Transcriptome-wide comparison of sequence variation in divergent ecotypes of kokanee salmon
Bibliographic record
Abstract
BACKGROUND: High throughput next-generation sequencing technology has enabled the collection of genome-wide sequence data and revolutionized single nucleotide polymorphism (SNP) discovery in a broad range of species. When analyzed within a population genomics framework, SNP-based genotypic data may be used to investigate questions of evolutionary, ecological, and conservation significance in natural populations of non-model organisms. Kokanee salmon are recently diverged freshwater populations of sockeye salmon (Oncorhynchus nerka) that exhibit reproductive ecotypes (stream-spawning and shore-spawning) in lakes throughout western North America and northeast Asia. Current conservation and management strategies may treat these ecotypes as discrete stocks, however their recent divergence and low levels of gene flow make in-season genetic stock identification a challenge. The development of genome-wide SNP markers is an essential step towards fine-scale stock identification, and may enable a direct investigation of the genetic basis of ecotype divergence. RESULTS: We used pooled cDNA samples from both ecotypes of kokanee to generate 750 million base pairs of transcriptome sequence data. These raw data were assembled into 11,074 high coverage contigs from which we identified 32,699 novel single nucleotide polymorphisms. A subset of these putative SNPs was validated using high-resolution melt analysis and Sanger resequencing to genotype independent samples of kokanee and anadromous sockeye salmon. We also identified a number of contigs that were composed entirely of reads from a single ecotype, which may indicate regions of differential gene expression between the two reproductive ecotypes. In addition, we found some evidence for greater pathogen load among the kokanee sampled in stream-spawning habitats, suggesting a possible evolutionary advantage to shore-spawning that warrants further study. CONCLUSIONS: This study provides novel genomic resources to support population genetic and genomic studies of both kokanee and anadromous sockeye salmon, and has the potential to produce markers capable of fine-scale stock assessment. While this RNAseq approach was successful at identifying a large number of new SNP loci, we found that the frequency of alleles present in the pooled transcriptome data was not an accurate predictor of population allele frequencies.
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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".