Phylogenomic inferences from reference-mapped and de novo assembled short-read sequence data using RADseq sequencing of California white oaks (<i>Quercus</i> section <i>Quercus</i>)
Bibliographic record
Abstract
The emergence of next generation sequencing has increased by several orders of magnitude the amount of data available for phylogenetics. Reduced representation approaches, such as restriction-sited associated DNA sequencing (RADseq), have proven useful for phylogenetic studies of non-model species at a wide range of phylogenetic depths. However, analysis of these datasets is not uniform and we know little about the potential benefits and drawbacks of de novo assembly versus assembly by mapping to a reference genome. Using RADseq data for 83 oak samples representing 16 taxa, we identified variants via three pipelines: mapping sequence reads to a recently published draft genome of Quercus lobata, and de novo assembly under two sets of locus filters. For each pipeline, we inferred the maximum likelihood phylogeny. All pipelines produced similar trees, with minor shifts in relationships within well-supported clades, despite the fact that they yielded different numbers of loci (68 000 - 111 000 loci) and different degrees of overlap with the reference genome. We conclude that both the reference-aligned and de novo assembly pipelines yield reliable results, and that advantages and disadvantages of these approaches pertain mainly to downstream uses of RADseq data, not to phylogenetic inference per se.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".