Don't throw the baby out with the bathwater: identifying and mapping paralogs in salmonids
Bibliographic record
Abstract
Many eukaryotic genomes contain a large fraction of gene duplicates (or paralogs) as a result of ancient or recent whole-genome duplications (Ohno 1970; Jaillon et al. 2004; Kellis et al. 2004). Identifying paralogs with NGS data is a pervasive problem in both ancient polyploids and neopolyploids. Likewise, paralogs are often treated as a nuisance that has to be detected and removed (Everett et al. 2012). In this issue of Molecular Ecology Resources, Waples et al. (2015) show that exclusion might not be necessary and how we may miss out on important genomic information in doing so. They present a novel statistical approach to detect paralogs based on the segregation of RAD loci in haploid offspring and test their method by constructing linkage maps with and without these duplicated loci in chum salmon, Oncorhynchus keta (Fig.1). Their linkage map including the resolved paralogs shows that these are mostly located in the distal regions of several linkage groups. Particularly intriguing is their finding that these homoeologous regions appear impoverished in transposable elements (TE). Given the role that TE play in genome remodelling, it is noteworthy that these elements are of low abundance in regions showing residual tetrasomic inheritance. This raises the question whether re-diploidization is constrained in these regions and whether they might have a role to play in salmonid speciation. This study provides an original approach to identifying duplicated loci in species with a pedigree, as well as providing a dense linkage map for chum salmon, and interesting insights into the retention of gene duplicates in an ancient polyploid.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".