Data from: Reconstructing phylogeny from reduced-representation genome sequencing data without assembly or alignment
Bibliographic record
Abstract
Reduced-representation genome sequencing such as RADseq aids the analysis of genomes by reducing the quantity of data, thereby lowering both sequencing costs and computational burdens. RADseq was initially designed for studying genetic variation across genomes at the population level, but has also proved to be suitable for interspecific phylogeny reconstruction. RADseq data pose challenges for standard phylogenomic methods, however, due to incomplete coverage of the genome and large amounts of missing data. Alignment-free methods are both efficient and accurate for phylogenetic reconstructions with whole genomes and are especially practical for non-model organisms; nonetheless, alignment-free methods have not been applied with reduced genome sequencing data. Here, we test a full-genome assembly and alignment-free method, AAF, in application to RADseq data and propose two procedures for reads selection to remove reads from restriction sites that were not found in taxa being compared. We validate these methods using both simulations and real datasets. Reads selection improved the accuracy of phylogenetic construction in every simulated scenario and the two real datasets, making AAF as good or better than a comparable alignment-based method, even though AAF had much lower computational burdens. We also investigated the sources of missing data in RADseq and their effects on phylogeny reconstruction using AAF. The AAF pipeline modified for RADseq or other reduced-representation sequencing data, phyloRAD, is available on github (https://github.com/fanhuan/phyloRAD).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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".