Computation vs. cloning: evaluation of two methods for haplotype determination
Bibliographic record
Abstract
Nuclear sequence data, often from multiple loci, are increasingly being employed in analyses of population structure and history, yet there has been relatively little evaluation of methods for accurately and efficiently separating the alleles or haplotypes in heterozygous individuals. We compared the performance of a computational method of haplotype reconstruction and standard cloning methods using a highly variable intron (ornithine decarboxylase, intron 6) in three closely related species of dabbling ducks (genus Anas). Cloned sequences from 32 individuals were compared to results obtained from phase 2.1.1 . phase correctly identified haplotypes in 28 of 30 heterozygous individuals when the underlying model assumed no recombination. Haplotypes of the remaining two individuals were also inferred correctly except for unique polymorphisms, the phase of which was appropriately indicated as uncertain (phase probability = 0.5). For a larger set of 232 individuals, results were essentially identical regardless of the recombination model used and haplotypes for all 30 of the tested heterozygotes were correctly inferred, with the exception of uncertain phase for unique polymorphisms in one individual. In contrast, initial sequences of one clone per sample yielded accurate haplotype determination in only 26 of 30 individuals; polymerase chain reaction (PCR)/cloning errors resulting from misincorporation of individual nucleotides could be recognized and avoided by comparison to direct sequences, but errors due to PCR recombination resulted in incorrect haplotype reconstruction in four individuals. The accuracy of haplotypes reconstructed by phase, even when dealing with a relatively small number of samples and numerous variable sites, suggests broad utility of computational approaches for reducing the cost and improving the efficiency of data collection from nuclear sequence loci.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".