A two‐tier bioinformatic pipeline to develop probes for target capture of nuclear loci with applications in Melastomataceae
Bibliographic record
Abstract
Premise Putatively single‐copy nuclear (SCN) loci, which are identified using genomic resources of closely related species, are ideal for phylogenomic inference. However, suitable genomic resources are not available for many clades, including Melastomataceae. We introduce a versatile approach to identify SCN loci for clades with few genomic resources and use it to develop probes for target enrichment in the distantly related Memecylon and Tibouchina (Melastomataceae). Methods We present a two‐tiered pipeline. First, we identified putatively SCN loci using MarkerMiner and transcriptomes from distantly related species in Melastomataceae. Published loci and genes of functional significance were then added (384 total loci). Second, using HybPiper, we retrieved 689 homologous template sequences for these loci using genome‐skimming data from within the focal clades. Results We sequenced 193 loci common to Memecylon and Tibouchina. Probes designed from 56 template sequences successfully targeted sequences in both clades. Probes designed from genome‐skimming data within a focal clade were more successful than probes designed from other sources. Discussion Our pipeline successfully identified and targeted SCN loci in Memecylon and Tibouchina, enabling phylogenomic studies in both clades and potentially across Melastomataceae. This pipeline could be easily applied to other clades with few genomic resources.
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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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".