A target enrichment probe set for resolving the flagellate land plant tree of life
Bibliographic record
Abstract
PREMISE: New sequencing technologies facilitate the generation of large-scale molecular data sets for constructing the plant tree of life. We describe a new probe set for target enrichment sequencing to generate nuclear sequence data to build phylogenetic trees with any flagellate land plants, including hornworts, liverworts, mosses, lycophytes, ferns, and all gymnosperms. METHODS: We leveraged existing transcriptome and genome sequence data to design the GoFlag 451 probes, a set of 56,989 probes for target enrichment sequencing of 451 exons that are found in 248 single-copy or low-copy nuclear genes across flagellate plant lineages. RESULTS: Our results indicate that target enrichment using the GoFlag451 probe set can provide large nuclear data sets that can be used to resolve relationships among both distantly and closely related taxa across the flagellate land plants. We also describe the GoFlag 408 probes, an optimized probe set covering 408 of the 451 exons from the GoFlag 451 probe set that is commercialized by RAPiD Genomics. CONCLUSIONS: A target enrichment approach using the new probe set provides a relatively low-cost solution to obtain large-scale nuclear sequence data for inferring phylogenetic relationships across flagellate land plants.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".