Next‐generation polyploid phylogenetics: rapid resolution of hybrid polyploid complexes using PacBio single‐molecule sequencing
Bibliographic record
Abstract
Difficulties in generating nuclear data for polyploids have impeded phylogenetic study of these groups. We describe a high-throughput protocol and an associated bioinformatics pipeline (Pipeline for Untangling Reticulate Complexes (Purc)) that is able to generate these data quickly and conveniently, and demonstrate its efficacy on accessions from the fern family Cystopteridaceae. We conclude with a demonstration of the downstream utility of these data by inferring a multi-labeled species tree for a subset of our accessions. We amplified four c. 1-kb-long nuclear loci and sequenced them in a parallel-tagged amplicon sequencing approach using the PacBio platform. Purc infers the final sequences from the raw reads via an iterative approach that corrects PCR and sequencing errors and removes PCR-mediated recombinant sequences (chimeras). We generated data for all gene copies (homeologs, paralogs, and segregating alleles) present in each of three sets of 50 mostly polyploid accessions, for four loci, in three PacBio runs (one run per set). From the raw sequencing reads, Purc was able to accurately infer the underlying sequences. This approach makes it easy and economical to study the phylogenetics of polyploids, and, in conjunction with recent analytical advances, facilitates investigation of broad patterns of polyploid evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".