Cost-Effective Discovery of Nucleotide Polymorphisms in Populations of an Allopolyploid Species Using Pool-Seq
Bibliographic record
Abstract
Population genetics studies of allopolyploid species lag behind those of diploid species because of practical difficulties in analysis of homeologs-duplicated gene copies originating from hybridized parental species. Pool-Seq, i.e. massive parallel sequencing of pooled individuals, has high potential for detecting nucleotide polymorphisms within and among multiple populations; however, its use has been limited to diploid species. We applied Pool-Seq to an allopolyploid species by developing a bioinformatic pipeline that assigns reads to each homeolog as well as to each polymorphic allele within each homeolog. We simultaneously sequenced eight genes from twenty individuals from each of 24 populations, and found over 100 polymorphic sites in each homeolog. For two sites, we estimated allele frequencies using the number of reads and then validated these estimations by making individual-based estimations. Pool-Seq using our bioinformatic pipeline allows efficient evaluation of nucleotide polymorphisms in a large number of individuals, even in allopolyploid species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".