using 454 pyrosequencing data from dwarf and normal lake whitefish and backcross hybrids. Our main goals were to gather a large data set of SNP markers, document their distribution within coding regions, evaluate the effect of species divergence on allele frequencies and combine results with previous genomic studies to identify candidate genes underlying the adaptive divergence of lake whitefish. We identified 6094 putative SNPs in 2674 contigs (mean size: 576 bp, range: 101-6116) and 1540 synonymous and 1734 non-synonymous mutations for a genome-wide non-synonymous to synonymous substitution rate ratio (p(N)/p(S)) of 0.37. As expected based on the young age (<15 000 years) of whitefish species pair, the overall level of divergence between them was relatively weak. Yet, 89 SNPs showed pronounced allele frequency differences between sympatric normal and dwarf whitefish. Among these, SNPs in genes annotated to energy metabolic functions were the most abundant and this, in addition to previous experimental data at the gene expression and phenotypic level, brings compelling evidence that genes involved in energy metabolism are prime candidates explaining the adaptive divergence of lake whitefish species pairs. Finally, we unexpectedly identified 44 contigs annotated to transposable elements and these were predominantly composed of backcross hybrids sequences. This indicates an elevated activity of transposable elements, which could potentially contribute to the reduced fitness of hybrids previously documented.
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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) | 0.000 | 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 teacher head, 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".