Targeted sequencing for high-resolution evolutionary analyses following genome duplication in salmonid fish: Proof of concept for key components of the insulin-like growth factor axis
Bibliographic record
Abstract
High-throughput sequencing has revolutionised comparative and evolutionary genome biology. It has now become relatively commonplace to generate multiple genomes and/or transcriptomes to characterize the evolution of large taxonomic groups of interest. Nevertheless, such efforts may be unsuited to some research questions or remain beyond the scope of some research groups. Here we show that targeted high-throughput sequencing offers a viable alternative to study genome evolution across a vertebrate family of great scientific interest. Specifically, we exploited sequence capture and Illumina sequencing to characterize the evolution of key components from the insulin-like growth (IGF) signalling axis of salmonid fish at unprecedented phylogenetic resolution. The IGF axis represents a central governor of vertebrate growth and its core components were expanded by whole genome duplication in the salmonid ancestor ~95Ma. Using RNA baits synthesised to genes encoding the complete family of IGF binding proteins (IGFBP) and an IGF hormone (IGF2), we captured, sequenced and assembled orthologous and paralogous exons from species representing all ten salmonid genera. This approach generated 299 novel sequences, most as complete or near-complete protein-coding sequences. Phylogenetic analyses confirmed congruent evolutionary histories for all nineteen recognized salmonid IGFBP family members and identified novel salmonid-specific IGF2 paralogues. Moreover, we reconstructed the evolution of duplicated IGF axis paralogues across a replete salmonid phylogeny, revealing complex historic selection regimes - both ancestral to salmonids and lineage-restricted - that frequently involved asymmetric paralogue divergence under positive and/or relaxed purifying selection. Our findings add to an emerging literature highlighting diverse applications for targeted sequencing in comparative-evolutionary genomics. We also set out a viable approach to obtain large sets of nuclear genes for any member of the salmonid family, which should enable insights into the evolutionary role of whole genome duplication before additional nuclear genome sequences become available.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".